data/course_data.json

{
    "course": {
        "id": "gen-ai-mastery",
        "title": "Generative AI Mastery",
        "subtitle": "From Fundamentals to Production Deployment",
        "description": "Master the complete spectrum of Generative AI - from foundational machine learning concepts to advanced large language model deployment strategies. This comprehensive course takes you from zero to hero in AI development.",
        "level": "Intermediate",
        "duration": "25+ hours",
        "rating": 4.8,
        "reviews": 12547,
        "enrolled": 89234,
        "last_updated": "2025-02-01",
        "language": "English",
        "certificate": true,
        "instructors": [
            {
                "name": "Dr. Sarah Chen",
                "title": "AI Research Lead, Stanford AI Lab",
                "image": "SC",
                "rating": 4.9,
                "students": 156000,
                "courses": 12
            },
            {
                "name": "Prof. Michael Roberts",
                "title": "Former Google AI Engineer",
                "image": "MR",
                "rating": 4.8,
                "students": 98000,
                "courses": 8
            }
        ],
        "skills": [
            "Machine Learning Fundamentals",
            "Deep Learning Architecture",
            "Large Language Models",
            "RAG Systems",
            "Model Optimization",
            "Production Deployment",
            "MLOps",
            "AI Ethics"
        ],
        "prerequisites": [
            "Basic Python programming",
            "High school mathematics",
            "Familiarity with command line"
        ],
        "target_audience": [
            "Software developers transitioning to AI",
            "Data scientists expanding their skills",
            "Product managers understanding AI capabilities",
            "Researchers exploring LLMs",
            "Entrepreneurs building AI products"
        ]
    },
    "modules": [
        {
            "id": 1,
            "title": "Foundations of AI & Machine Learning",
            "subtitle": "Building a Solid Foundation for AI Excellence",
            "description": "Begin your AI journey with a comprehensive exploration of machine learning fundamentals. This module establishes the core concepts that will serve as the building blocks for your entire AI career. You'll understand not just the 'how' but the 'why' behind every technique.",
            "duration": "5 hours",
            "lessons_count": 8,
            "level": "Beginner",
            "learning_objectives": [
                "Understand the fundamental concepts of machine learning and differentiate between supervised, unsupervised, and reinforcement learning paradigms",
                "Implement and train neural networks from scratch using Python, understanding the mathematics behind forward and backward propagation",
                "Apply gradient descent and its variants to optimize model parameters and understand the trade-offs between different optimization strategies",
                "Diagnose and address common model issues including overfitting, underfitting, and the bias-variance tradeoff",
                "Implement regularization techniques including L1/L2 regularization, dropout, and batch normalization to improve model generalization"
            ],
            "lessons": [
                {
                    "id": 1,
                    "title": "Introduction to Machine Learning",
                    "type": "video",
                    "duration": "25 min",
                    "description": "Explore the foundations of machine learning, its history, and real-world applications. Understand how ML is transforming industries from healthcare to finance.",
                    "content": "Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. In this lesson, we'll explore the three main types of machine learning: supervised learning (learning from labeled data), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning (learning through trial and error). We'll examine real-world applications including recommendation systems, autonomous vehicles, and medical diagnosis. By the end, you'll understand when to apply each type of learning and the key considerations for choosing the right approach for your problem.",
                    "resources": [
                        "Stanford CS229 Lecture Notes - Introduction",
                        "Nature article: 'Machine learning in 10 pictures'"
                    ]
                },
                {
                    "id": 2,
                    "title": "Supervised Learning Deep Dive",
                    "type": "video",
                    "duration": "35 min",
                    "description": "Master the fundamentals of supervised learning, including classification and regression algorithms, evaluation metrics, and best practices.",
                    "content": "Supervised learning is the most common form of machine learning used in practice. We'll explore key algorithms including linear regression for predicting continuous values, logistic regression for binary classification, decision trees for interpretable models, and ensemble methods like random forests for improved accuracy. You'll learn about essential evaluation metrics: accuracy, precision, recall, F1-score, and ROC-AUC curves. We'll cover the critical importance of train-test splits, cross-validation, and avoiding data leakage. By the end, you'll be able to build, evaluate, and optimize supervised learning models for real-world problems.",
                    "resources": [
                        "Scikit-learn Documentation - Supervised Learning",
                        "Kaggle Tutorial: Supervised Learning Basics"
                    ]
                },
                {
                    "id": 3,
                    "title": "Unsupervised Learning & Clustering",
                    "type": "video",
                    "duration": "30 min",
                    "description": "Discover patterns in unlabeled data using clustering algorithms and dimensionality reduction techniques.",
                    "content": "Unsupervised learning reveals hidden patterns in data without labeled outcomes. We'll dive into clustering algorithms: K-means for centroid-based clustering, hierarchical clustering for tree-like structures, and DBSCAN for density-based clustering. You'll learn dimensionality reduction with Principal Component Analysis (PCA) for feature extraction and t-SNE for visualization. We'll explore anomaly detection using isolation forests and autoencoders. Practical applications include customer segmentation, image compression, and fraud detection. You'll implement these techniques on real datasets and learn to interpret unsupervised learning results.",
                    "resources": [
                        "Towards Data Science: Unsupervised Learning Guide",
                        "Scikit-learn Clustering Documentation"
                    ]
                },
                {
                    "id": 4,
                    "title": "Neural Networks Architecture",
                    "type": "video",
                    "duration": "40 min",
                    "description": "Understand the building blocks of neural networks including neurons, layers, activation functions, and network architectures.",
                    "content": "Neural networks are the foundation of modern AI. We'll start with the biological inspiration and move to artificial neurons (perceptrons). You'll understand the role of layers: input layers for data ingestion, hidden layers for feature transformation, and output layers for predictions. We'll explore activation functions in depth: sigmoid and tanh for bounded outputs, ReLU for deep networks, and softmax for multi-class classification. You'll learn about different architectures: feedforward networks for structured data, convolutional networks for images, and recurrent networks for sequences. By the end, you'll be able to design neural network architectures for various problem types.",
                    "resources": [
                        "3Blue1Brown: Neural Networks Series",
                        "Deep Learning Book - Chapter 6"
                    ]
                },
                {
                    "id": 5,
                    "title": "Training Neural Networks",
                    "type": "video",
                    "duration": "45 min",
                    "description": "Learn the complete training pipeline from data preprocessing to model optimization and hyperparameter tuning.",
                    "content": "Training neural networks effectively requires understanding multiple interconnected concepts. We'll cover data preprocessing: normalization, standardization, and handling missing values. You'll learn about loss functions: mean squared error for regression, cross-entropy for classification, and custom losses for specific tasks. We'll explore optimization algorithms: stochastic gradient descent (SGD), momentum, RMSprop, and Adam optimizer. You'll understand the importance of learning rate scheduling and adaptive learning rates. We'll discuss batch training, mini-batch sizes, and their impact on convergence. By the end, you'll be able to train neural networks efficiently and debug common training issues.",
                    "resources": [
                        "Andrej Karpathy: A Recipe for Training Neural Networks",
                        "Papers: Adam Optimizer, Batch Normalization"
                    ]
                },
                {
                    "id": 6,
                    "title": "Backpropagation & Gradient Descent",
                    "type": "video",
                    "duration": "35 min",
                    "description": "Master the mathematics behind neural network training with backpropagation and gradient descent optimization.",
                    "content": "Backpropagation is the algorithm that makes training deep networks possible. We'll derive the backpropagation equations step by step, understanding how gradients flow through the network. You'll learn about computational graphs and automatic differentiation. We'll explore gradient descent variants: batch gradient descent for stable convergence, stochastic gradient descent for faster iterations, and mini-batch gradient descent as the practical middle ground. You'll understand the challenges: vanishing and exploding gradients, saddle points, and local minima. We'll discuss techniques to address these: careful initialization, gradient clipping, and momentum-based methods. By the end, you'll have a deep understanding of how neural networks actually learn.",
                    "resources": [
                        "Christopher Olah: Calculus on Computational Graphs",
                        "Stanford CS231n: Optimization Notes"
                    ]
                },
                {
                    "id": 7,
                    "title": "Bias-Variance Tradeoff",
                    "type": "reading",
                    "duration": "25 min",
                    "description": "Understand one of the most fundamental concepts in machine learning model selection and evaluation.",
                    "content": "The bias-variance tradeoff is central to understanding model performance. Bias refers to error from incorrect assumptions in the learning algorithm - high bias leads to underfitting. Variance refers to error from sensitivity to fluctuations in the training set - high variance leads to overfitting. We'll explore how model complexity affects both: simple models have high bias and low variance, while complex models have low bias and high variance. You'll learn diagnostic techniques: learning curves to detect underfitting/overfitting, validation curves to understand hyperparameter effects. We'll discuss the role of ensemble methods in reducing variance and feature engineering in reducing bias. By the end, you'll be able to diagnose model issues and apply appropriate remedies.",
                    "resources": [
                        "Scott Fortmann-Roe: Understanding the Bias-Variance Tradeoff",
                        "Elements of Statistical Learning - Chapter 7"
                    ]
                },
                {
                    "id": 8,
                    "title": "Regularization Techniques",
                    "type": "video",
                    "duration": "30 min",
                    "description": "Prevent overfitting and improve model generalization with regularization methods.",
                    "content": "Regularization is essential for building models that generalize well to unseen data. We'll cover L1 (Lasso) regularization for feature selection and sparse models, and L2 (Ridge) regularization for weight decay. You'll learn about dropout as a powerful regularization technique for neural networks, understanding its theoretical motivation and practical implementation. We'll explore batch normalization for training stability and its regularization effects. You'll understand early stopping as a form of regularization based on validation performance. We'll discuss data augmentation for computer vision and its role in regularization. By the end, you'll have a toolkit of regularization techniques to improve any model.",
                    "resources": [
                        "Dropout Paper: Srivastava et al.",
                        "Batch Normalization Paper: Ioffe & Szegedy"
                    ]
                }
            ],
            "quiz": {
                "title": "Module 1 Assessment",
                "description": "Test your understanding of machine learning fundamentals",
                "time_limit": "15 minutes",
                "passing_score": 80,
                "questions": [
                    {
                        "question": "Which of the following is an example of unsupervised learning?",
                        "options": ["Image classification", "Customer segmentation", "Spam detection", "Weather prediction"],
                        "correct": 1,
                        "explanation": "Customer segmentation is unsupervised because it groups customers based on similarities in data without predefined labels. The algorithm discovers natural groupings in the data."
                    },
                    {
                        "question": "What problem does high bias typically indicate?",
                        "options": ["Overfitting", "Underfitting", "Good generalization", "None of the above"],
                        "correct": 1,
                        "explanation": "High bias indicates the model is too simple to capture the underlying pattern in the data, leading to underfitting. The model makes strong assumptions that don't match reality."
                    },
                    {
                        "question": "Which activation function is most commonly used in hidden layers of deep networks?",
                        "options": ["Sigmoid", "Tanh", "ReLU", "Linear"],
                        "correct": 2,
                        "explanation": "ReLU (Rectified Linear Unit) is most commonly used because it avoids the vanishing gradient problem, is computationally efficient, and has been shown to work well in practice for deep networks."
                    },
                    {
                        "question": "What is the primary purpose of backpropagation?",
                        "options": ["Initializing weights", "Updating weights to minimize loss", "Scaling input data", "Reducing model size"],
                        "correct": 1,
                        "explanation": "Backpropagation calculates gradients of the loss function with respect to each weight in the network, allowing us to update weights in the direction that minimizes the loss."
                    },
                    {
                        "question": "Which regularization technique randomly deactivates neurons during training?",
                        "options": ["L1 Regularization", "L2 Regularization", "Dropout", "Batch Normalization"],
                        "correct": 2,
                        "explanation": "Dropout randomly sets a fraction of neurons to zero during each training iteration, preventing the network from relying too heavily on any single neuron and improving generalization."
                    }
                ]
            },
            "assignment": {
                "title": "Building Your First Neural Network",
                "description": "Implement a complete neural network pipeline for image classification on the MNIST dataset. This hands-on assignment will solidify your understanding of neural network architecture, training, and evaluation.",
                "time_estimate": "4-6 hours",
                "difficulty": "Intermediate",
                "tasks": [
                    {
                        "id": 1,
                        "title": "Data Loading and Preprocessing",
                        "description": "Load the MNIST dataset and implement proper preprocessing including normalization, train-validation split, and data exploration.",
                        "hints": ["Use torchvision.datasets or keras.datasets for easy loading", "Normalize pixel values to [0, 1] range", "Visualize sample images to understand the data"]
                    },
                    {
                        "id": 2,
                        "title": "Network Architecture Design",
                        "description": "Design a feedforward neural network with: input layer (784 neurons for 28x28 images), two hidden layers (128 and 64 neurons with ReLU activation), and output layer (10 neurons with softmax activation).",
                        "hints": ["Use nn.Sequential in PyTorch or Sequential in Keras", "Consider adding dropout between layers", "Initialize weights properly"]
                    },
                    {
                        "id": 3,
                        "title": "Training Pipeline Implementation",
                        "description": "Implement the complete training pipeline: loss function (cross-entropy), optimizer (SGD or Adam), training loop with mini-batches, and validation after each epoch.",
                        "hints": ["Use DataLoader for batch processing", "Track training and validation loss", "Print progress every N batches"]
                    },
                    {
                        "id": 4,
                        "title": "Hyperparameter Experimentation",
                        "description": "Experiment with different learning rates (0.1, 0.01, 0.001), batch sizes (32, 64, 128), and observe their impact on training convergence and final accuracy.",
                        "hints": ["Create a systematic experiment tracking system", "Use validation accuracy to compare", "Plot learning curves for each configuration"]
                    },
                    {
                        "id": 5,
                        "title": "Regularization and Analysis",
                        "description": "Implement dropout regularization and L2 regularization. Compare models with and without regularization in terms of training loss, validation accuracy, and overfitting behavior.",
                        "hints": ["Start with dropout rate of 0.2-0.5", "Compare learning curves", "Analyze where the model makes mistakes using confusion matrix"]
                    }
                ],
                "deliverables": [
                    "Complete Jupyter notebook with well-commented code",
                    "Training and validation loss/accuracy curves for different configurations",
                    "Confusion matrix and analysis of misclassified examples",
                    "500-word reflection on bias-variance tradeoff observed in your experiments",
                    "Final model achieving at least 97% validation accuracy"
                ],
                "rubric": {
                    "code_quality": "20% - Clean, well-commented, following best practices",
                    "implementation": "30% - Correct implementation of all required components",
                    "experiments": "25% - Thorough exploration of hyperparameters",
                    "analysis": "15% - Insightful analysis of results and tradeoffs",
                    "accuracy": "10% - Final model achieving target accuracy"
                }
            },
            "resources": [
                {
                    "type": "video",
                    "title": "3Blue1Brown: Neural Networks",
                    "url": "https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi",
                    "description": "Beautiful visual explanations of neural network concepts"
                },
                {
                    "type": "book",
                    "title": "Deep Learning",
                    "authors": "Goodfellow, Bengio, Courville",
                    "description": "Comprehensive textbook on deep learning fundamentals"
                },
                {
                    "type": "course",
                    "title": "Stanford CS231n",
                    "url": "http://cs231n.stanford.edu/",
                    "description": "Stanford's course on convolutional neural networks"
                }
            ]
        },
        {
            "id": 2,
            "title": "Advanced AI Architectures",
            "subtitle": "Mastering Computer Vision and Natural Language Processing",
            "description": "Dive deep into the architectures that power modern AI applications. From convolutional networks that see to transformers that understand language, you'll learn the design principles behind these powerful models and gain hands-on experience implementing them.",
            "duration": "6 hours",
            "lessons_count": 8,
            "level": "Intermediate",
            "learning_objectives": [
                "Design and implement convolutional neural networks (CNNs) for image classification, object detection, and segmentation tasks",
                "Understand and apply recurrent neural networks (RNNs, LSTMs, GRUs) for sequence modeling and time series prediction",
                "Master the attention mechanism and transformer architecture that underlies modern language models",
                "Implement transfer learning and fine-tuning strategies using pre-trained models",
                "Apply deep learning to real-world problems in computer vision and natural language processing"
            ],
            "lessons": [
                {
                    "id": 1,
                    "title": "Computer Vision Fundamentals",
                    "type": "video",
                    "duration": "40 min",
                    "description": "Understand how computers perceive and process visual information, from basic image operations to feature extraction.",
                    "content": "Computer vision enables machines to derive meaningful information from visual inputs. We'll start with image representation: pixels, color spaces (RGB, HSV, LAB), and image formats. You'll learn fundamental operations: convolution, pooling, and their role in feature extraction. We'll explore edge detection (Sobel, Canny), corner detection (Harris), and feature descriptors (SIFT, HOG). Understanding these classical techniques provides intuition for modern deep learning approaches. We'll discuss the ImageNet dataset and how it revolutionized computer vision. By the end, you'll appreciate how deep learning builds upon these foundational concepts."
                },
                {
                    "id": 2,
                    "title": "Convolutional Neural Networks",
                    "type": "video",
                    "duration": "50 min",
                    "description": "Master CNN architecture including convolutional layers, pooling, and the design patterns behind successful models.",
                    "content": "Convolutional Neural Networks are the backbone of modern computer vision. We'll explore the convolution operation in detail: kernels, strides, padding, and the receptive field concept. You'll understand pooling layers (max, average, global) for spatial downsampling and invariance. We'll study landmark architectures: LeNet-5 for digit recognition, AlexNet that won ImageNet 2012, VGGNet for depth, ResNet with skip connections, and EfficientNet for scaling. You'll learn about modern techniques: depthwise separable convolutions, squeeze-and-excitation blocks, and attention in CNNs. By the end, you'll be able to design CNN architectures for various vision tasks."
                },
                {
                    "id": 3,
                    "title": "Object Detection & Segmentation",
                    "type": "video",
                    "duration": "45 min",
                    "description": "Extend classification to object detection and semantic segmentation with YOLO, R-CNN, and U-Net architectures.",
                    "content": "Beyond classification, real-world applications require localization and segmentation. We'll cover object detection: the sliding window approach, region proposals (R-CNN family: R-CNN, Fast R-CNN, Faster R-CNN), and single-shot detectors (YOLO, SSD). You'll learn about anchor boxes, non-maximum suppression, and evaluation metrics (IoU, mAP). For segmentation, we'll explore semantic segmentation with FCNs and U-Net, instance segmentation with Mask R-CNN, and panoptic segmentation. We'll discuss practical considerations: handling small objects, real-time inference, and multi-scale detection. By the end, you'll be able to build systems that understand not just what's in an image, but where."
                },
                {
                    "id": 4,
                    "title": "Natural Language Processing Foundations",
                    "type": "video",
                    "duration": "45 min",
                    "description": "Learn text representation techniques from bag-of-words to word embeddings, and sequence modeling with RNNs.",
                    "content": "Natural Language Processing enables machines to understand human language. We'll start with text preprocessing: tokenization, stemming, lemmatization, and stop word removal. You'll learn text representations: one-hot encoding, bag-of-words, TF-IDF, and their limitations. We'll dive into word embeddings: Word2Vec (skip-gram, CBOW), GloVe, and FastText, understanding how they capture semantic relationships. We'll introduce sequence modeling with RNNs: vanilla RNNs, the vanishing gradient problem, and solutions with LSTM and GRU. You'll implement text classification and sentiment analysis. By the end, you'll understand how language is represented and processed by neural networks."
                },
                {
                    "id": 5,
                    "title": "Sequence-to-Sequence Models",
                    "type": "video",
                    "duration": "40 min",
                    "description": "Build encoder-decoder architectures for translation, summarization, and other sequence transduction tasks.",
                    "content": "Sequence-to-sequence models transform one sequence into another, enabling machine translation, summarization, and question answering. We'll explore the encoder-decoder architecture: encoding the input sequence into a context vector and decoding it into the output sequence. You'll understand the limitations of fixed-length context vectors and how attention mechanisms address this bottleneck. We'll cover beam search for decoding, teacher forcing for training, and techniques to handle out-of-vocabulary words. We'll discuss evaluation metrics: BLEU for translation, ROUGE for summarization. By the end, you'll be able to build and train seq2seq models for various applications."
                },
                {
                    "id": 6,
                    "title": "Attention Mechanism",
                    "type": "video",
                    "duration": "35 min",
                    "description": "Understand the attention mechanism that revolutionized NLP and became the foundation of transformers.",
                    "content": "Attention allows models to focus on relevant parts of the input when producing each part of the output. We'll trace its development: from Bahdanau attention (additive) to Luong attention (multiplicative) to self-attention. You'll understand the intuition: dynamic weighting of encoder states based on the current decoder state. We'll dive into scaled dot-product attention: queries, keys, and values. You'll learn about multi-head attention for capturing different types of relationships. We'll discuss computational considerations: quadratic complexity with sequence length and approximate attention methods. By the end, you'll have a deep understanding of attention, the key innovation behind transformers."
                },
                {
                    "id": 7,
                    "title": "Transformer Architecture",
                    "type": "video",
                    "duration": "50 min",
                    "description": "Master the transformer architecture that powers GPT, BERT, and all modern large language models.",
                    "content": "The transformer architecture, introduced in 'Attention Is All You Need' (2017), revolutionized NLP and beyond. We'll dissect its components: multi-head self-attention, position-wise feed-forward networks, layer normalization, and residual connections. You'll understand the encoder stack for understanding and the decoder stack for generation. We'll explore positional encoding for sequence order information. We'll discuss encoder-only models (BERT), decoder-only models (GPT), and encoder-decoder models (T5). You'll learn about computational efficiency, parallel training, and scaling laws. By the end, you'll understand the architecture that underlies all modern language models."
                },
                {
                    "id": 8,
                    "title": "Transfer Learning & Pre-trained Models",
                    "type": "video",
                    "duration": "45 min",
                    "description": "Leverage pre-trained models to achieve state-of-the-art results with limited data and compute.",
                    "content": "Transfer learning enables leveraging knowledge from pre-trained models for new tasks. We'll explore the transfer learning pipeline: pre-training on large datasets (ImageNet for vision, massive text corpora for NLP), then fine-tuning on target tasks. You'll learn about feature extraction vs. fine-tuning, freezing layers, and learning rate scheduling. We'll cover model zoos: torchvision for vision models, Hugging Face Transformers for language models. We'll discuss domain adaptation, multi-task learning, and prompt engineering for language models. By the end, you'll be able to effectively use pre-trained models for diverse applications with limited labeled data."
                }
            ],
            "quiz": {
                "title": "Module 2 Assessment",
                "description": "Test your understanding of advanced AI architectures",
                "time_limit": "15 minutes",
                "passing_score": 80,
                "questions": [
                    {
                        "question": "Which component of a CNN is responsible for feature extraction?",
                        "options": ["Fully connected layers", "Pooling layers", "Convolutional layers", "Dropout layers"],
                        "correct": 2,
                        "explanation": "Convolutional layers apply learnable filters to input images, extracting hierarchical features from low-level edges to high-level semantic concepts."
                    },
                    {
                        "question": "What problem do LSTMs primarily solve compared to basic RNNs?",
                        "options": ["Faster training", "Reduced parameters", "Long-term dependency issues", "Better image recognition"],
                        "correct": 2,
                        "explanation": "LSTMs address the vanishing gradient problem in RNNs through their gating mechanisms (input, forget, output gates), enabling them to capture long-term dependencies in sequences."
                    },
                    {
                        "question": "In attention mechanisms, what do the query, key, and value represent?",
                        "options": ["Input, output, and loss", "Search terms, relevance, and content", "Training, validation, and test sets", "Weights, biases, and activations"],
                        "correct": 1,
                        "explanation": "In attention, queries represent what we're looking for, keys determine relevance/compatibility, and values contain the actual content to be attended to and aggregated."
                    },
                    {
                        "question": "Which technique is used to reduce spatial dimensions in CNNs?",
                        "options": ["Convolution", "Pooling", "Normalization", "Padding"],
                        "correct": 1,
                        "explanation": "Pooling (max or average) reduces spatial dimensions by summarizing regions of the feature map, providing translation invariance and reducing computation."
                    },
                    {
                        "question": "What is the main advantage of transfer learning?",
                        "options": ["Eliminates need for training data", "Reduces computation time using pre-trained models", "Guarantees better accuracy", "Works only for image classification"],
                        "correct": 1,
                        "explanation": "Transfer learning leverages knowledge from models pre-trained on large datasets, significantly reducing training time and data requirements for new tasks."
                    }
                ]
            },
            "assignment": {
                "title": "Computer Vision & NLP Project",
                "description": "Build an image classification system and a text sentiment analyzer using transfer learning. Compare custom architectures with pre-trained models.",
                "time_estimate": "6-8 hours",
                "difficulty": "Intermediate",
                "tasks": [
                    {
                        "id": 1,
                        "title": "Dataset Selection and Exploration",
                        "description": "Choose a dataset for image classification (e.g., flowers, food, or satellite imagery) and a text dataset for sentiment analysis. Perform exploratory data analysis."
                    },
                    {
                        "id": 2,
                        "title": "Custom CNN Implementation",
                        "description": "Design and implement a CNN architecture from scratch for your chosen dataset. Document your design choices."
                    },
                    {
                        "id": 3,
                        "title": "Transfer Learning with Vision Models",
                        "description": "Fine-tune a pre-trained model (ResNet, EfficientNet, or Vision Transformer) on your dataset. Compare with your custom CNN."
                    },
                    {
                        "id": 4,
                        "title": "Text Classification Pipeline",
                        "description": "Build a sentiment classifier using both traditional methods (TF-IDF + classifier) and transformer-based models (BERT)."
                    },
                    {
                        "id": 5,
                        "title": "Analysis and Comparison",
                        "description": "Compare custom vs. pre-trained models in terms of accuracy, training time, and data requirements. Visualize results with appropriate metrics."
                    }
                ],
                "deliverables": [
                    "Complete code for both projects with documentation",
                    "Training curves and performance metrics",
                    "Comparative analysis report (1000 words)",
                    "Visualization of model predictions and errors",
                    "Recommendations for model selection based on requirements"
                ]
            }
        },
        {
            "id": 3,
            "title": "Generative AI & Large Language Models",
            "subtitle": "Understanding and Building with Modern AI",
            "description": "Explore the revolutionary world of generative AI and large language models. From understanding how GPT and BERT work to implementing RAG systems and fine-tuning, you'll gain practical skills for building AI applications.",
            "duration": "7 hours",
            "lessons_count": 8,
            "level": "Advanced",
            "learning_objectives": [
                "Understand the architecture and training of large language models including GPT, BERT, and T5",
                "Implement prompt engineering techniques to effectively use LLMs for various tasks",
                "Build retrieval-augmented generation (RAG) systems combining LLMs with external knowledge",
                "Apply parameter-efficient fine-tuning methods (LoRA, QLoRA) to adapt LLMs to specific domains",
                "Evaluate LLM performance using appropriate metrics and benchmarks"
            ],
            "lessons": [
                {
                    "id": 1,
                    "title": "Large Language Model Fundamentals",
                    "type": "video",
                    "duration": "55 min",
                    "description": "Understand the architecture, training, and capabilities of modern large language models.",
                    "content": "Large Language Models (LLMs) have transformed AI applications. We'll explore the language modeling task: predicting the next token given previous context. You'll understand scaling laws: how performance improves with model size, data, and compute. We'll study different model families: GPT (decoder-only, autoregressive), BERT (encoder-only, bidirectional), and T5 (encoder-decoder, text-to-text). We'll discuss training objectives: causal language modeling, masked language modeling, and instruction tuning. You'll learn about emergent abilities, chain-of-thought reasoning, and in-context learning. By the end, you'll understand how LLMs work and their capabilities and limitations."
                },
                {
                    "id": 2,
                    "title": "Prompt Engineering Mastery",
                    "type": "video",
                    "duration": "45 min",
                    "description": "Learn techniques for effectively communicating with LLMs through prompts to get optimal results.",
                    "content": "Prompt engineering is the art of crafting inputs to get desired outputs from LLMs. We'll cover prompt design principles: clarity, specificity, and structure. You'll learn techniques: few-shot learning with examples, chain-of-thought prompting for reasoning, and role prompting for persona-based responses. We'll explore advanced methods: self-consistency, tree-of-thoughts, and ReAct (reasoning + acting). You'll understand system prompts, user prompts, and assistant responses. We'll discuss prompt templates, variables, and building reusable prompt libraries. By the end, you'll be able to effectively use prompts to accomplish complex tasks with LLMs."
                },
                {
                    "id": 3,
                    "title": "Embeddings and Vector Databases",
                    "type": "video",
                    "duration": "40 min",
                    "description": "Understand semantic embeddings and vector databases for efficient similarity search.",
                    "content": "Embeddings are dense vector representations that capture semantic meaning. We'll explore embedding models: Word2Vec, sentence transformers, and OpenAI embeddings. You'll understand embedding spaces: similarity measures (cosine, Euclidean, dot product), semantic clustering, and visualization with t-SNE/UMAP. We'll cover vector databases: Pinecone, Weaviate, Chroma, and FAISS. You'll learn about indexing strategies for efficient similarity search: HNSW, IVF, and PQ. We'll discuss embedding use cases: semantic search, clustering, and retrieval. By the end, you'll be able to build systems that understand semantic similarity."
                },
                {
                    "id": 4,
                    "title": "Retrieval-Augmented Generation (RAG)",
                    "type": "video",
                    "duration": "50 min",
                    "description": "Build systems that combine LLMs with external knowledge bases for accurate, up-to-date responses.",
                    "content": "RAG addresses LLM limitations by grounding responses in retrieved knowledge. We'll explore the RAG pipeline: document chunking, embedding, retrieval, and generation. You'll learn chunking strategies: fixed-size, semantic, and recursive splitting. We'll cover retrieval methods: dense retrieval with embeddings, sparse retrieval with BM25, and hybrid approaches. You'll understand reranking for improved relevance. We'll discuss advanced RAG: query transformations, multi-hop retrieval, and citation generation. You'll implement a complete RAG system with LangChain or LlamaIndex. By the end, you'll be able to build RAG systems that provide accurate, sourced answers."
                },
                {
                    "id": 5,
                    "title": "Fine-Tuning LLMs",
                    "type": "video",
                    "duration": "50 min",
                    "description": "Learn techniques for adapting pre-trained LLMs to specific tasks and domains.",
                    "content": "Fine-tuning adapts pre-trained models to specific tasks. We'll cover full fine-tuning: when it's appropriate and computational requirements. You'll learn parameter-efficient methods: adapters, prefix tuning, and LoRA (Low-Rank Adaptation). We'll explore QLoRA for fine-tuning with limited GPU memory. You'll understand instruction tuning: creating instruction datasets, formatting, and training. We'll discuss data preparation: cleaning, deduplication, and quality filtering. You'll learn evaluation: held-out datasets, reference-based metrics, and human evaluation. By the end, you'll be able to fine-tune LLMs effectively for your specific use case."
                },
                {
                    "id": 6,
                    "title": "LLM Evaluation and Safety",
                    "type": "video",
                    "duration": "40 min",
                    "description": "Understand how to evaluate LLM performance and implement safety measures.",
                    "content": "Evaluating LLMs is crucial for deployment. We'll cover benchmark evaluations: MMLU, HellaSwag, and GSM8K. You'll learn about task-specific metrics: BLEU, ROUGE, and BERTScore for generation; accuracy for classification. We'll discuss human evaluation: A/B testing, preference ranking, and qualitative assessment. You'll understand LLM safety: bias detection, toxicity filtering, and guardrails. We'll explore red-teaming: adversarial testing and vulnerability assessment. You'll learn about constitutional AI and alignment techniques. By the end, you'll be able to evaluate and deploy LLMs responsibly."
                },
                {
                    "id": 7,
                    "title": "Building LLM Applications",
                    "type": "video",
                    "duration": "45 min",
                    "description": "Design and implement production-ready applications using LLMs.",
                    "content": "Building LLM applications requires more than just API calls. We'll cover architecture patterns: routing, fallbacks, and caching. You'll learn about frameworks: LangChain, LlamaIndex, and Haystack for building LLM apps. We'll discuss memory management: conversation history, summarization, and sliding windows. You'll understand agents: tool use, planning, and multi-step reasoning. We'll explore deployment considerations: latency optimization, cost management, and rate limiting. You'll learn about monitoring: logging, tracing, and debugging LLM applications. By the end, you'll be able to build robust LLM-powered applications."
                },
                {
                    "id": 8,
                    "title": "Multimodal Models",
                    "type": "video",
                    "duration": "35 min",
                    "description": "Explore models that understand and generate across multiple modalities: text, images, and more.",
                    "content": "Multimodal AI combines understanding across different data types. We'll explore vision-language models: CLIP for image-text understanding, BLIP for image captioning, and LLaVA for visual question answering. You'll learn about image generation: diffusion models, DALL-E, and Stable Diffusion. We'll discuss audio models: Whisper for speech recognition and text-to-speech systems. You'll understand cross-modal retrieval: finding images from text and vice versa. We'll cover emerging areas: video understanding, 3D generation, and multimodal agents. By the end, you'll be prepared to work with AI systems that bridge multiple modalities."
                }
            ],
            "quiz": {
                "title": "Module 3 Assessment",
                "description": "Test your understanding of generative AI and LLMs",
                "time_limit": "15 minutes",
                "passing_score": 80,
                "questions": [
                    {
                        "question": "What is the primary difference between GPT and BERT models?",
                        "options": ["GPT is unidirectional, BERT is bidirectional", "GPT is for translation, BERT for generation", "GPT has fewer parameters", "No significant difference"],
                        "correct": 0,
                        "explanation": "GPT uses causal (unidirectional) attention, predicting the next token based on previous tokens. BERT uses bidirectional attention, considering context from both directions simultaneously."
                    },
                    {
                        "question": "What does RAG stand for?",
                        "options": ["Recursive Attention Generation", "Retrieval-Augmented Generation", "Random Access Generation", "Reinforced Adaptive Generation"],
                        "correct": 1,
                        "explanation": "RAG (Retrieval-Augmented Generation) combines retrieval of relevant documents with generation, grounding LLM responses in external knowledge."
                    },
                    {
                        "question": "Which technique adapts a pre-trained LLM without full retraining?",
                        "options": ["Full fine-tuning", "Transfer learning", "PEFT (Parameter-Efficient Fine-Tuning)", "Model distillation"],
                        "correct": 2,
                        "explanation": "PEFT methods like LoRA fine-tune only a small number of parameters while keeping the base model frozen, significantly reducing compute and memory requirements."
                    },
                    {
                        "question": "What is the purpose of attention masking?",
                        "options": ["Increase model parameters", "Prevent attending to padding or future tokens", "Speed up training", "Reduce model size"],
                        "correct": 1,
                        "explanation": "Attention masking prevents the model from attending to padding tokens (in batches) and, in autoregressive models, from attending to future tokens during training."
                    },
                    {
                        "question": "Which is NOT a common LLM challenge?",
                        "options": ["Hallucination", "High computational requirements", "Perfect factual accuracy", "Bias in training data"],
                        "correct": 2,
                        "explanation": "LLMs do NOT have perfect factual accuracy - they can generate plausible-sounding but incorrect information (hallucination). This is a known challenge."
                    }
                ]
            },
            "assignment": {
                "title": "Build a RAG-Powered Q&A System",
                "description": "Create an end-to-end retrieval-augmented generation system for domain-specific question answering, including document processing, vector storage, retrieval, and generation.",
                "time_estimate": "8-10 hours",
                "difficulty": "Advanced",
                "tasks": [
                    {
                        "id": 1,
                        "title": "Domain Selection and Document Collection",
                        "description": "Choose a domain (legal, medical, technical docs) and collect relevant documents for your knowledge base."
                    },
                    {
                        "id": 2,
                        "title": "Document Processing Pipeline",
                        "description": "Implement document loading, text extraction, chunking strategies, and preprocessing."
                    },
                    {
                        "id": 3,
                        "title": "Embedding and Vector Store Setup",
                        "description": "Choose and implement an embedding model, set up a vector database (Chroma, Pinecone, or FAISS), and index your documents."
                    },
                    {
                        "id": 4,
                        "title": "Retrieval System Implementation",
                        "description": "Implement semantic search, experiment with chunk sizes and retrieval parameters, and evaluate retrieval quality."
                    },
                    {
                        "id": 5,
                        "title": "LLM Integration and Generation",
                        "description": "Connect your retrieval system to an LLM, design effective prompts, and implement response generation with citations."
                    },
                    {
                        "id": 6,
                        "title": "Evaluation and Iteration",
                        "description": "Create a test set of questions, evaluate answer quality, compare RAG vs. non-RAG responses, and iterate on improvements."
                    }
                ],
                "deliverables": [
                    "Complete RAG implementation with code documentation",
                    "Comparison of different chunking strategies with retrieval metrics",
                    "Test set of 20+ questions with RAG vs. baseline comparison",
                    "Technical report (2-3 pages) on architecture decisions",
                    "Demo video showing the system in action"
                ]
            }
        },
        {
            "id": 4,
            "title": "Model Optimization & Deployment",
            "subtitle": "From Research to Production",
            "description": "Take your AI models from prototype to production. Learn optimization techniques to make models faster and smaller, and deployment strategies to serve them at scale.",
            "duration": "6 hours",
            "lessons_count": 8,
            "level": "Advanced",
            "learning_objectives": [
                "Apply model compression techniques including quantization, pruning, and knowledge distillation",
                "Optimize inference performance through efficient attention mechanisms and caching",
                "Deploy models using containers, APIs, and serverless architectures",
                "Implement monitoring, logging, and A/B testing for ML systems",
                "Build MLOps pipelines for continuous integration and deployment of ML models"
            ],
            "lessons": [
                {
                    "id": 1,
                    "title": "Model Compression Overview",
                    "type": "video",
                    "duration": "40 min",
                    "description": "Understand the landscape of model compression techniques and when to apply each.",
                    "content": "Model compression enables deployment in resource-constrained environments. We'll explore the motivation: reducing latency, memory, and cost while maintaining accuracy. You'll learn the compression spectrum: from simple quantization to complex knowledge distillation. We'll cover the accuracy-efficiency tradeoff and how to measure it. You'll understand target platforms: edge devices, mobile, browsers, and cloud. We'll discuss benchmarking: throughput, latency, memory footprint, and energy consumption. By the end, you'll have a framework for choosing compression techniques based on your constraints."
                },
                {
                    "id": 2,
                    "title": "Quantization Techniques",
                    "type": "video",
                    "duration": "45 min",
                    "description": "Reduce model size and increase speed through precision reduction with minimal accuracy loss.",
                    "content": "Quantization reduces numerical precision to shrink models and accelerate inference. We'll start with post-training quantization: converting FP32 to INT8 without retraining. You'll learn quantization-aware training: incorporating quantization effects during training for better accuracy. We'll explore different precisions: FP16, BF16, INT8, INT4, and the emerging INT1 (binary) and INT2. You'll understand calibration: choosing optimal quantization ranges for each layer. We'll discuss per-tensor vs. per-channel quantization and symmetric vs. asymmetric schemes. By the end, you'll be able to apply quantization to achieve 2-4x compression with minimal accuracy loss."
                },
                {
                    "id": 3,
                    "title": "Pruning and Sparsity",
                    "type": "video",
                    "duration": "40 min",
                    "description": "Remove unnecessary parameters to create smaller, faster models.",
                    "content": "Pruning removes unnecessary weights from neural networks. We'll cover magnitude-based pruning: removing weights below a threshold. You'll learn structured pruning: removing entire neurons, channels, or attention heads for hardware efficiency. We'll explore the lottery ticket hypothesis: finding sparse trainable subnetworks. You'll understand iterative pruning: gradually increasing sparsity with retraining. We'll discuss unstructured vs. structured sparsity and hardware support. You'll learn about sparse training: training sparse models from scratch. By the end, you'll be able to achieve 10-100x sparsity while maintaining performance."
                },
                {
                    "id": 4,
                    "title": "Knowledge Distillation",
                    "type": "video",
                    "duration": "35 min",
                    "description": "Transfer knowledge from large models to smaller, efficient models.",
                    "content": "Knowledge distillation trains a small student model to mimic a larger teacher model. We'll explore the intuition: soft targets contain more information than hard labels. You'll learn distillation loss: combining hard labels with soft predictions and temperature scaling. We'll cover different distillation approaches: response-based, feature-based, and relation-based. You'll understand progressive distillation: teacher-student-student chains. We'll discuss distillation for specific tasks: BERT to DistilBERT, LLM distillation with chain-of-thought. By the end, you'll be able to compress models while preserving most of their performance."
                },
                {
                    "id": 5,
                    "title": "Efficient Attention Mechanisms",
                    "type": "video",
                    "duration": "45 min",
                    "description": "Optimize the most computationally expensive component of transformers.",
                    "content": "Attention has quadratic complexity with sequence length, limiting applications. We'll explore efficient attention variants: sparse attention patterns, low-rank approximations, and kernel-based methods. You'll learn about FlashAttention: memory-efficient exact attention through tiling and recomputation. We'll cover linear attention: reducing complexity to O(n) with kernel tricks. You'll understand multi-query attention and grouped-query attention for faster inference. We'll discuss sliding window attention for long sequences. You'll learn about hardware-aware attention implementations. By the end, you'll be able to handle long sequences efficiently."
                },
                {
                    "id": 6,
                    "title": "Deployment Strategies",
                    "type": "video",
                    "duration": "50 min",
                    "description": "Deploy models for production using containers, APIs, and scalable architectures.",
                    "content": "Production deployment requires careful consideration of latency, throughput, and reliability. We'll cover model serving frameworks: TorchServe, TensorFlow Serving, and Triton Inference Server. You'll learn about containerization with Docker and orchestration with Kubernetes. We'll discuss API design: REST vs. gRPC, batching strategies, and async processing. You'll understand autoscaling: based on CPU/GPU utilization, queue length, and custom metrics. We'll explore edge deployment: ONNX Runtime, TensorRT, and Core ML. You'll learn about model versioning, canary deployments, and rollbacks. By the end, you'll be able to deploy models that scale."
                },
                {
                    "id": 7,
                    "title": "MLOps for LLMs",
                    "type": "video",
                    "duration": "45 min",
                    "description": "Build pipelines for continuous training, evaluation, and deployment of LLM systems.",
                    "content": "MLOps brings software engineering best practices to ML systems. We'll cover CI/CD for ML: automated testing, model validation, and deployment pipelines. You'll learn about experiment tracking: MLflow, Weights & Biases, and Neptune. We'll discuss model registries: versioning, metadata, and lineage. You'll understand monitoring: data drift, model performance, and alerting. We'll explore prompt versioning and management. You'll learn about feedback loops: collecting user feedback, evaluating model outputs, and retraining. We'll cover cost optimization: spot instances, model caching, and request batching. By the end, you'll be able to build robust MLOps pipelines for LLMs."
                },
                {
                    "id": 8,
                    "title": "Performance Optimization & Profiling",
                    "type": "video",
                    "duration": "40 min",
                    "description": "Profile, debug, and optimize model performance for production workloads.",
                    "content": "Optimization requires understanding where time is spent. We'll cover profiling tools: PyTorch profiler, NVIDIA Nsight, and Python profilers. You'll learn to identify bottlenecks: data loading, CPU-GPU transfer, and kernel execution. We'll discuss optimization techniques: operator fusion, memory pinning, and gradient checkpointing. You'll understand inference optimization: batching, caching, and speculative decoding. We'll explore distributed inference: model parallelism and tensor parallelism. You'll learn about benchmarking methodology: warm-up runs, statistical significance, and realistic workloads. By the end, you'll be able to optimize models for maximum throughput or minimum latency."
                }
            ],
            "quiz": {
                "title": "Module 4 Assessment",
                "description": "Test your understanding of model optimization and deployment",
                "time_limit": "15 minutes",
                "passing_score": 80,
                "questions": [
                    {
                        "question": "What is the primary goal of quantization?",
                        "options": ["Increase model size", "Reduce precision to decrease model size", "Improve training speed", "Add more parameters"],
                        "correct": 1,
                        "explanation": "Quantization reduces the numerical precision of model weights (e.g., from FP32 to INT8), decreasing model size and often improving inference speed with minimal accuracy loss."
                    },
                    {
                        "question": "Which technique trains a smaller model to mimic a larger one?",
                        "options": ["Pruning", "Quantization", "Knowledge distillation", "Fine-tuning"],
                        "correct": 2,
                        "explanation": "Knowledge distillation trains a smaller 'student' model to replicate the behavior of a larger 'teacher' model by matching its soft predictions, transferring the teacher's knowledge efficiently."
                    },
                    {
                        "question": "What does FlashAttention optimize?",
                        "options": ["Model accuracy", "Attention computation efficiency", "Training data quality", "Model interpretability"],
                        "correct": 1,
                        "explanation": "FlashAttention reduces memory usage and improves speed by computing attention in a memory-efficient way using tiling and recomputation, avoiding materializing the full attention matrix."
                    },
                    {
                        "question": "Which deployment scenario benefits most from model compression?",
                        "options": ["Cloud servers with unlimited resources", "Edge devices with limited memory", "Development environments", "Research experiments"],
                        "correct": 1,
                        "explanation": "Edge devices have strict memory, compute, and power constraints, making model compression essential for deploying AI models on smartphones, IoT devices, or embedded systems."
                    },
                    {
                        "question": "What is a key benefit of containerization?",
                        "options": ["Improved model accuracy", "Consistent environments across platforms", "Reduced training time", "Automatic hyperparameter tuning"],
                        "correct": 1,
                        "explanation": "Containerization packages models with their dependencies, ensuring consistent behavior across development, testing, and production environments, eliminating 'works on my machine' issues."
                    }
                ]
            },
            "assignment": {
                "title": "Optimize and Deploy an LLM",
                "description": "Take a large language model, apply optimization techniques, and deploy it as a scalable API service.",
                "time_estimate": "10-12 hours",
                "difficulty": "Advanced",
                "tasks": [
                    {
                        "id": 1,
                        "title": "Model Selection and Baseline",
                        "description": "Select an open-source LLM (e.g., Mistral-7B, Llama-2-7B), establish baseline metrics for latency, throughput, and memory usage."
                    },
                    {
                        "id": 2,
                        "title": "Quantization Implementation",
                        "description": "Apply quantization (INT8, INT4) using appropriate tools (bitsandbytes, GPTQ). Measure accuracy degradation on a benchmark dataset."
                    },
                    {
                        "id": 3,
                        "title": "Additional Optimizations",
                        "description": "Implement at least one additional optimization: FlashAttention, KV-cache optimization, or speculative decoding."
                    },
                    {
                        "id": 4,
                        "title": "API Development",
                        "description": "Create a REST API for your optimized model with proper request handling, batching, and error handling."
                    },
                    {
                        "id": 5,
                        "title": "Containerization and Deployment",
                        "description": "Containerize your application with Docker. Deploy locally or to a cloud platform (Hugging Face Spaces, AWS, GCP)."
                    },
                    {
                        "id": 6,
                        "title": "Performance Benchmarking",
                        "description": "Benchmark your deployed model: measure latency (P50, P95, P99), throughput, and resource utilization under various loads."
                    }
                ],
                "deliverables": [
                    "Optimized model with before/after comparison metrics",
                    "Docker container with complete API implementation",
                    "Benchmark report with latency, throughput, and memory metrics",
                    "Cost analysis for different deployment options",
                    "Documentation for API endpoints and usage examples"
                ]
            }
        },
        {
            "id": 5,
            "title": "Real-World AI Applications",
            "subtitle": "Building Impactful AI Products",
            "description": "Bring together everything you've learned to build real-world AI applications. This module focuses on practical considerations: ethics, product design, and building systems that create value.",
            "duration": "5 hours",
            "lessons_count": 8,
            "level": "Advanced",
            "learning_objectives": [
                "Apply AI to solve real-world problems across industries including healthcare, finance, and education",
                "Design AI products with user experience, trust, and safety in mind",
                "Navigate ethical considerations including bias, fairness, and transparency",
                "Build complete AI systems from problem definition to deployment and monitoring",
                "Plan and execute AI projects with appropriate scoping and resource estimation"
            ],
            "lessons": [
                {
                    "id": 1,
                    "title": "AI in Industry: Use Cases and Patterns",
                    "type": "video",
                    "duration": "45 min",
                    "description": "Explore how AI is transforming industries and identify opportunities for AI applications.",
                    "content": "AI is reshaping every industry. We'll explore healthcare: diagnosis assistance, drug discovery, and personalized treatment. You'll learn about finance: fraud detection, algorithmic trading, and credit scoring. We'll discuss education: adaptive learning, automated grading, and intelligent tutoring. You'll understand retail: demand forecasting, recommendation systems, and inventory optimization. We'll cover manufacturing: predictive maintenance, quality control, and supply chain optimization. You'll learn about legal tech: contract analysis, case prediction, and document review. By the end, you'll be able to identify AI opportunities in any industry."
                },
                {
                    "id": 2,
                    "title": "Product Design for AI",
                    "type": "video",
                    "duration": "40 min",
                    "description": "Design AI products that users love, with proper UX patterns for AI interactions.",
                    "content": "AI products require thoughtful design. We'll explore UX patterns for AI: progressive disclosure, confidence indicators, and explanation interfaces. You'll learn about handling uncertainty: graceful degradation, fallback options, and human-in-the-loop. We'll discuss user trust: transparency, consistency, and recovery from errors. You'll understand feedback loops: user corrections, preference learning, and continuous improvement. We'll cover onboarding: setting appropriate expectations and teaching users AI capabilities. By the end, you'll be able to design AI features that enhance rather than frustrate."
                },
                {
                    "id": 3,
                    "title": "AI Ethics and Responsibility",
                    "type": "video",
                    "duration": "50 min",
                    "description": "Navigate the ethical challenges of AI development and implement responsible AI practices.",
                    "content": "AI systems can have significant societal impact. We'll cover bias in AI: sources, measurement, and mitigation strategies. You'll learn about fairness: different definitions (demographic parity, equal opportunity) and trade-offs. We'll discuss transparency and explainability: interpretable models, explainability techniques (SHAP, LIME), and documentation. You'll understand privacy: data protection, differential privacy, and federated learning. We'll explore accountability: governance, auditing, and liability. You'll learn about AI safety: alignment, robustness, and catastrophic risks. By the end, you'll be equipped to build AI responsibly."
                },
                {
                    "id": 4,
                    "title": "Building AI Agents",
                    "type": "video",
                    "duration": "45 min",
                    "description": "Create AI agents that can reason, plan, and use tools to accomplish complex tasks.",
                    "content": "AI agents go beyond simple generation to accomplish multi-step tasks. We'll explore agent architectures: ReAct (reasoning + acting), Plan-and-Solve, and hierarchical planning. You'll learn about tool use: function calling, API integration, and tool selection. We'll discuss memory: short-term context, long-term storage, and retrieval. You'll understand multi-agent systems: collaboration, competition, and emergent behavior. We'll cover agent evaluation: task completion, efficiency, and safety. You'll learn about frameworks: LangChain agents, AutoGPT, and BabyAGI. By the end, you'll be able to build agents that accomplish complex goals."
                },
                {
                    "id": 5,
                    "title": "End-to-End AI System Design",
                    "type": "video",
                    "duration": "50 min",
                    "description": "Design complete AI systems from data pipeline to user interface.",
                    "content": "Real AI systems are more than models. We'll explore system architecture: data ingestion, feature stores, model serving, and monitoring. You'll learn about data pipelines: collection, cleaning, labeling, and versioning. We'll discuss feature engineering: feature stores, online serving, and feature freshness. You'll understand model lifecycle: training, validation, deployment, and retirement. We'll cover reliability: fallbacks, circuit breakers, and graceful degradation. You'll learn about scalability: horizontal scaling, caching, and load balancing. By the end, you'll be able to architect complete AI systems."
                },
                {
                    "id": 6,
                    "title": "Project Planning and Estimation",
                    "type": "video",
                    "duration": "35 min",
                    "description": "Plan AI projects effectively with realistic scoping and resource estimation.",
                    "content": "AI projects often exceed estimates due to inherent uncertainty. We'll cover project phases: discovery, prototyping, production, and iteration. You'll learn about scoping: MVP definition, success criteria, and risk assessment. We'll discuss estimation techniques: story points, three-point estimation, and Monte Carlo simulation. You'll understand resource planning: compute needs, data requirements, and team skills. We'll explore risk management: technical risks, data risks, and stakeholder risks. You'll learn about stakeholder communication: setting expectations, reporting progress, and managing changes. By the end, you'll be able to plan AI projects that deliver on time."
                },
                {
                    "id": 7,
                    "title": "Monitoring and Maintenance",
                    "type": "video",
                    "duration": "40 min",
                    "description": "Implement monitoring, alerting, and maintenance strategies for production AI systems.",
                    "content": "Production AI systems require ongoing attention. We'll cover monitoring metrics: model performance, data quality, and system health. You'll learn about drift detection: data drift, concept drift, and prediction drift. We'll discuss alerting: thresholds, escalation, and on-call practices. You'll understand retraining strategies: scheduled, triggered, and continuous. We'll explore A/B testing: experiment design, statistical significance, and rollout strategies. You'll learn about incident response: detection, diagnosis, mitigation, and postmortems. By the end, you'll be able to maintain AI systems that continue to perform over time."
                },
                {
                    "id": 8,
                    "title": "Career Paths in AI",
                    "type": "reading",
                    "duration": "30 min",
                    "description": "Explore career opportunities in AI and chart your path forward.",
                    "content": "The AI field offers diverse career paths. We'll explore roles: ML Engineer, Data Scientist, AI Researcher, ML Infrastructure Engineer, and AI Product Manager. You'll learn about skill requirements: technical skills, soft skills, and domain expertise. We'll discuss career progression: individual contributor vs. management, and specialization vs. breadth. You'll understand the landscape: big tech, startups, research labs, and consulting. We'll cover portfolio building: projects, publications, and open source contributions. You'll learn about networking: conferences, communities, and mentorship. By the end, you'll have a roadmap for your AI career."
                }
            ],
            "quiz": {
                "title": "Module 5 Assessment",
                "description": "Test your understanding of real-world AI applications",
                "time_limit": "15 minutes",
                "passing_score": 80,
                "questions": [
                    {
                        "question": "Which is NOT a typical challenge in Gen AI product development?",
                        "options": ["Controlling output quality", "High computational costs", "Perfect reproducibility of outputs", "Handling inappropriate content"],
                        "correct": 2,
                        "explanation": "Gen AI outputs are inherently stochastic - achieving perfect reproducibility is generally not possible or expected. The other options are genuine challenges that need to be addressed."
                    },
                    {
                        "question": "What is key when designing UX for AI?",
                        "options": ["Maximizing model parameters", "Providing controls and guardrails", "Minimizing user interaction", "Avoiding user feedback"],
                        "correct": 1,
                        "explanation": "AI products should give users appropriate controls, set expectations, provide explanations, and have guardrails to handle errors gracefully, building trust and improving user experience."
                    },
                    {
                        "question": "Which industry has seen significant disruption from Gen AI?",
                        "options": ["Manufacturing only", "Healthcare, finance, marketing, and more", "No industries affected", "Only technology sector"],
                        "correct": 1,
                        "explanation": "Gen AI is transforming multiple industries simultaneously: healthcare (diagnosis, drug discovery), finance (analysis, fraud detection), marketing (content, personalization), and many others."
                    },
                    {
                        "question": "What is a common approach to building AI agents?",
                        "options": ["Training from scratch always", "Combining LLMs with tools and memory", "Using only rule-based systems", "Avoiding external tools"],
                        "correct": 1,
                        "explanation": "Modern AI agents combine LLMs for reasoning with tools for action (APIs, databases) and memory for context, enabling them to accomplish complex multi-step tasks."
                    },
                    {
                        "question": "What ethical concern is particularly relevant for Gen AI?",
                        "options": ["Hardware compatibility", "Generating misleading or harmful content", "Programming language efficiency", "Network connectivity issues"],
                        "correct": 1,
                        "explanation": "Gen AI can generate plausible-sounding but false information (hallucinations) or harmful content, making content safety and truthfulness critical ethical concerns."
                    }
                ]
            },
            "assignment": {
                "title": "Build an End-to-End AI Product",
                "description": "Design, implement, and deploy a complete AI application that solves a real problem, including user interface, API, and monitoring.",
                "time_estimate": "12-15 hours",
                "difficulty": "Advanced",
                "tasks": [
                    {
                        "id": 1,
                        "title": "Problem Definition",
                        "description": "Identify a real-world problem that can be addressed with AI. Document the problem, target users, success criteria, and ethical considerations."
                    },
                    {
                        "id": 2,
                        "title": "System Design",
                        "description": "Design the complete system architecture: data flow, model selection, API design, and user interface. Create diagrams and documentation."
                    },
                    {
                        "id": 3,
                        "title": "Implementation",
                        "description": "Build the core functionality: data pipeline, model integration, API endpoints, and basic UI. Focus on a minimal viable product."
                    },
                    {
                        "id": 4,
                        "title": "Safety and Ethics Implementation",
                        "description": "Implement at least one safety measure: content filtering, bias detection, or user controls. Document your approach."
                    },
                    {
                        "id": 5,
                        "title": "Deployment and Documentation",
                        "description": "Deploy your application (local, cloud, or Hugging Face Spaces). Create user documentation and API documentation."
                    },
                    {
                        "id": 6,
                        "title": "Demo and Reflection",
                        "description": "Create a demo video showing your application in action. Write a reflection on challenges, tradeoffs, and future improvements."
                    }
                ],
                "deliverables": [
                    "Problem statement and requirements document",
                    "System architecture diagram with component descriptions",
                    "Working application with code and documentation",
                    "User guide with screenshots and examples",
                    "Ethical considerations document addressing potential harms",
                    "Demo video (3-5 minutes)",
                    "Reflection document (500 words)"
                ]
            }
        }
    ],
    "capstone": {
        "title": "Domain-Specific RAG Chatbot",
        "subtitle": "Your Culminating Project",
        "description": "Build a production-ready, domain-specific retrieval-augmented generation chatbot that demonstrates mastery of all course concepts. This project integrates knowledge from all five modules and showcases your ability to build real-world AI systems.",
        "duration": "20-25 hours",
        "difficulty": "Advanced",
        "requirements": [
            {
                "id": 1,
                "title": "Domain Selection and Data Collection",
                "description": "Choose a specific domain (legal, medical, technical documentation, educational content, etc.) and collect a comprehensive knowledge base of relevant documents.",
                "deliverables": [
                    "Domain justification document",
                    "List of sources and documents",
                    "Data quality assessment report"
                ]
            },
            {
                "id": 2,
                "title": "RAG System Implementation",
                "description": "Implement a complete RAG pipeline including document processing, embedding generation, vector storage, retrieval, and generation with an LLM.",
                "deliverables": [
                    "Document processing pipeline",
                    "Embedding and vector store setup",
                    "Retrieval and generation system",
                    "Conversation memory implementation"
                ]
            },
            {
                "id": 3,
                "title": "Optimization and Evaluation",
                "description": "Apply at least two optimization techniques and implement a comprehensive evaluation framework.",
                "deliverables": [
                    "Optimization implementation (quantization, caching, etc.)",
                    "Evaluation dataset (20+ test queries)",
                    "Performance benchmarks",
                    "Ablation study results"
                ]
            },
            {
                "id": 4,
                "title": "Production Deployment",
                "description": "Deploy your chatbot with a user-friendly interface, monitoring, and proper error handling.",
                "deliverables": [
                    "Deployed application (cloud or local)",
                    "User interface (web or API)",
                    "Monitoring and logging setup",
                    "Error handling and fallbacks"
                ]
            },
            {
                "id": 5,
                "title": "Documentation and Presentation",
                "description": "Create comprehensive documentation and present your project.",
                "deliverables": [
                    "Technical documentation",
                    "API documentation",
                    "User guide",
                    "Demo video (5-7 minutes)",
                    "Presentation slides"
                ]
            }
        ],
        "evaluation_criteria": {
            "functionality": "25% - System works correctly and handles edge cases",
            "technical_implementation": "25% - Proper use of techniques from all modules",
            "optimization": "15% - Effective optimization with documented improvements",
            "user_experience": "15% - Intuitive interface and helpful responses",
            "documentation": "10% - Clear, comprehensive documentation",
            "presentation": "10% - Professional demo and explanation"
        }
    }
}