Welcome to Survival Analysis in R for Public Health!

The three earlier courses in this series covered statistical thinking, correlation, linear regression and logistic regression. This one will show you how to run survival – or “time to event” – analysis, explaining what’s meant by familiar-sounding but deceptive terms like hazard and censoring, which have specific meanings in this context. Using the popular and completely free software R, you’ll learn how to take a data set from scratch, import it into R, run essential descriptive analyses to get to know the data’s features and quirks, and progress from Kaplan-Meier plots through to multiple Cox regression. You’ll use data simulated from real, messy patient-level data for patients admitted to hospital with heart failure and learn how to explore which factors predict their subsequent mortality. You’ll learn how to test model assumptions and fit to the data and some simple tricks to get round common problems that real public health data have. There will be mini-quizzes on the videos and the R exercises with feedback along the way to check your understanding.

Prerequisites

Some formulae are given to aid understanding, but this is not one of those courses where you need a mathematics degree to follow it. You will need basic numeracy (for example, we will not use calculus) and familiarity with graphical and tabular ways of presenting results. The three previous courses in the series explained concepts such as hypothesis testing, p values, confidence intervals, correlation and regression and showed how to install R and run basic commands. In this course, we will recap all these core ideas in brief, but if you are unfamiliar with them, then you may prefer to take the first course in particular, Statistical Thinking in Public Health, and perhaps also the second, on linear regression, before embarking on this one.


Immunology: Innate Immune System

Our immune system relies on an innate and an adaptive arm that communicate and collaborate to provide us with an optimal response against pathogens. This course focuses on our innate immunity which provides us our first, fast and inherited defence against infections.

In this course, you will take a closer look at the mechanisms and cellular components involved in this swift response that occurs within minutes of exposure to a threat. Throughout the course, and guided by our active researchers, you will have opportunities to recognise its key protective mechanisms and to explain their importance for our overall health. You will learn about the mechanisms it uses to inform our adaptive immune system of the presence of a threat, and understand how some environmental factors, such as our own internal microbiome, influences it. Finally, you will have opportunities to reflect on current related issues and controversies in this fascinating field of research.

Sophie Rutschmann
Malgorzata (Maggie) Trela


This course specifically explores approaches and tools  and how to apply them in public health settings. Students will learn how to critically analyse the power dynamics present between multidisciplinary stakeholders and appreciate the need for reciprocity between those delivering and those receiving health care; between both those conducting and those participating in research. They will also learn how to select and evaluate different participatory approaches to apply these to public health programmes and/or research. Tools with which to do this include undertaking a stakeholder-mapping exercise and needs assessment, including a critical and reasoned narrative to justify the approach.

While this course, as with the rest of the specialisation, focuses on public health and ways of involving citizens and patients in programmes and research, these concepts apply to other disciplines too. So, you don't have to be a public health specialist or work in healthcare to gain insight from this course.

If you would like to learn more about the theories and core principles of participation within a public health context, we suggest taking Introduction to Participatory Approaches in Public Health. If you're planning a research project and want to learn more about participation in resaerchs, explore our course Public Involvement in Research.

Approximately 3 hours of study per week over 5 weeks.

Applying Participatory Approaches in Public Health Settings


This course covers various themes around design, regulatory approaches, ethics, technology adoption, implementation and strategy as applied to digital health. These session cover areas to include data regulations, examples of data breaches in digital health, the challenges and opportunities of technology adoption and implementation with a focus on the non-adoption, abandonment, scale-up, spread and sustainability framework (NASSS Framework). The strategy part of this course focuses on understanding a simple strategy for digital health through PESTLE and SWOT analysis, and examples of their application in digital health.

Design and Implementation of Digital Health Interventions


Large language models (LLMs) are trained on human-generated text, but additional methods are needed to align an LLM with human values and preferences.

Reinforcement Learning from Human Feedback (RLHF) is currently the main method for aligning LLMs with human values and preferences. RLHF is also used for further tuning a base LLM to align with values and preferences that are specific to your use case.

In this course, you will gain a conceptual understanding of the RLHF training process, and then practice applying RLHF to tune an LLM. You will:

1. Explore the two datasets that are used in RLHF training: the “preference” and “prompt” datasets.

2. Use the open source Google Cloud Pipeline Components Library, to fine-tune the Llama 2 model with RLHF.

3. Assess the tuned LLM against the original base model by comparing loss curves and using the “Side-by-Side (SxS)” method.


Building Multimodal Search and RAG

Learn how to build multimodal search and RAG systems. RAG systems enhance an LLM by incorporating proprietary data into the prompt context. Typically, RAG applications use text documents, but, what if the desired context includes multimedia like images, audio, and video? This course covers the technical aspects of implementing RAG with multimodal data to accomplish this.

1. Learn how multimodal models are trained through contrastive learning and implement it on a real dataset.

2. Build any-to-any multimodal search to retrieve relevant context across different data types.

3. Learn how LLMs are trained to understand multimodal data through visual instruction tuning and use them on multiple image reasoning examples.

4. Implement an end-to-end multimodal RAG system that analyzes retrieved multimodal context to generate insightful answers.

5. Explore industry applications like visually analyzing invoices and flowcharts to output structured data.

6. Create a multi-vector recommender system that suggests relevant items by comparing their similarities across multiple modalities.

As AI systems increasingly need to process and reason over multiple data modalities, learning how to build such systems is an important skill for AI developers.

This course equips you with the key skills to embed, retrieve, and generate across different modalities. By gaining a strong foundation in multimodal AI, you’ll be prepared to build smarter search, RAG, and recommender systems.


Knowledge Graphs for RAG

Knowledge graphs are used in development to structure complex data relationships, drive intelligent search functionality, and build powerful AI applications that can reason over different data types. Knowledge graphs can connect data from both structured and unstructured sources (databases, documents, etc.), providing an intuitive and flexible way to model complex, real-world scenarios.

Unlike tables or simple lists, knowledge graphs can capture the meaning and context behind the data, allowing you to uncover insights and connections that would be difficult to find with conventional databases. This rich, structured context is ideal for improving the output of large language models (LLMs), because you can build more relevant context for the model than with semantic search alone.

This course will teach you how to leverage knowledge graphs within retrieval augmented generation (RAG) applications. You’ll learn to:

1. Understand the basics of how knowledge graphs store data by using nodes to represent entities and edges to represent relationships between nodes.

2. Use Neo4j’s query language, Cypher, to retrieve information from a fun graph of movie and actor data.

3. Add a vector index to a knowledge graph to represent unstructured text data and find relevant texts using vector similarity search.

4. Build a knowledge graph of text documents from scratch, using publicly available financial and investment documents as the demo use case

5. Explore advanced techniques for connecting multiple knowledge graphs and using complex queries for comprehensive data retrieval.

6. Write advanced Cypher queries to retrieve relevant information from the graph and format it for inclusion in your prompt to an LLM.

After course completion, you’ll be well-equipped to use knowledge graphs to uncover deeper insights in your data, and enhance the performance of LLMs with structured, relevant context.


If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the DeepLearning.AI TensorFlow Developer Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new DeepLearning.AI TensorFlow Developer Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning


Building and Evaluating Data Agents

Learn how to build and evaluate a data agent in “Building and Evaluating Data Agents,” a course created in collaboration with Snowflake, and taught by Anupam Datta, AI Research Lead, and Josha Reini, Developer Advocate at Snowflake.

You’ll design a data agent that connects to data sources (databases, files) and performs web searches to respond to users’ queries. The agent will consist of sub-agents, each specialized in connecting to a particular data source, and other sub-agents that summarize or visualize the results. To answer a particular query, the agent will use a planner that identifies which sub-agents to call and in what order.

You’ll add observability to the agent’s workflow and evaluate the quality of its output. Using an LLM-as-a-judge approach, you’ll assess whether the final answer is relevant to the user’s query and grounded in the collected data. You’ll also evaluate the process by determining whether the agent’s goal, plan, and actions (GPA) are all aligned.

Finally, you’ll apply inline evaluations to evaluate the agent’s performance during runtime. At every retrieval step, you’ll evaluate if the collected data is relevant to the user’s query. The agent will use this evaluation score to decide if it needs to adjust its plan.

What you’ll do, in detail:

Understand what data agents are and how they can be trustworthy when their goal, plan, and actions are properly aligned.
Build a data agent that plans, performs web searches ,and visualizes or summarizes the results, using a multi-agent workflow implemented in LangGraph.
Expand the agent’s capabilities by adding a Cortex sub-agent that retrieves information from structured and unstructured data stored in Snowflake.
Add tracing to the agent’s workflow to log the steps it takes to answer a query.
Evaluate the context relevance of the retrieved results, the groundedness of the final answer, and its relevance to the user’s query.
Measure the alignment of the agent’s goal, plan, and actions (GPA) by computing metrics such as plan quality, plan adherence, logical consistency, and execution efficiency.
Improve the agent’s performance by adding inline evaluations and updating the agent’s prompt.

By the end, you’ll know how to build, trace, and evaluate a multi-agent workflow that plans tasks, pulls context from structured and unstructured data, performs web search, and summarizes or visualizes the final results.


This course is designed to enhance your skills by integrating AI chatbots as pair programmers in your development process. You’ll learn about how large language models (LLMs) work and how this general-purpose technology can be applied to common software development tasks to help boost productivity, creativity, and support you in your tasks as a developer.

By the end of this course, you will be able to:
- Understand the differences between machine learning and traditional software development
- Describe how large language models generate text
- Prompt an LLM to assist in the tasks that make up the software developer role
- Guide an LLM to complete a task in a specific way by writing detailed prompts and iterating to improve output
- Leverage the depth of software development knowledge encoded in an LLM by prompting it to assume specific job roles or personas
- Write code quickly using an LLM as a pair-coding partner
- Analyze code for efficiency, security, and performance using an LLM

This course assumes you have a background in software development, but are new to using LLMs as part of your development process. By knowing how machine learning systems work, and having an understanding of how they can be applied in software development, you’ll be able to use them more effectively to support your growth and work as a developer or engineer.

Introduction to Generative AI for Software Development


Building AI Applications with Haystack

In Building AI Applications with Haystack you will learn a high-level orchestration framework that helps ensure your applications are flexible, extendible, and maintainable, even as the technology stack changes, user needs arise, and new features are added.

Using a framework can provide common features out of the box that significantly speeds up the development process. Haystack offers robust and flexible architecture and framework for building AI applications. It manages complexity and helps you focus more on developing your application at a higher level of abstraction.

Throughout the course, you will develop several projects, including a RAG app, a news summarization app, a chat agent with function calling, and a self-reflecting agent with loops.

What you’ll do:

1. Learn about the core abstractions and unique building blocks of the Haystack framework and see how these elements can be combined for various AI use cases.

2. Build a RAG pipeline by using Haystack components, pipelines, and document stores.

3. Create custom components in your pipeline by building a Hacker News summarizer that extends your app’s ability to access APIs.

4. Use conditional routing to create a branching pipeline with a fall back to web-search when the LLM does not have the context needed to fully respond to the user’s query.

5. Build a self-reflecting agent for named entity recognition with a Haystack pipeline that is able to loop using an output validator custom component.

6. Create a chat agent using OpenAI’s function-calling capabilities which allow you to provide Haystack pipelines as tools to the LLM, enhancing that agent’s capabilities.

Start building exciting LLM applications and optimizing your development workflow using Haystack.


AI Python for Beginners

AI Python for Beginners is designed to help you leverage the power of Python programming, even if your goal isn’t to become a software developer or AI engineer. This four-part course that teaches you to code practical AI applications from day one, even if you've never programmed before. You’ll learn with support from an AI chatbot that can help you get immediate feedback, quickly fix bugs, and keep you on track while learning new skills. You'll gain a foundational understanding of Python while building AI-powered tools like custom recipe generators, smart to-do lists, and vacation planners, and learn essential programming concepts such as variables, functions, loops, and data structures.

By the end of this course, you'll be able to write Python scripts that interact with large language models, automate tasks, analyze your own data, and even create simple AI agents - skills that are increasingly valuable across industries from tech and finance to healthcare and creative fields.

Accelerated learning with AI:

Experience a new kind of learning with AI chatbot integration. This intelligent assistant helps you write, test, and debug code, providing instant feedback and personalized guidance, making sure you’re never coding alone. With the rise of AI tools, the effort and time required to learn helpful coding skills has significantly decreased, making it more accessible and beneficial for everyone.


Introduction to On-Device AI

As AI moves beyond the cloud, on-device inference is rapidly expanding to smartphones, IoT devices, robots, AR/VR headsets, and more. Billions of mobile and other edge devices are ready to run optimized AI models.

This course equips you with key skills to deploy AI on device:

1. Explore how deploying models on device reduces latency, enhances efficiency, and preserves privacy.

2. Go through key concepts of on-device deployment such as neural network graph capture, on-device compilation, and hardware acceleration.

3. Convert pretrained models from PyTorch and TensorFlow for on-device compatibility.

4. Deploy a real-time image segmentation model on device with just a few lines of code.

5. Test your model performance and validate numerical accuracy when deploying to on-device environments

6. Quantize and make your model up to 4x faster and 4x smaller for higher on-device performance.

7. See a demonstration of the steps for integrating the model into a functioning Android app.

Learn from Krishna Sridhar, Senior Director of Engineering at Qualcomm, who has played a pivotal role in deploying over 1,000 models on devices and, with his team, has created the infrastructure used by over 100,000 applications.

By learning these techniques, you’ll be positioned to develop and deploy AI to billions of devices and optimize your complex models to run efficiently on the edge.


Advance your PyTorch skills by building sophisticated deep learning models and preparing them for deployment. You’ll design custom architectures that go beyond Sequential models, exploring Siamese Networks, ResNet, and DenseNet to understand how modern systems handle complex data.

You’ll build Transformer architectures and explore how attention mechanisms power modern language models. You’ll also learn how diffusion models generate realistic images by reversing noise. Along the way, you’ll visualize model behavior using saliency maps and class activation maps, and prepare models for deployment with ONNX, MLflow, pruning, and quantization. By the end, you’ll be ready to create efficient, interpretable, and deployable PyTorch models for real-world deep learning tasks.

PyTorch: Advanced Architectures and Deployment


Evaluating and Debugging Generative AI

Machine learning and AI projects require managing diverse data sources, vast data volumes, model and parameter development, and conducting numerous test and evaluation experiments. Overseeing and tracking these aspects of a program can quickly become an overwhelming task.

This course will introduce you to Machine Learning Operations tools that manage this workload. You will learn to use the Weights & Biases platform which makes it easy to track your experiments, run and version your data, and collaborate with your team.


Из этого курса вы узнаете, как создавать успешные проекты по машинному обучению. Вы — лидер команды по внедрению ИИ или хотите им стать? Этот курс научит вас ставить правильные цели для своей команды.

 Многое из содержимого этого курса никогда не предлагалось в других образовательных проектах и наработано на основе моего опыта построения и внедрения многочисленных проектов. В настоящий курс также включены два тренажера, которые позволят вам отработать принятие решений в ходе организации проектов по машинному обучению. Тренажеры дадут вам опыт, на получение которого иначе могло бы потребоваться несколько лет работы в машинном обучении.

После двух недель занятий вы:
— поймете, как диагностировать ошибки в системах машинного обучения;
— научитесь выделять наиболее перспективные направления для снижения количества ошибок;
— получите знания о сложных настройках машинного обучения, таких как несоответствие наборов для обучения тестовым наборам, и сравнении показателей машины с показателями человеческого уровня;
— узнаете, как применять сквозное обучение (end-to-end learning), перенос обучения (transfer learning) и многозадачное обучение (multi-task learning).

Я видел, как команды специалистов впустую тратили месяцы и даже годы работы потому, что не понимали принципы, излагаемые в этом курсе. Я надеюсь, что этот двухнедельный курс сэкономит месяцы вашего времени.

Он независим от других, и для его прохождения нужны только базовые знания в области машинного обучения. Это третий курс специализации «Глубокое обучение».

Структурирование проектов по машинному обучению

Andrew Ng
Younes Bensouda Mourri
Kian Katanforoosh


Enhancing a RAG system’s performance depends on efficiently processing diverse unstructured data sources.

In this course, you’ll learn techniques for representing all sorts of unstructured data, like text, images, and tables, from many different sources and implement them to extend your LLM RAG pipeline to include Excel, Word, PowerPoint, PDF, and EPUB files.

1. How to preprocess data for your LLM application development, focusing on how to work with different document types.

2. How to extract and normalize various documents into a common JSON format and enrich it with metadata to improve search results.

3. Techniques for document image analysis, including layout detection and vision transformers, to extract and understand PDFs, images, and tables.

4. How to build a RAG bot that is able to ingest different documents like PDFs, PowerPoints, and Markdown files.

Apply the skills you’ll learn in this course to real-world scenarios, enhancing your RAG application and expanding its versatility.

Preprocessing Unstructured Data for LLM Applications


Building Systems with the ChatGPT API

In Building Systems With The ChatGPT API, you will learn how to automate complex workflows using chain calls to a large language model. Unlock new development capabilities and improve your efficiency in this brand new short course.

You’ll build:
1. Chains of prompts that interact with the completions of prior prompts.
2. Systems where Python code interacts with both completions and new prompts.
3. A customer service chatbot using all the techniques from this course.

You’ll learn how to apply these skills to practical scenarios, including classifying user queries to a chat agent’s response, evaluating user queries for safety, and processing tasks for chain-of-thought, multi-step reasoning.

This one-hour course, taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI), builds on the lessons taught in the popular ChatGPT Prompt Engineering for Developers, though it is not a prerequisite. Hands-on examples make each concept easy to understand. Built-in Jupyter notebooks allow you to seamlessly experiment with the code and prompts presented in the course.


IA Para Todos (Español)

La IA no es solo para ingenieros. Si desea que su organización esté mejor preparada en el uso de la IA, este es el curso que todos deberían hacer, especialmente sus colegas no técnicos.

En este curso, aprenderá lo siguiente:

- El significado detrás de la terminología común de IA, incluidos términos como redes neuronales, aprendizaje automático, aprendizaje profundo y ciencia de datos
- Lo que la IA puede realmente hacer, y lo que no
- Cómo detectar oportunidades para aplicar la IA a los problemas en su propia organización
- La experiencia de crear proyectos de ciencia de datos y aprendizaje automático
- Cómo trabajar con un equipo de IA y crear una estrategia de IA en su empresa
- Cómo guiar debates sociales y éticos entorno a la IA

Si bien este curso es en gran parte no técnico, los ingenieros también pueden hacerlo para aprender sobre los aspectos comerciales de la IA.


Réseaux neuronaux et Deep Learning

Vous souhaitez vous lancer dans l’IA de pointe ? Ce cours est là pour vous y aider. Les ingénieurs en Deep Learning sont très convoités et la maîtrise de ce domaine vous ouvrira de nombreuses opportunités professionnelles. Le Deep Learning est également un nouveau « superpouvoir » qui vous permettra de développer des systèmes d’IA qui n’étaient même pas envisageables il y a encore quelques années.

Vous découvrirez dans ce cours les bases du Deep Learning. Une fois que vous l’aurez terminé, vous serez en mesure de :
- comprendre les grandes tendances technologiques sur lesquelles repose le Deep Learning ;
- développer, entraîner et utiliser des réseaux neuronaux profonds entièrement connectés ;
- mettre en œuvre des réseaux neuronaux efficaces (vectorisés) ;
- comprendre les principaux paramètres de l’architecture d’un réseau neuronal.

Ce cours ne se limitera pas à une description rapide ou superficielle du Deep Learning, mais vous expliquera également son fonctionnement. Une fois que vous l’aurez terminé, vous serez donc en mesure de l’utiliser dans vos propres applications. En outre, si vous recherchez un poste dans l’IA, vous aurez la capacité de répondre à des questions de base posées lors d’entretiens.

Il s’agit du premier cours de la Spécialisation Deep Learning.

Andrew Ng
Kian Katanforoosh
Younes Bensouda Mourri


Multimodal models like Gemini are pushing the boundaries of what’s possible by unifying traditionally siloed data modalities. With Gemini, you can build applications that seamlessly understand and reason across text, images, and videos, enabling a new class of intelligent systems. For example, building a virtual interior designer that can analyze a user’s room images, understand their style preferences from a text description, and generate personalized design recommendations. Or creating a smart document processing pipeline that can extract structured data from complex PDFs, answer questions based on the content, and generate human-like summaries.

You’ll learn prompt engineering techniques to guide Gemini’s behavior and optimize its performance for diverse use cases, from creative story generation to analytical report writing. And you’ll discover how to integrate Gemini with external APIs and databases using function calling, with the ability to infuse your applications with real-time data and dynamic content.

What you’ll learn, in detail:

1. Introduction to Gemini Models: Explore the Gemini model family, and understand the key differences and use cases for Gemini Nano, Pro, Flash, and Ultra. Understand how to select optimal models based on capability, latency, and cost considerations.

2. Multimodal Prompting and Parameter Control: Learn advanced techniques for structuring effective text-image-video prompts to elicit desired model behavior. Fine-tune key parameters like temperature, top_p, top_k to control model creativity vs determinism.

3. Best Practices for Multimodal Prompting: Get experience with prompt engineering for Gemini multimodal models, and best practices around role assignment, task decomposition, and formatting. Analyze the impact of prompt-image ordering on model performance for different objectives.

4. Creating Use Cases with Images: Build engaging multimodal applications like interior design assistants and receipt itemization tools. Leverage Gemini’s cross-modal reasoning capabilities to analyze relationships between entities across multiple images.

5. Developing Use Cases with Videos: Implement “needle in the haystack” semantic video search powered by Gemini’s large context window. Explore techniques for long-form video QA and content summarization.

6. Integrating Real-Time Data with Function Calling: Extend Gemini with external knowledge and live data via function calling and API integration. Combine Gemini’s Natural Language Understanding (NLU) capabilities with APIs for up-to-date facts and interactive services.

Through this course, you’ll become well-versed in Gemini’s capabilities, how to maximize them in different use cases, and a portfolio of practical techniques for architecting advanced multimodal AI applications.

Note that due to technical requirements, this course features downloadable-only notebooks on the learning platform. You are free to download, review, and run these notebooks on your own.

Large Multimodal Model Prompting with Gemini


In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.

By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow.

The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

At the rate of 5 hours a week, it typically takes 5 weeks to complete this course

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization


Modèles de séquence

Cette formation vous apprendra à construire des modèles pour le langage naturel, l’audio et les autres données de séquence. Grâce à l’apprentissage profond, les algorithmes de séquence fonctionnent beaucoup mieux qu’il y a deux ans ; nous disposons donc de nombreuses applications très intéressantes en matière de reconnaissance vocale, de synthèse musicale, de chatbots, de traduction automatique, de compréhension naturelle du langage, etc.

Vous allez:
- Comprendre comment construire et former des réseaux neuronaux récurrents (RNN) et des variantes couramment utilisées telles que les GRU et les LSTM.
-  Être capable d’appliquer des modèles de séquence à des problèmes de langage naturel, y compris la synthèse de texte.
-  Pouvoir appliquer des modèles de séquence à des applications audio, incluant la reconnaissance vocale et la synthèse musicale.

C’est le cinquième et dernier cours de la spécialisation Apprentissage profond.

deeplearning.ai travaille également en partenariat avec le NVIDIA Deep Learning Institute (DLI) dans le cours 5, Modèles de séquence, afin de fournir une affectation de programmation sur la traduction automatique avec l’apprentissage en profondeur. Vous aurez la possibilité de construire un projet d’apprentissage en profondeur avec un contenu de pointe, pertinent pour l’industrie.


Generative AI models, like large language models, often exceed the capabilities of consumer-grade hardware and are expensive to run. Compressing models through methods such as quantization makes them more efficient, faster, and accessible. This allows them to run on a wide variety of devices, including smartphones, personal computers, and edge devices, and minimizes performance degradation.

Join this course to:

1. Quantize any open source model with linear quantization using the Quanto library.

2. Get an overview of how linear quantization is implemented. This form of quantization can be applied to compress any model, including LLMs, vision models, etc.

3. Apply “downcasting,” another form of quantization, with the Transformers library, which enables you to load models in about half their normal size in the BFloat16 data type.

By the end of this course, you will have a foundation in quantization techniques and be able to apply them to compress and optimize your own generative AI models, making them more accessible and efficient.

Quantization Fundamentals with Hugging Face


Building Agentic RAG with LlamaIndex

Join our new short course and learn from Jerry Liu, co-founder and CEO at LlamaIndex to start using agentic RAG, a framework designed to build research agents skilled in tool use, reasoning, and decision-making with your data.

In this course:

1. Build the simplest form of agentic RAG – a router. Given a query, the router will pick one of two query engines, Q&A or summarization, to execute a query over a single document.

2. Add tool calling to your router agent where you will use an LLM to not only pick a function to execute but also infer an argument to pass to the function.

3. Build a research assistant agent. Instead of tool calling in a single-shot setting, an agent is able to reason over tools in multiple steps.

4. Build a multi-document agent where you will learn how to extend the research agent to handle multiple documents.

Unlike the standard RAG pipeline—suitable for simple queries across a few documents—this intelligent approach adapts based on initial findings to enhance further data retrieval. You’ll learn to develop an autonomous research agent, enhancing your ability to engage with and analyze your data comprehensively.

You’ll practice building agents capable of intelligently navigating, summarizing, and comparing information across multiple research papers from arXiv. Additionally, you’ll learn how to debug these agents, ensuring you can guide their actions effectively.

Explore one of the most rapidly advancing applications of agentic AI!


AI For Medical Treatment

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.

Medical treatment may impact patients differently based on their existing health conditions. In this third course, you’ll recommend treatments more suited to individual patients using data from randomized control trials. In the second week, you’ll apply machine learning interpretation methods to explain the decision-making of complex machine learning models. Finally, you’ll use natural language entity extraction and question-answering methods to automate the task of labeling medical datasets.

These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend that you take the Deep Learning Specialization.

Pranav Rajpurkar
Bora Uyumazturk
Amirhossein Kiani
Eddy Shyu


Improving Accuracy of LLM Applications

Join our new short course, Improving Accuracy of LLM Applications with Lamini and Meta. Learn from Sharon Zhou, Co-founder & CEO of Lamini, and Amit Sangani, Senior Director of Partner Engineering, Meta.

Many developers have experienced frustration with inconsistent results when working with LLM applications. This course offers a systematic approach to enhance the accuracy and reliability of your LLM applications.

You will build an SQL agent, add evaluation metrics to measure performance, and use prompt engineering and self-reflection to make the model perform better. Finally, you will fine-tune the model with techniques like LoRA and memory tuning that embeds facts in model weights to reduce hallucinations.

In this course, you’ll use Llama’s family of open-source models.

What you’ll do:

1. Build a text to SQL agent and simulate situations where it hallucinates to begin the evaluation process.

2. Build an evaluation framework to systematically measure performance, including criteria for good evaluations, best practices, and how to develop an evaluation score.

3. Learn how instruction fine-tuning enhances pre-trained LLMs to follow instructions, and how memory fine-tuning embeds facts to reduce hallucinations.

4. Break fine-tuning myths and see how Performance-Efficient Fine-tuning (PEFT) techniques like Low-Rank Adaptation(LoRA) reduce training time by 100x and Mixture of Memory Experts (MoME) reduces it even further.

5. Go through an iterative process of generating training data and fine-tuning, learning practical tips such as adding examples, generating variations, and filtering generated data to increase model accuracy.

Start improving the accuracy of LLM applications today!


AI pair programming is being rapidly adopted by developers to help with tasks across the tech stack, from catching bugs to quickly inserting entire code snippets.

Learn how LLMs can enhance, debug, and document your code in this new course built in collaboration with Google.

Get free access to Google’s PaLM API and get hands-on experience that you can apply to your own projects. You’ll learn how to use an LLM in pair programming to:

1. Simplify and improve your code

2. Write test cases

3. Debug and refactor your code

4. Explain and document any complex code written in any coding language

Pair Programming with a Large Language Model


PyTorch: Techniques and Ecosystem Tools

Master advanced PyTorch techniques to build high-performing, efficient deep learning models.

In this course, you’ll expand your skills in hyperparameter optimization, model profiling, and workflow efficiency. You’ll experiment with learning rate schedulers, tackle overfitting, and use automated hyperparameter tuning with Optuna to boost model performance. Learn how to design flexible architectures, measure model efficiency with the PyTorch Profiler, and make the most of your compute resources.

You’ll also dive into real-world applications using TorchVision for computer vision tasks like loading, transforming, and augmenting image data, and leveraging Hugging Face for natural language processing. You’ll apply transfer learning and fine-tune pre-trained models to adapt them for new problems.

By the end, you’ll know how to train smarter, optimize deeper, and build PyTorch models ready for production-level deployment.


Team Software Engineering with AI

In this course, you'll elevate your software development skills by learning how to leverage AI in collaborative team environments. You'll discover how to use large language models (LLMs) to streamline testing processes, create comprehensive documentation, and manage complex dependencies.

By the end of this course, you will be able to:

- Utilize LLMs to generate and implement various types of software tests, from exploratory to security testing
- Create clear, useful documentation that follows best practices and language-specific conventions
- Use AI to explore and manage software dependencies, including resolving conflicts and addressing security issues
- Debug common dependency-related problems with AI as your pair-programmer

These skills will enhance your ability to work effectively in development teams, improve code quality, and streamline the software development lifecycle. By learning AI-assisted collaboration techniques, you'll become a more valuable asset to any development team and be better prepared to tackle complex, real-world software engineering challenges.


머신 러닝 프로젝트 구조화

딥 러닝 전문화 과정의 세 번째 과정에서는 성공적인 머신 러닝 프로젝트를 구축하고 머신 러닝 프로젝트 리더로서 의사 결정을 연습하는 방법을 배우게 됩니다.

이 과정을 마치면 머신 러닝 시스템의 오류를 진단할 수 있고, 오류를 줄이기 위한 전략의 우선 순위를 지정하고, 일치하지 않는 training/test set와 같은 복합적인 ML 설정을 이해하며 휴먼 레벨의 성능에 필적 및/또는 능가하는 ML 설정을 이해하고, 종단 간 학습, 전이 학습, 멀티 태스크 러닝을 적용할 수 있게 됩니다.

이는 또한 기본적인 머신 러닝 지식이 있는 학습자를 위한 독립형 과정입니다. 이 과정에서는 많은 딥 러닝 제품을 구축하고 출시한 Andrew Ng의 경험을 활용합니다. AI 팀의 방향을 제시할 수 있는 기술 리더가 되고 싶다면 이 과정은 수년간의 ML 업무 경험을 거친 후에 얻을 수 있는 ‘산업 경험’을 제공해드립니다.

딥 러닝 전문화 과정은 딥 러닝의 기능, 도전 과제 및 결과를 이해하고 첨단 AI 기술 개발에 참여할 수 있도록 준비하는 데 도움이 되는 기본 프로그램입니다. 머신 러닝을 업무에 적용하고, 기술 경력의 수준을 높이고, AI 세계의 최종적인 단계를 밟을 수 있는 지식과 기술을 쌓을 수 있는 경로를 제공합니다.

주당 5시간 기준의 경우 이 과정 완료에는 일반적으로 4주가 소요됩니다.


Réseaux neuronaux convolutifs

Ce cours vous apprendra à créer des réseaux neuronaux convolutifs et à les appliquer aux données d'image. Grâce à l'apprentissage en profondeur, la vision par ordinateur fonctionne beaucoup mieux qu'il y a seulement deux ans, ce qui permet de nombreuses applications passionnantes allant de la conduite autonome en toute sécurité, à la reconnaissance faciale précise, à la lecture automatique des images radiologiques.

Vous allez:
-  Comprendre comment créer un réseau neuronal convolutif, notamment les variations récentes telles que les réseaux résiduels.
-  Savoir appliquer les réseaux convolutifs aux tâches de détection et de reconnaissance visuelles.
- Savoir utiliser le transfert de style neuronal pour générer de l'art.
- Être à même d'appliquer ces algorithmes à une variété d'images, de vidéos et d'autres données 2D ou 3D.

Il s'agit du quatrième cours de spécialisation d'apprentissage approfondi.


Applied Statistics for Data Analytics

Throughout this course, you will learn the fundamental statistical concepts, analyses, and visualizations that serve as the foundation for a career as a data analyst.

Whether you're new to statistics or looking to refresh your skills, this course will equip you with powerful techniques to extract meaningful insights from your data. By the end of this course, you will feel more confident and capable of implementing rigorous statistical analyses in your career as a data analyst!

In the first module, you’ll explore the essential building blocks of statistics that enable rigorous data analysis. By the end, you’ll be able to define populations, samples, and sampling methods; characterize datasets using measures of central tendency, variability, and skewness; use correlation to understand relationships between features; and employ segmentation to reveal insights about different groups within your data. You’ll apply these concepts to real-world scenarios: analyzing movie ratings and durations over time, explaining customer behavior, and exploring healthcare outcomes.

In the second module, you’ll cover key probability rules and concepts like conditional probability and independence, all with real-world examples you’ll encounter as a data analyst. Then you’ll explore probability distributions, both discrete and continuous. You'll learn about important distributions like the binomial and normal distributions, and how they model real-world phenomena. You’ll also see how you can use sample data to understand the distribution of your population, and how to answer common business questions like how common are certain outcomes or ranges of outcomes? Finally, you’ll get hands on with simulation techniques. You'll see how to generate random data following specific distributions, allowing you to model complex scenarios and inform decision-making.

In modules 3 and 4, you'll learn powerful techniques to draw conclusions about populations based on sample data. This is your first foray into inferential statistics. You’ll start by constructing confidence intervals - a way to estimate population parameters like means and proportions with a measure of certainty. You'll learn how to construct and interpret these intervals for both means and proportions. You’ll also visualize how this powerful technique helps you manage the inherent uncertainty when investigating many business questions. Next, you’ll conduct hypothesis testing, a cornerstone of statistical inference that helps you determine whether an observed difference reflects random variation or a true difference. You'll discover how to formulate hypotheses, calculate test statistics, and interpret p-values to make data-driven decisions. You’ll learn tests for means and proportions, as well as how to compare two samples.

Throughout the course, you’ll use large language models as a thought partner for descriptive and inferential statistics. You'll see how AI can help formulate hypotheses, interpret results, and even perform calculations and create visualizations for those statistics.


Build LLM Apps with LangChain.js

JavaScript is the world’s most popular programming language, and now developers can program in JavaScript to build powerful LLM apps.

This course will show webdevs how to expand their toolkits with LangChain.js, a popular JavaScript framework for building with LLMs, and will cover useful concepts for creating powerful, context-aware applications.


If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the DeepLearning.AI TensorFlow Developer Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

In Course 2 of the DeepLearning.AI TensorFlow Developer Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models.

The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Convolutional Neural Networks in TensorFlow


In this course, you will:

• Compare Functional and Sequential APIs, discover new models you can build with the Functional API, and build a model that produces multiple outputs including a Siamese network.
• Build custom loss functions (including the contrastive loss function used in a Siamese network) in order to measure how well a model is doing and help your neural network learn from training data.
• Build off of existing standard layers to create custom layers for your models, customize a network layer with a lambda layer, understand the differences between them, learn what makes up a custom layer, and explore activation functions.
• Build off of existing models to add custom functionality, learn how to define your own custom class instead of using the Functional or Sequential APIs, build models that can be inherited from the TensorFlow Model class, and build a residual network (ResNet) through defining a custom model class.

The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models.

This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

At the rate of 5 hours a week, it typically takes 4 weeks to complete this course.

Custom Models, Layers, and Loss Functions with TensorFlow


In Course 4 of the Natural Language Processing Specialization, you will:

a) Translate complete English sentences into Portuguese using an encoder-decoder attention model,
b) Build a Transformer model to summarize text,
c) Use T5 and BERT models to perform question-answering.

By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, and created tools to translate languages and summarize text!

Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. Please make sure that you’ve completed course 3 - Natural Language Processing with Sequence Models - before starting this course.

This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

At the rate of 5 hours a week, it typically takes 4 weeks to complete one Course.

Natural Language Processing with Attention Models

Younes Bensouda Mourri
Łukasz Kaiser
Eddy Shyu


Advanced Learning Algorithms

In the second course of the Machine Learning Specialization, you will:
• Build and train a neural network with TensorFlow to perform multi-class classification
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

By the end of this Specialization, you will have mastered key theoretical concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

Andrew Ng
Aarti Bagul
Geoff Ladwig
Eddy Shyu


Data Storytelling

In this course, you’ll learn to communicate insights in ways that drive real decisions. You’ll explore how to choose the right format to present your findings and tailor your message to technical and non-technical audiences.

You’ll start by learning how to craft data stories that provide context and align with business goals. Then, you’ll apply those skills by designing clear, effective charts that highlight a single insight. Next, you’ll build interactive dashboards in Tableau using features like annotations, tooltips, and “Show Me.” These skills transfer easily to tools like Power BI and Looker Studio. In the final module, you’ll prepare for the job search: analyzing roles, tailoring your resume and portfolio, building your network, and getting ready for interviews.

Throughout the course, you’ll practice applying these skills to realistic business scenarios so you’re ready to share insights that influence decisions.


Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises. As a learner in this program, you'll need basic to intermediate Python programming skills to be successful.

After completing this course, you will be able to:

•  Describe and quantify the uncertainty inherent in predictions made by machine learning models, using the concepts of probability, random variables, and probability distributions.
•  Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science like Bernoulli, Binomial, and Gaussian distributions
•  Apply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problems
•  Assess the performance of machine learning models using interval estimates and margin of errors
•  Apply concepts of statistical hypothesis testing to commonly used tests in data science like AB testing
•  Perform Exploratory Data Analysis on a dataset to find, validate, and quantify patterns.

Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow visualizations to help you see how the math behind machine learning actually works.

We recommend you have a high school level of mathematics (functions, basic algebra) and familiarity with programming (data structures, loops, functions, conditional statements, debugging). Assignments and labs are written in Python but the course introduces all the machine learning libraries you’ll use.

At the rate of 5 hours per week, it will take you around 4 weeks to complete.

Probability & Statistics for Machine Learning & Data Science


Introducing Multimodal Llama 3.2

Join our new short course, Introducing Multimodal Llama 3.2, and learn from Amit Sangani, Senior Director of AI Partner Engineering at Meta, to learn all about the latest additions to the Llama models 3.1 and 3.2, from custom tool calling to multimodality and the new Llama stack.

Open models are a key building block of AI and a key enabler of AI research. With Meta’s family of open models, anyone can download, customize, fine-tune, or build new applications on top of them, allowing AI innovation. The Llama model family now ranges from 1B model parameters to its 405B foundation model, allowing for diverse use cases and applications.

In this course, you’ll learn about the new vision capabilities that Llama 3.2 brings to the Llama family. You’ll learn how to leverage this along with tool-calling, and Llama Stack, which is an open-source orchestration layer for building on top of the Llama family of models.

In detail, you’ll:

1. Learn about the new models, how they were trained, their features, and how they fit into the Llama family.

2. Understand how to do multimodal prompting with Llama and work on advanced image reasoning use cases such as understanding errors on a car dashboard, adding up the total of three restaurant receipts, grading written math homework, and many more.

3. Learn different roles—system, user, assistant, ipython—in the Llama 3.1 and 3.2 family and the prompt format that identifies those roles.

4. Understand how Llama uses the tiktoken tokenizer, and how it has expanded to a 128k vocabulary size that improves encoding efficiency and enables support for seven non-English languages.

5. Learn how to prompt Llama to call both built-in and custom tools with examples for web search and solving math equations.

6. Learn about ‘Llama Stack API’, which is a standardized interface for canonical toolchain components like fine-tuning or synthetic data generation to customize Llama models and build agentic applications.

Start building exciting applications on Llama!


In this course, you will:

a) Explore image classification, image segmentation, object localization, and object detection. Apply transfer learning to object localization and detection.
b) Apply object detection models such as regional-CNN and ResNet-50, customize existing models, and build your own models to detect, localize, and label your own rubber duck images.
c) Implement image segmentation using variations of the fully convolutional network (FCN) including U-Net and d) Mask-RCNN to identify and detect numbers, pets, zombies, and more.
d) Identify which parts of an image are being used by your model to make its predictions using class activation maps and saliency maps and apply these ML interpretation methods to inspect and improve the design of a famous network, AlexNet.

The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models.

This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

Advanced Computer Vision with TensorFlow


In this course, you will:
- Assess the challenges of evaluating GANs and compare different generative models
- Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs
- Identify sources of bias and the ways to detect it in GANs
- Learn and implement the techniques associated with the state-of-the-art StyleGANs

The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.

Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs.

This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.

At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.

Build Better Generative Adversarial Networks (GANs)


AI and Public Health

In this course, you will be introduced to the basics of artificial intelligence and machine learning and how they are applied in real-world scenarios in the AI for Good space. You will also be introduced to a framework for problem solving where AI is part of the solution. The course concludes with a case study featuring three Jupyter notebook labs where you’ll create an air quality monitoring application for the city of Bogotá, Colombia.


신경망 및 딥 러닝

딥 러닝 전문화의 첫 번째 과정에서는 신경망과 딥 러닝의 기본 개념을 학습합니다.

마지막에는 완전히 연결된 심층 신경망의 구축, 훈련 및 적용, 효율적인(벡터화된) 신경망 구현, 신경망 아키텍처의 주요 파라미터 식별, 딥 러닝을 자체 애플리케이션에 적용 등 딥 러닝의 부상을 주도하는 중요한 기술 동향에 익숙해질 것입니다.

딥 러닝 전문화는 딥 러닝의 기능, 과제 및 결과를 이해하고 첨단 AI 기술 개발에 참여할 수 있도록 준비하는 데 도움이 되는 기본 프로그램입니다. 머신 러닝을 업무에 적용하고, 기술 경력의 수준을 높이고, AI 세계에서 결정적인 단계를 밟을 수 있는 지식과 기술을 얻을 수 있는 경로를 제공합니다.

주당 5시간의 비율로 전문화 과정의 각 과정을 마치는 데 약 5주가 소요됩니다.


Сверточные нейронные сети

Этот курс научит вас строить сверточные нейронные сети и использовать их для обработки изображений. Благодаря глубокому обучению машинное зрение сегодня работает намного лучше, чем всего лишь два года назад, и это позволяет использовать его в самых разных отраслях, начиная от безопасного автономного вождения и точного распознавания лиц и заканчивая автоматической интерпретацией рентгеновских снимков.

В рамках курса вы:

— научитесь строить сверточные нейронные сети, включая их самые современные виды, такие как остаточные сети;
— узнаете, как применять сверточные сети в задачах визуального обнаружения объектов и распознавания изображений;
— узнаете, как использовать нейронную передачу стиля для создания изображений;
— научитесь применять алгоритмы к изображениям, видео и другим 2D- и 3D-данным.

Это четвертый курс специализации «Глубокое обучение».

4 недели обучения по 4–5 часов в день


النماذج المتعاقبة

ستتعلم في هذا المساق كيفية إنشاء نماذج للغة الطبيعية والصوت وغيرها من البيانات المتعاقبة. بفضل التعلم العميق، تعمل خوارزميات التسلسل والتعاقب بشكل أفضل بكثير مما كانت عليه قبل عامين فقط مما يتيح العديد من التطبيقات المثيرة في التعرف على الكلام والتركيبات الموسيقية وروبوتات الدردشة والترجمة الآلية وفهم اللغة الطبيعية وغيرها من التطبيقات الأخرى.

سوف تتعلم كيفية:
- فهم كيفية بناء وتدريب الشبكات العصبونية المتكررة (RNN) والمتغيرات الشائعة الاستخدام مثل GRUs والذاكرة طويلة المدى.
- تطبيق نماذج التسلسل على المشاكل الطبيعية للغة بما في ذلك تركيب النص.
- تطبيق نماذج التسلسل على التطبيقات الصوتية، بما في ذلك التعرف على الكلام والتركيبات الموسيقية.

هذا هو المساق الخامس والأخير من تخصص التعلم العميق.

تشاركdeeplearning.ai أيضًا مع مؤسسة نيفيديا للتعلم العميق في المساق الخامس، نماذج التسلسل، لتوفير مهمة برمجة على الترجمة الآلية مع التعلم العميق. سوف تتاح لك فرصة إنشاء مشروع تعلم عميق بمحتوى متطور وذو صلة بالعملية الصناعية.


Последовательные модели

Данный курс научит вас строить модели естественных языков, звуков и других последовательных данных. Благодаря глубокому обучению последовательные алгоритмы сегодня работают в разы лучше, чем ещё два года назад. Это открывает широчайший спектр возможностей применения алгоритмов в распознавании речи, синтезе музыки, чат-ботах, машинном переводе, понимании естественных языков и во многом другом.

 Вы научитесь:
— строить и обучать рекуррентные нейронные сети (РНС, RNN), а также широко используемые управляемые рекуррентные блоки (УРБ, GRU) и долгую краткосрочную память (ДКП, LSTM);
— применять последовательные модели в задачах по обработке естественного языка, включая синтез текста;
— применять модели последовательностей к звуковой информации, например для распознавания речи или синтеза музыки.

Это пятый и заключительный курс специализации «Глубокое обучение».

 Задача по программированию машинного перевода с глубоким обучением, содержащаяся в этом курсе, разработана deeplearning.ai совместно с партнером — Институтом глубокого обучения NVIDIA (DLI). У вас будет возможность создать проект по глубокому обучению с современным, актуальным для индустрии содержанием.


Quantization in Depth

In Quantization in Depth you will build model quantization methods to shrink model weights to ¼ their original size, and apply methods to maintain the compressed model’s performance. Your ability to quantize your models can make them more accessible, and also faster at inference time.

Implement and customize linear quantization from scratch so that you can study the tradeoff between space and performance, and then build a general-purpose quantizer in PyTorch that can quantize any open source model. You’ll implement  techniques to compress model weights from 32 bits to 8 bits and even 2 bits.

Join this course to:

1. Build and customize linear quantization functions, choosing between two “modes”: asymmetric and symmetric; and three granularities: per-tensor, per-channel, and per-group quantization.

2. Measure the quantization error of each of these options as you balance the performance and space tradeoffs for each option.

3. Build your own quantizer in PyTorch, to quantize any open source model’s dense layers from 32 bits to 8 bits.

4. Go beyond 8 bits, and pack four 2-bit weights into one 8-bit integer.

Quantization in Depth lets you build and customize your own linear quantizer from scratch, going beyond standard open source libraries such as PyTorch and Quanto, which are covered in the short course Quantization Fundamentals, also by Hugging Face.

This course gives you the foundation to study more advanced quantization methods, some of which are recommended at the end of the course.


الشبكات العصبونية الالتفافية

سيُعلمك هذا المساق طريقة إعداد الشبكات العصبونية الالتفافية وتطبيقها على بيانات الصورة. وبفضل التعلم المتعمق، تعمل الرؤية الحاسوبية على نحوٍ أفضل كثيرًا مما كانت عليه في العامين السابقين وأتاح هذا الكثير من التطبيقات الحالية التي تمتد بدءًا من التحكم الذاتي الآمن حتى التعرف على الوجه بصورةٍ دقيقة وحتى القراءة الأوتوماتيكية للصور الإشعاعية. ستُصبح مُلِم بطريقة إعداد شبكة عصبونية التفافية، بما في ذلك التغييرات التي طرأت في الآونة الأخيرة من قبيل الشبكات المتبقية. وستتعرف على طريقة تطبيق الشبكات الالتفافية على وظائف الكشف والتعرف المرئيين. وستتعرف على طريقة استعمال خاصية نقل النمط العصبي لإنتاج الأعمال الفنية. وسيُصبح بوسعك تطبيق هذه الخوارزميات على مجموعة مختلفة من الصور ومقاطع الفيديو وغيرها من المعطيات ثنائية الأبعاد وثلاثية الأبعاد. ويُمثل هذا المساق الرابع من نوعه في تخصص التعلم المتعمق.


In Course 1 of the Natural Language Processing Specialization, you will:

a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes,
b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, and
c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality-sensitive hashing to relate words via approximate k-nearest neighbor search.

By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text.

This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Natural Language Processing with Classification and Vector Spaces


Building Your Own Database Agent

In Building Your Own Database Agent you will develop an AI agent that interacts with databases using natural language, simplifying the process for querying and extracting insights. Created in partnership with Microsoft and taught by Adrian Gonzalez Sanchez, Data and AI Specialist at Microsoft, this course is designed for developers, data professionals, as well as business analysts and professionals who want more sophisticated interaction with their databases through natural language instead of advanced SQL queries.

What you’ll do in this course:

1. Learn about the levels of knowledge customization with Azure OpenAI Service, focusing on RAG to build your first AI agent, deploy your Azure OpenAI Service instance, test the API, and set up an orchestration engine like LangChain to enable these scenarios.

2. Load tabular data from a CSV file and perform natural language queries using the Azure OpenAI service to extract information quickly. Learn to reapply the agent to analyze your own CSV files.

3. Implement LangChain agents to connect to a provided SQL database, and how to build a DB agent that translates natural language to SQL code.

4. Use Azure OpenAI Service’s function calling feature to use pre-built functions for sending queries to databases, improving the efficiency and security of your SQL agent.

5. Work with the Assistants API and test it with the function calling and code interpreter features, which will enable you to connect to SQL databases and create your own DB agents more efficiently.

By the end of the course, you’ll be equipped with the technical knowledge and practical experience to implement similar systems in your own projects or organizations, enabling more efficient and accessible data interaction and analysis.


In this course, you will:
- Learn about GANs and their applications
- Understand the intuition behind the fundamental components of GANs
- Explore and implement multiple GAN architectures
- Build conditional GANs capable of generating examples from determined categories

The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.

Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs.

This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.

Build Basic Generative Adversarial Networks (GANs)


En este curso, a través de una combinación de conferencias de video, demostraciones y prácticas, aprenderás acerca de los componentes principales de un sistema operativo y cómo realizar tareas críticas tales como administrar software y usuarios, y configurar hardware. Vamos a terminar con un ejemplo de cómo puede aparecer este contenido en una entrevista.

Al final de este curso, podrás:
● navegar por los sistemas de archivos de Windows y Linux utilizando una interfaz gráfica de usuario y un intérprete de línea de comandos.
● configurar usuarios, grupos y permisos para el acceso a la cuenta.
● instalar, configurar y eliminar software en los sistemas operativos Windows y Linux.
● configurar particiones de disco y sistemas de archivos.
● comprender cómo funcionan los procesos del sistema y cómo gestionarlos.
● trabajar con registros del sistema y herramientas de conexión remota.
● utilizar el conocimiento del sistema operativo para solucionar problemas comunes en un rol de Especialista de soporte de TI.

Sistemas operativos y tú: Convertirse en un usuario avanzado


في دورة كابستون أو الدورة النهائية الأخيرة هذه لشهادة إدارة المشروعات من Google، ستتدرب على تطبيق معرفة ومهارات إدارة المشروعات التي تعلمتها حتى الآن. نشجع المتعلمين على إكمال الدورات 1-5 قبل بدء الدورة النهائية، حيث أنها توفر الأساس اللازم لإكمال الأنشطة في هذه الدورة.

أثناء تقدمك في هذه الدورة التدريبية، سوف "تراقب" مدير المشروعات في سيناريو العالم الحقيقي وستكمل العشرات من الأنشطة العملية. وسوف تقوم بما يلي:
- تحليل وثائق المشروع لتحديد متطلبات المشروع وتقييم الأطراف المعنية
- إكمال ميثاق المشروع واستخدمه كأداة لمواءمة نطاق المشروع وأهدافه بين الأطراف المعنية
- تحديد المهام والمعالم الرئيسية وتوثيقها وتحديد أولوياتها في خطة المشروع
- تحديد معايير إدارة الجودة واستكشاف كيفية مشاركة البيانات النوعية بشكل فعال
- إظهار تأثير مشروعك من خلال التقارير الفعالة

بنهاية هذه الدورة التدريبية، ستكون قد اكتسبت مجموعة من أدوات إدارة المشروع والتي ستوضح المهارات التي تعلمتها خلال البرنامج بأكمله، مثل قدرتك على إدارة الأطراف المعنية والفرق وتنظيم الخطط وتوصيل تفاصيل المشروع. يمكن أن تظهر هذه الأدوات استعدادك الوظيفي عند التقدم بطلب للحصول على وظائف في هذا المجال. لمزيد من الإعداد للمقابلة لوظائف إدارة المشروعات، سوف تفكر في المشروعات السابقة، وإعداد "عرض ترويجي قصير"، وتتوقع أسئلة المقابلة الشائعة. سيستمر مديرو مشروعات Google الحاليون في إرشادك وتزويدك بالاستراتيجيات والأدوات والموارد لتحقيق أهدافك.

بعد الانتهاء من هذا البرنامج، يجب أن تكون مجهزا للتقدم بطلب للحصول على وظائف على المستوى التمهيدي كمدير مشروعات. ستتاح لك أيضًا الفرصة للمطالبة بشهادة الإنجاز التي ستكون معروفة لدى أصحاب العمل.

كابستون: تطبيق إدارة المشروعات في العالم الحقيقي


هذه هي الدورة التدريبية السادسة في شهادة تحليلات البيانات من Google. ستزودك هذه الدورات بالمهارات اللازمة للتقدم لوظائف محلل البيانات على المستوى التمهيدي. سوف تتعلم كيفية تصور وتقديم نتائج البيانات الخاصة بك عندما تكمل عملية تحليل البيانات. ستوضح لك هذه الدورة التدريبية كيف يمكن أن تساعد تصورات البيانات، مثل لوحات المعلومات المرئية، في إحياء بياناتك. ستستكشف أيضًا Tableau، وهو منصة لتصور البيانات سيساعدك على إنشاء تصورات فعالة لعروضك التقديمية. سيستمر محللو بيانات Google الحاليون بإرشادك وتزويدك بالطرق العملية لإنجاز مهام محلل البيانات الشائعة باستخدام أفضل الأدوات والموارد.

سيتم تجهيز المتعلمين الذين يكملون برنامج الشهادة هذا للتقدم لوظائف المستوى التمهيدي كمحللين بيانات. لا تلزم خبرة سابقة.

بنهاية هذه الدورة، ستكون قادرًا على:
 - فحص أهمية تصور البيانات.
 - تعلم كيفية تكوين سرد مقنع من خلال قصص البيانات.
 - اكتساب فهم لكيفية استخدام Tableau لإنشاء لوحات معلومات وعوامل تصفية للوحة المعلومات.
 - اكتشف كيفية استخدام Tableau لإنشاء تصورات فعالة.
 - اكتشف المبادئ والممارسات المتضمنة في العروض التقديمية الفعالة.
 - تعرف على كيفية مراعاة القيود المحتملة المرتبطة بالبيانات في عروضك التقديمية.
 - فهم كيفية تطبيق أفضل الممارسات على الأسئلة والأجوبة مع جمهورك.

مشاركة البيانات من خلال فن مؤثرات عرض التصور


3. 探索用データを準備する

Google データアナリティクス プロフェッショナル認定プログラムの 3 つめのコースです。このコースでは、1～2 つめのコースで学んだトピックの理解を深めながら、表計算ソフトや SQL などのツールを使って目的に合ったデータを抽出し活用する方法、データの整理と保護の方法など、より実践的なデータアナリティクススキルを身につけるための新しいトピックについても学びます。
また、現職の Google データ アナリストが、最適なツールやリソースを使って、一般的なアナリスト業務を遂行する実践的な方法を指導します。

この認定プログラムを修了すると、エントリーレベルのデータ アナリスト職に応募できるようになります。過去の業務経験は不要です。

このコース修了後の目標は以下の通りです。
- データアナリストが、分析のために収集するデータをどのように決定するかを知る。
 - 構造化データや非構造化データ、データ型、データ形式について学ぶ。
- データの信頼性を確保するために、データの中にあるさまざまな種類のバイアスを識別する方法を知る。
- データベースやデータセットに対して、データアナリストがどのように表計算ソフトや SQL を使用するかを学ぶ。
 - オープンデータや、データ倫理とデータプライバシーの関連性および重要性を理解する。
 - データベースにアクセスし、データを抽出、フィルタリング、並べ替えする方法について理解する。
 - データを整理し、安全に管理するためのベストプラクティスを学ぶ。


Fondamentaux de la cybersécurité

Voici le premier cours de la Certification en cybersécurité de Google. Ces cours vous permettront d'acquérir les compétences dont vous avez besoin pour postuler à un emploi de niveau débutant dans le domaine de la cybersécurité.

Dans ce cours, vous serez initié au monde de la cybersécurité grâce à un programme interactif élaboré par Google. Les événements majeurs qui ont conduit à l'expansion du domaine de la cybersécurité, le rôle essentiel que revêt la cybersécurité dans la gestion d'une entreprise aujourd'hui et les responsabilités professionnelles qui incombent à un analyste cybersécurité débutant, ainsi que les compétences requises, seront mis en avant.

Les employés de Google qui travaillent actuellement dans le domaine de la cybersécurité vous guideront à travers des vidéos, des activités pratiques et des exemples qui simulent des tâches courantes de cybersécurité, et vous aideront à développer vos compétences pour vous préparer à l'emploi.

Les participants qui termineront cette certification seront aptes à postuler à des emplois de niveau débutant dans le domaine de la cybersécurité. Aucune expérience préalable n'est nécessaire.


Pense fora da caixa (de entrada): e-mail marketing é o quarto dos sete cursos do Certificado de Marketing Digital e E-commerce do Google. Este curso abordará como realizar uma campanha de e-mail marketing bem-sucedida. O e-mail marketing é um dos canais de marketing digital mais antigos e comprovados, e é um componente essencial de qualquer estratégia geral de marketing digital. O e-mail é um canal principal para muitas empresas alcançarem seus clientes atuais, incentivarem a interação com a empresa, impulsionarem compras e fidelizarem clientes. Neste curso, você descobrirá o e-mail marketing e verá tópicos como: criar uma estratégia de e-mail marketing, realizar campanhas de e-mail e medir os resultados dessas campanhas. Você também aprenderá a usar listas de e-mail e a empregar automação e fluxos de trabalho.

Os colaboradores do Google que trabalham atualmente na área serão seus guias, fornecendo atividades práticas e exemplos que simulam tarefas comuns de marketing digital e e-commerce, mostrando algumas das melhores ferramentas e recursos usados no trabalho.

Os alunos que concluírem os sete cursos deste programa terão os recursos de que precisam para se candidatar a vagas de trabalho de marketing digital e e-commerce para iniciantes. Nenhuma experiência anterior é necessária.

Ao final deste curso, você será capaz de:
– Escrever prévias de texto eficazes e linhas de assunto com base nas práticas recomendadas
– Criar automação de e-mail marketing e fluxos de trabalho
– Criar e manter listas de e-mail
– Escrever um copy de e-mail eficaz
– Gerenciar contatos e segmentar listas
– Usar as práticas recomendadas para lidar com informações que permitem identificação pessoal, ou PII, e dados do usuário com segurança
– Medir e analisar os resultados da campanha de e-mail


Rethink How You Decide with AI

The right questions make AI a valuable tool for decision-making. "Rethink How You Decide with AI" teaches you how to use AI as a thought partner, allowing you to make more intentional, informed decisions.

Through guided activities, reflection, and hands-on AI exploration, you'll examine the habits, assumptions, and patterns that shape your choices. You'll identify the constraints and tradeoffs behind real decisions, compare different approaches to setting priorities, and practice using AI to generate perspectives, challenge assumptions, and uncover blind spots.

Your goal isn’t finding the "perfect answer," it’s developing a flexible decision-making process that evolves with you. Along the way, you'll learn how to use generative AI to stress-test ideas, evaluate alternatives, and strengthen your reasoning while maintaining ownership of your decisions.

By the end of this course, you’ll have a personalized system that supports your decision-making, allowing you to move forward with your ideas with great clarity, confidence, and intention.

This is the third course in the "Applied AI: Data Analysis, Workflows, and Decisions" series, a three-course series on practical ways to integrate AI into your personal and professional routines.


Translating Research to Communities

Health research must positively impact the lives of the general population, so the public must receive and interpret research findings in a meaningful way. That is why translating research discoveries into practice is an important phase of the translational research spectrum.

In this course, you’ll gain an introduction to the concepts of population health research and the ethical use of data science to drive advances in human health. Specifically, you’ll learn about the computational models used in population health research, the function of community partners in conducting translational research and disseminating findings, as well as the role of population health during health crises like the COVID-19 pandemic, Opioid crisis, and the Flint, Michigan, USA water crisis.

This is the fourth of five courses in the “Translational Science” series.


Leveraging Generative AI for Social Impact Organizations focuses on the issue of resource scarcity commonly faced by social impact organizations and how generative AI tools can help free up limited staff capacity from routine administrative tasks. Social impact organizations often find themselves overwhelmed by repeatedly responding to common inquiries, data management and documentation needs, and other basic functions that limit their ability to fully devote time to core programming and service delivery. In this course, you’ll learn concrete examples of how AI could improve efficiency in areas like automatically answering frequently asked questions, converting information requests into readable reports, or drafting, designing, and distributing communications materials. Gain insight into key opportunities to leverage AI to expand your organization’s bandwidth for mission-critical activities. The course also explores the limitations and ethical considerations of relying on generative AI versus human judgment and nuanced interactions in social programming.


Innovative Climate Solutions

Climate action isn’t just about cutting emissions; it is about reimagining how our societies thrive. Innovative Climate Solutions challenges us to look beyond standard sustainability practices and dive into meaningful climate action.

This course examines key solutions from technological, social, institutional, and grassroots perspectives. Additionally, you’ll take a justice-oriented approach, exploring not just what works technically for communities, but what works equitably.

Ultimately, you will shift from a passive observer to an active analyst of change, learning to recognize how individual choices, grassroots movements, and institutional shifts combine to spark climate action. You will leave with an understanding of the crisis and be equipped to champion the innovative solutions that drive genuine, lasting impact.

This is the third course in the Climate Solutions series, a three-course series on addressing climate change with innovative, community-centered strategies that protect biodiversity for the benefit of all.


Causes of Racial Inequity in Healthcare

The second course of the Addressing Racial Health Inequity in Healthcare specialization you will journey through a survey of critical drivers of racial inequities in healthcare. These critical drivers need to be known in order to implement  interventions that can achieve health equity.

You will explore issues that arise around the financing of healthcare in the U.S, and how this system enables race-based disparities. You will then explore the multiple dimensions of access to care, and how various dimensions perpetuate racial inequities in care. You will also discuss the historical backdrop of mistreatment in healthcare settings that remains pervasive among racial groups. And finally, you will unpack implicit bias and how these biases influence diagnosis and treatment patterns among different racial groups.


Object-oriented programming (or OOP) is a model that allows you to organize data by classes and objects using attributes. It’s also one of the most popular types of programming, making it an essential skill to help you better communicate with programmers in your organization.

In this four-week course from the University of Michigan, you will expand your knowledge of OOP and learn how to write programs with objects from classes and how to write your own classes. You’ll also gain confidence as you learn to understand and modify larger object-oriented programs.

This course has a highly stimulating and creative approach to OOP. This includes activities like creating an image collage as well as a model of a real-world object, such as a Magic 8-Ball.

Through these activities, you’ll put your programming knowledge into practice using fun and innovative methods.

This course will allow you to communicate and work alongside other learners along the journey.
With this support, you’ll be able to use knowledge-sharing and teamwork to broaden your understanding and explore the possibilities of OOP together.

This course is designed for learners interested in extending their knowledge of Python.

It will also be helpful to those wishing to learn a textual programming language or for professionals who work and communicate with programmers but don’t use Python.

The Power of Object-Oriented Programming


Ethics in Engineering

Explore unique case studies in engineering ethics. In this four-week course, you’ll examine different historical case studies and understand how they led to classic engineering failures.

Though each case is unique and has a distinct context, they all share common themes; a backstory, a disastrous event, a post-event with ramifications, and outcomes.

For each case, you’ll watch and hear video lectures and explore foundational literature. You’ll also have the opportunity to discuss the case in detail and check your knowledge through quizzes and reflections on your understanding of the case. This format will help you develop a working knowledge of ethical foundations.

Delve into four case studies, including the VW emissions scandal, the failure of the Denver airport baggage system, the fatal case of the Therac 25 radiation machine, and the software failure of the Ariane 5 rocket launch.

With each case, you’ll identify key aspects that led to the engineering failures and discuss the outcomes of the failures.

You’ll also explore the significance of each case and how they led to corrective actions.

Develop your knowledge of engineering alongside experts in the industry. The course will help you reflect on engineering as a design discipline and its impact on humans.

You’ll be guided by Dr. David Chesney, an expert in both industry and academics with 20 years of experience at General Motors Corporation and 20 years working at the University of Michigan. Dr. Chesney’s background is in Mechanical Engineering, Biomedical Engineering, and Computer Science and he is the Toby Teorey Collegiate Lecturer in Electrical Engineering and Computer Science at the University of Michigan College of Engineering.

This course will be particularly useful for engineering students and professionals, although no prior experience is required.


Unlock the potential of your HR career with “Human Capital: Foundations and Essentials.” Dive into the evolution of HR roles, from traditional titles like Personnel Director to modern designations such as Human Capital Manager and People Operations Leader. This course equips you with the skills to evaluate and implement effective communication and leadership strategies that span the entire people space. Throughout the course, you’ll analyze and reflect on mini-case studies based on real-world scenarios to bridge course content with your own professional experiences.

Explore the legal and strategic aspects of HR as you transition from conventional methods to contemporary practices that prioritize employee engagement, well-being, and workforce development. With an overview of the Human Capital Management Framework and its implications on beliefs, values, and health and safety, this course ensures you are well-rounded in the fundamental and cutting-edge elements of human capital management. Immerse yourself in this dynamic field and emerge with the expertise to drive meaningful change in your organization.

This is the first course in “Extraordinary Human Capital Management,” a course series for human resource professionals interested in advancing their skills to align with organizational and employee needs.


Building Web Applications in Django

In this course, you will learn how Django communicates with a database through model objects. You will explore Object-Relational Mapping (ORM) for database access and how Django models implement this pattern.  We will review the Object-Oriented (OO) pattern in Python.  You will learn basic Structured Query Language (SQL) and database modeling, including one-to-many and many-to-many relationships and how they work in both the SQL and Django models. You will learn how to use the Django console and scripts to work with your application objects interactively.


Additive Manufacturing

This course, Additive Manufacturing, is the third course in the Coursera Specialization, Digital Technologies and the Future of Manufacturing.

In this course, learners will be introduced to the concept of Additive Manufacturing, learn how it is applied in manufacturing, and what businesses should consider as they decide to implement this technology. Considerations include the economics of the technology, information technology infrastructure, manufacturing ecosystem partners, the business value of implementing Additive Manufacturing, and what needs to happen across the organization to ensure successful implementation.

Learners will hear from industry experts as they share their perspectives on the opportunities and challenges of implementing Additive Manufacturing, how Additive Manufacturing is being implemented in their companies, and insights on the future of this technology within their industry and across manufacturing.

The content presented in this course draws on a number of real-life interviews and case studies, and was created through a partnership with Siemens.


In Theories and Frameworks, you’ll take a close look at learners and learning contexts. As you define your target audience, you’ll learn how to use learner personas as a communication tool with stakeholders and how these can be used to influence learning goals, assessments, and activities.

Explore theories of learning and motivation that inform design. You’ll be introduced to learning taxonomies for defining learning outcomes, such as Bloom’s taxonomy and Fink’s Taxonomy of Significant Learning. You’ll take a closer look at design frameworks, such as Backward Design, Integrated Course Design, and the ADDIE model.

Next, you’ll see examples of conceptual tools that can be used to support ideation, brainstorming, and course outlining. This course will include an immersive activity in which you’ll join learning experience design colleagues to develop, refine, and present project plans to stakeholders. By taking this course, you’ll gain experience mapping out a course design from start to finish and be well equipped to continue on in the series.

For teachers in the state of Michigan: This series, "An Introduction to Learning Experience Design (LXD)," is approved in the state of Michigan for a maximum of 44.5 State Continuing Education Clock Hours (SCECHs) for teachers. The successful completion of all 3 courses in the series will be required to claim SCECHs.

Learning Experience Design: Theories and Frameworks


Python Data Structures

This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”.  This course covers Python 3.


This UX course provides an introduction to the fields of UX research and design. Learners will gain an understanding of what is involved in UX research, including conducting interviews, evaluating systems, and analyzing systems using principles of good design. Learners will also learn about the work involved in UX Design, including the generation of promising design solutions and the creation of prototypes at multiple levels of fidelity. By interleaving successive phases of UX Research and Design, learners will see how to learn from inevitable mistakes and improve towards a product with a great UX.

What you'll learn:

- The skills needed for UX research and design

- How UX researchers discover and assess user needs and assess possible designs

- How to conduct a micro-usability test

- How UX designers use sketching and prototyping to develop design concepts
- How to incorporate a user-centered focus into the design process

- Key features of human behavior and describe their impact on the design of interactive systems

- Techniques for critiquing and designing interactive systems based on human capabilities and behavior

Introduction to User Experience Principles and Processes


Climate Change and Adaptation

Climate change hits hardest where people and environments are most vulnerable. Adapting to it is as much a social challenge as a technical one. In "Climate Change & Adaptation," you'll explore the social dimensions of adaptation and how it shapes livelihoods, especially in less-developed regions.

The course examines the concepts at the heart of climate adaptation and impacts: vulnerability and resilience, the ways human societies have historically adapted to climate variability and change, and future adaptation needs that will shape development to come.

This is the second course in the "Climate Solutions" series, a three-course series on addressing climate change with innovative, community-centered strategies that protect biodiversity for the benefit of all.


Web Design for Everybody Capstone

The capstone will develop a higher-quality web portfolio. Students will demonstrate the ability to design and implement a responsive site for a minimum of three platforms. Adherence to validation and accessibility standards will be required. The evolving student implementations will be reviewed each week by capstone peers and teaching assistants to make sure that the student keeps up with the agenda of the course.

Upon completion of this course students will feel comfortable creating and/or updating existing front-end sites, utilizing existing frameworks, and testing sites for accessibility compliance.

This course is only open to students who have completed the first four courses in the Web Design for Everybody specialization: Introduction to HTML5, Introduction to CSS3, Interactivity with JavaScript, and Advanced Styling with Responsive Design.

Colleen van Lent, Ph.D.
Charles Russell Severance


Decoding AI: A Deep Dive into AI Models and Predictions explores the significance of large datasets, demystifies generative artificial intelligence (AI), and challenges common media myths about AI. By defining key terms and exploring how systems “learn” from data, you will gain a baseline understanding of how AI works. Work to understand different critiques of AI narratives, learn to navigate conversations with precision, discern conflicts of interest, and appreciate the multidisciplinary expertise needed to shape AI's impact on society.  This course provides you with the strategies and frameworks to engage in better conversation about the role of AI in your work and beyond.

This is the third course in Understanding Data: Navigating Statistics, Science, and AI Specialization, in which you’ll gain a core foundation for statistical and data literacy and gain an understanding of the data we encounter in our everyday lives.


Introduction to Healthcare Finance

Introduction to Healthcare Finance equips healthcare leaders with fundamental finance tools and concepts necessary for effective collaboration with experts and managing organizations. In this course, you’ll explore healthcare pricing, investment valuation, and pharmaceutical research and development costing. You will consider the motivation, consequences, and valuation techniques essential to helping you make strategic decisions and engage in effective bargaining in healthcare mergers and acquisitions. Get an inside look at the variations and complexities of healthcare pricing, trade-offs between financial risk and performance incentives, and the bargaining processes vital to pricing strategies and organizational viability. Financial valuation techniques, such as net present value and internal rate of return, are explored alongside financial risk considerations and opportunity costs.


Python استخدام قواعد البيانات مع

سيتعرف الطلاب خلال هذه الدورة على أساسيات لغة الاستعلام البنوية (SQL) وتصميم قاعدة البيانات الأساسية لتخزين البيانات كجزءٍ من جمع البيانات متعددة الخطوات وتحليلها ومعالجتها. تستخدم الدورة التدريبية SQLite3 قاعدة بيانات لها. نعمل أيضًا على إنشاء متتبعات الويب وعمليات جمع البيانات متعددة الخطوات وتصورها. نستخدم مكتبة D3.js لإجراء تصور البيانات الأساسية. تتناول هذه الدورة الفصلين 14 و15 من كتاب "Python for Everybody". كي تجتاز هذه الدورة، ينبغي أن تكون على دراية بالمواد المذكورة في الفصل الأول إلى الفصل 13 من الكتاب الدراسي والدورات الثلاث الأولى في هذا التخصص. تتناول هذه الدورة Python 3.

5 أسابيع دراسية، 2- 3 ساعات أسبوعيًا


Programming in C

C remains one of the most popular languages thanks to its portability and efficiency, and laying the groundwork for many programming languages like C++, Java, JavaScript, and Python. In this course, you’ll dive deeper into the syntax of C, learning the functions of the language and how to properly apply them within an algorithm. Identify types, operators, and expressions within C to build recursive functions and examine the role of the C preprocessor in achieving software portability.


Estruturas de dados Python

Este curso apresentará as estruturas de dados centrais da linguagem de programação Python. Vamos superar os fundamentos da programação de procedimentos e explorar como podemos usar as estruturas de dados integradas do Python, como listas, dicionários e tuplas, para realizar análises de dados cada vez mais complexas. Este curso cobrirá os capítulos 6 a 10 do livro “Python para Todos”.  Este curso aborda o Python 3.


The hope good presenters project when pitching their ideas is not naïve hope. They’re not Pollyanna at the podium. Instead, the solutions they offer are supported by research, data, and expertise. This course will teach you how to merge hard facts with an imaginative vision in a way that at once resonates with and inspires your audience.

Speaking and Presenting: Pitches and Persuasion


“Human Capital: Strategic HR and Managing Talent” explores the importance of finding and cultivating talent that transforms your organization. Learn how strategic human capital management drives organizational success and empowers you to align talent management with company goals. Develop a comprehensive framework for leadership and management that fully leverages human capital, equipping you to navigate the complexities of modern HR.

Gain valuable skills in identifying and growing talent through proven human resources frameworks, and understand how to leverage these skills throughout your organization’s strategy execution. In addition to building your talent acquisition skills, this course explores the role of technology, including AI and people analytics, in evolving HR practices. Gain insights into using these innovations to enhance decision-making and strategic planning. By engaging with these topics, you'll understand how to assess and implement strategies that align with your organization's broader objectives.

This is the second course in “Extraordinary Human Capital Management,” a course series for human resource professionals interested in advancing their skills to align with organizational and employee needs.