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Logistic Regression Implementation in Python
Problem statement: The aim is to make predictions on the survival outcome of passengers.
Since this is a binary classification, logistic regression can be used to build the model.
Dataset source: https://www.kaggle.com/c/titanic/data
#Importing the libraries import panda... | ['Logistic Regression', 'Supervised Learning', 'Machine Learning', 'Harshita Yadav', 'Titanic Dataset'] |
Explain Supervised vs. Unsupervised Learning in Machine Learning to a 5-year-old
Machine Learning
With the help of Machine learning, a system can make decision which can be relatable to the decisions that humans make. Machine learning has the ability to learn from the data it inputs. For now, Machine learning is tryi... | ['Basics', 'Data Science', 'Supervised Learning', 'Machine Learning', 'Unsupervised Learning'] |
Background — weakly supervised learning
Deep learning has several advantages over traditional machine learning methods when it comes to performing supervised learning tasks:
i. Whereas traditional machine learning techniques rely on feature extraction by domain experts, deep learning algorithms learn high-level featu... | ['Supervised Learning', 'Machine Learning', 'Weakly Supervised', 'Deep Learning'] |
Multiple Linear Regression Implementation using Python
Problem statement: Build a Multiple Linear Regression Model to predict sales based on the money spent on TV, Radio, and Newspaper for advertising.
Importing the Libraries
#Importing the libraries import pandas as pd
import numpy as np
import matplotlib.pyplot ... | ['Multiple Linearregression', 'Sales Prediction', 'Supervised Learning', 'Machine Learning', 'Harshita Yadav'] |
Types of Linear Regression
Simple Linear Regression
Simple Linear Regression helps to find the linear relationship between two continuous variables. It uses one independent variable to predict a dependent variable by fitting a best linear relationship.
It has only one independent variable (X) and one dependent varia... | ['Sales Prediction', 'Supervised Learning', 'Machine Learning', 'Harshita Yadav', 'Simple Linear Regression'] |
If a few of my neighbors leave the telecom service provider, should I leave it too? No, not really. That’s not what I meant.
When choosing a telecommunication service provider, customers usually have many choices. They can choose any service provider and may move away from the current provider. When a customer chooses... | ['Knn Classification', 'Customer Churn', 'Knn', 'Supervised Learning', 'Machine Learning'] |
Introduction to the problem :-
In this blog, we would work with one of the popular data-set i.e. of LendingClub. Its a US peer-to-peer lending company, headquartered in San Francisco, California. It was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission (S... | ['Keras', 'Supervised Learning', 'Machine Learning', 'Artificial Neural Network', 'Exploratory Data Analysis'] |
Most production machine learning applications today are based on supervised learning. In this setup, a machine learning model trains on a set of labeled training data in order to learn how to do a certain task. For example, an ML model may train on images that are labeled with the type of pet in that image so that the ... | ['Data', 'Deep Learning', 'Labeling', 'Supervised Learning', 'Machine Learning'] |
A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
Decision tree is a very simple and powerful tool in... | ['Data Science', 'Supervised Learning', 'Machine Learning', 'Decision Tree', 'Python'] |
Taken from http://www.thebluediamondgallery.com/wooden-tile/t/transfer.html Nick Youngson
Abstract
In this blog, I’m going to talk about how I have gotten an accuracy greater than 88% (92% epoch 22) with Cifar-10 using transfer learning, I used VGG16 and I applied a very low constant learning rate and I implemented t... | ['Supervised Learning', 'Learning Rate', 'Transfer Learning'] |
1. How would you define Machine Learning?
Machine Learning is the science (and art) of programming computers so they can learn from data.
2. Can you name four types of problems where it shines?
• Problems for which existing solutions require a lot of hand-tuning or long lists of rules: one Machine Learning algorithm... | ['Machine Learning Systems', 'Supervised Learning', 'Machine Learning', 'Unsupervised Learning', 'Algorithms'] |
2. Build a classification tree 🔨
If you enjoy math, I encourage you to manually calculate alongside this guide to make most of this blog. In this section, those characters who are parents are abbreviated as pa and the non-parents are abbreviated as np for brevity.
2.1. Using gini impurity (a.k.a gini index or gini c... | ['Data Science', 'Supervised Learning', 'Machine Learning', 'Decision Tree', 'Classification'] |
Visualizing the histogram plots, I identified the pattern of each of the variables. As we can see, there is a right skewness in most of the distributions. However, the variables “density” and “pH” show that they follow a normal distribution. Also, I can mention that the variables “residual sugar” and “chlorides” have a... | ['Towards Data Science', 'Data Science', 'Supervised Learning', 'Machine Learning', 'R'] |
Before moving into each of these areas, let’s clarify a few concepts around the Machine Learning Process. If you are familiar with how Machine Learning works, you can skip this section.
A high-level definition of Machine Learning can be seen as:
Given some data representative of an area (sales, politics, education) y... | ['Artificial Intelligence', 'Reinforcement Learning', 'Supervised Learning', 'Machine Learning', 'Unsupervised Learning'] |
I ntroduction
Naïve Bayes algorithm is a supervised classification algorithm based on Bayes theorem with strong(Naïve) independence among features.
Bayes’ Theorem
In probability theory and statistics, Bayes’ theorem describes the probability of an event, based on prior knowledge of conditions that might be related t... | ['Supervised Learning', 'Machine Learning', 'Naive Bayes', 'Classification Algorithms', 'Naive Bayes Classifier'] |
Collect and Know the Data
We will be using the already collected Abalone dataset to see the algorithms in action. The first step in knowing the data is to know what it contains. This means understanding the type (continuous numeric, discrete numeric or categorical) and meaning of each feature and noting down the numbe... | ['Supervised Learning', 'Machine Learning', 'Artificial Intelligence', 'Pandas'] |
This article will be an introduction to supervised learning, the algorithms will be discussed briefly as they will be seen in details in the next lectures. The goal is to have a clear idea about what we will be dealing with when we are solving a problem that needs a supervised learning algorithm.
Let us keep in mind t... | ['Supervised Learning', 'Machine Learning', 'Classification', 'Regression'] |
About Machine Learning, I want to introduce some important concepts into a series of posts that will help us understand so much.
It all started when researchers intersted in artificial intelligence wondered if machines could think or learn like humans.
Machine Learning is a branch of Artificial Intelligence based on ... | ['Supervised Learning', 'Machine Learning', 'Unsupervised Learning', 'Data Science'] |
This Article guides to approach the data set and basic mathematical relationship with Machine Learning Algorithms.
The content of this article is a result set of multiple online,books,workshops references. Any critics or comments, please feel free to reach out. This article has been trimmed to contain only the necessa... | ['Regression', 'Supervised Learning', 'Machine Learning', 'Unsupervised Learning', 'Classification'] |
This article talks about the basic classification in machine learning or specifically in supervised learning. How a classifier builds the boundary to separate the two classes. It also talks about how binary classification is important and mathematics behind the supervised learning classification task.
We will try to u... | ['Supervised Learning', 'Machine Learning', 'Classification', 'Generalization'] |
image courtesy: shutterstock.com
Hello fellow learners and readers,
I have planned to publish a series of episodes on Machine Learning.
As myself being a ML enthusiast and a practitioner of Data Science , I would like to share some of my takeaways from what I’ve gathered.
Readers are always welcome for correcting a... | ['Artificial Intelligence', 'Datasciencecourse', 'Machine Learning', 'Supervised Learning', 'Unsupervised Learning'] |
Cyber Security Defensive Mechanisms built on Supervised Machine Learning Algorithms heavily rely on old historical logs of recent Cyber Attacks in order to train and tune detection models. The accuracy and relevance of such historical attack dataset facilitates in the effective detection of malwares or recognizing C&C ... | ['Cybersecurity', 'Dns Tunneling', 'Supervised Learning', 'Machine Learning', 'Splunk'] |
A chatbot is an AI solution that simulates a conversation with an end-user in natural language over any messaging app, the phone or web.
Why are ChatBots important?
A chatbot is a most promising platform for the interaction between humans and machines. AI chatbot can handle obscure questions, this means you don’t hav... | ['Chatbots', 'Artificial Intelligence', 'Semi Supervised Learning', 'FAQ', 'Supervised Learning'] |
Project Design
The problem that I am trying to solve is to predict/classify whether a person has heart disease or not, based on some of the personal info and medical test results.
That is my primary goal of my project, and the second goal of my project is to build a heart disease predictor — a demo app using Flask.
... | ['Healthcare Technology', 'Metis', 'Data Science', 'Supervised Learning', 'Machine Learning'] |
Supervised Machine Learning: Model Validation, a Step by Step Approach
Model validation is the process of evaluating a trained model on test data set. This provides the generalization ability of a trained model. Here I provide a step by step approach to complete first iteration of model validation in minutes. Shreyas ... | ['Supervised Learning', 'Machine Learning', 'Model Validation', 'Gridsearchcv'] |
We can either drop columns with high cardinality and NaN values if needed. Also for numerical features, we can fill the NaN values with its mean, median or mode instead of dropping it. In the same way for categorical features, we can categorize the NaN as a separate category.
Also based on the water table data set, we... | ['Supervised Learning', 'Machine Learning', 'Feature Engineering', 'Hyperparameter Tuning'] |
update : We have introduced an interactive learning App for machine learning / AI ,>> Check it out for Free now <<
Import the required libraries
import numpy as np
import pandas as pd
eps = np.finfo(float).eps
from numpy import log2 as log
‘eps’ here is the smallest representable number. At times we get log(0) or... | ['Artificial Intelligence', 'Supervised Learning', 'Machine Learning', 'Classification', 'Python'] |
update : We have introduced an interactive learning platform to learn machine learning / AI , check out this blog in interactive mode.
It’s a classification algorithm, that is used where the response variable is categorical. The idea of Logistic Regression is to find a relationship between features and probability of ... | ['Logistic Regression', 'Numpy', 'Supervised Learning', 'Machine Learning', 'Classification'] |
Motivation
For a long time I find myself stuck in a strange loop: I studied algorithms and spent long time on model details. I was confident that I understood the model. And after a month or two I pretty much forgot everything about the model. And I reviewed the model again and forgot it again.
This situation is most... | ['Supervised Learning', 'Machine Learning', 'Svm', 'Data Science'] |
Linear Regression With One Variable
Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).
In this paper, we will fit the ... | ['Data Science', 'Supervised Learning', 'Machine Learning', 'Regression Analysis', 'Data Analysis'] |
While supervised and unsupervised machine learning is a much more widespread practice among enterprises today, reinforcement learning (RL), as a goal-oriented ML technique, finds its application in mundane real-world activities. Gameplay, robotics, dialogue systems, autonomous vehicles, personalization, industrial auto... | ['Artificial Intelligence', 'Reinforcement Learning', 'Supervised Learning', 'Machine Learning', 'Unsupervised Learning'] |
Let’s check your basic knowledge of Decision Tree. Here are 10 multiple-choice questions for you and there’s no time limit. Have fun!
Figure: Decision tree [1]
Question 1: Decision trees are also known as CART. What is CART?
(A) Classification and Regression Trees
(B) Customer Analysis and Research Tool
(C) Commun... | ['Artificial Intelligence', 'Data Science', 'Supervised Learning', 'Machine Learning', 'Decision Tree'] |
Welcome to the 3rd article of “A Journey through XGBoost” series. Up to now, we have completed 2 milestones. What we have done so far is that, basically, we discussed how to set up the system to run XGBoost on our own computers, and also we did a classification task with XGBoost and created a small (but useful) web app... | ['Regression', 'Xgboost', 'Data Science', 'Supervised Learning', 'Machine Learning'] |
Introduction
K-nearest neighbors (KNN) is a type of supervised learning algorithm used for both regression and classification. KNN tries to predict the correct class for the test data by calculating the distance between the test data and all the training points. Then select the K number of points which is closet to th... | ['Knn Algorithm', 'Data Science', 'Supervised Learning', 'Machine Learning', 'Classification'] |
Side note, while I have put Neural Networks in a category of their own within the supervised learning branch, they can be used to solve a wide range of problems including classification and regression. The above chart is interactive so try clicking on different categories 👆.
How does the Random Forest classification ... | ['Random Forest Classifiers', 'Data Science', 'Supervised Learning', 'Machine Learning', 'Python'] |
Do you remember, as a kid the way your parents would guide you when you were learning to identify dogs and cats? When you got it wrong or identified a dog as a horse, they would tell you that you are wrong and help you learn better. They would repeatedly ask and correct you every time you answered it wrong until you go... | ['Supervised Learning', 'Machine Learning', 'Ml Unravelled'] |
Supervised, Unsupervised & Reinforcement….
Hello world, i hope you are all doing well. In this post we will learn about Machine learning concepts.
As many of us know Machine Learning(ML) is subset of Artificial Intelligence(AI). The objective of ML is to enable machines to learn by themselves using the provided data ... | ['Supervised Learning', 'Machine Learning', 'Unsupervised Learning', 'Reinforcement Learning'] |
Supervised learning is a method of understanding of function which has input(features) and output(label/target). In mathematics, we usually call them as x(dependent variable)and y(independent variable).
According to the characteristic of target variable, we can classify the data science problem as a regression or clas... | ['Supervised Learning', 'Machine Learning', 'Classification', 'Regression'] |
This post is going to be a little more technical than the last so lets put those old thinking caps on.
Right off the bat, we need to make a distinction. While most data nerds consider decision trees as a strict classification tool, there is also a regression variant. Even though regression trees split data at “nodes” ... | ['Data Science', 'Supervised Learning', 'Machine Learning', 'Decision Tree', 'Sklearn'] |
Basics of machine learning
Machine learning: A computer program that learn from its experience E, for a task T and gives performance P.if its performance at task in T as measured by P ,improves with experience E
Types of machine learning:
Supervised machine learning Unsupervised machine learning Reinforcement Machin... | ['Reinforcement Learning', 'Data Science', 'Supervised Learning', 'Machine Learning', 'Unsupervised Learning'] |
Introduction
Arvato offers financial solutions offers a wide range of services from Id & Fraud Management to payment financial services. The company is looking to use its available dataset to analyze demographics data for customers of a mail-order sales company in Germany, comparing it against demographics information... | ['Logistic Regression', 'K Means', 'Supervised Learning', 'Machine Learning', 'Unsupervised Learning'] |
It is mind-blowing and fascinating how every part of our daily lives is being affected by machine learning-driven applications and most of us go by our mundane life routines oblivious to this. Yet, to the average non-computer science or tech guys, at the mention of machine learning and deep learning in a conversation, ... | ['Artificial Intelligence', 'Machine Intelligence', 'Deep Learning', 'Supervised Learning', 'Machine Learning'] |
Photo by Kevin Ku
We’re taught to ride the bicycle, a few lanes around the house. We’re taught to solve a few examples of the math. The next similar numerical you encounter is something you learn training yourself over the previous ones. Hello?
I believe nothing is complex. I’ve often been intimidated by these buzzwo... | ['Artificial Intelligence', 'Introduction To Ml', 'Supervised Learning', 'Machine Learning', 'Introduction To Ai'] |
“Learning is any process by which a system improves performance from experience.”
“Machine Learning is concerned with computer programs that automatically improve their performance through experience” — Herbert Alexander Simon Alan Turing rightly questioned in his paper “Can machines do what we (as thinking entities) ... | ['Libraries', 'Python Programming', 'Supervised Learning', 'Machine Learning', 'Algorithms'] |
Supervised Learning :
Using labeled datasets to train algorithm in order to classify data or predict outcomes.
Output Y — Also called outcome, response, target, dependent variable
— Also called outcome, response, target, dependent variable Vector of inputs X — Also called as predictor, covariates, independent variab... | ['Predictor', 'Supervised Learning', 'Unsupervised Learning', 'Classification', 'Outcomes'] |
Performance measures for Supverised classification problem.
To find a solution for our problem based on actual data and the predicted data which helps you to choose the right metric based on ur problem of the data.
Before choosing the right metric for our problem we need to know few terminologies of the measures of t... | ['Supervised Learning', 'Machine Learning', 'Performance Metrics', 'Precision', 'Accuracy And Precision'] |
K Nearest Neighbor algorithm is one of the easy-to-implement supervised learning algorithms. It can be used to solve both classification and regression problems. KNN algorithms were proposed by T. M. Cover and P. E. Hart in 1967. The algorithm is used by making use of data from a sample set whose classes are known. The... | ['K Nearest Neighbours', 'Machine Learning', 'Supervised Learning', 'AI'] |
Machine Learning is the new technological wave whose impact on industries and the world can be compared to the emergence of PCs in the 1970s. While the first work now recognized as AI was done in 1943, it wasn’t until the 2010s that AI became widely used in consumer tech products and industries.
There are three types ... | ['AI', 'Artificial Intelligence', 'Supervised Learning', 'Machine Learning', 'Data Labeling'] |
Introduction
What is machine Learning ? This is the first question which come up when we dive into the world of data science. Machine Learning is basically making our machine learn from the data without actually explicitly providing a code or algorithm.
So, the next question which come up is what is supervised machin... | ['Linear Regression', 'Machine Learning', 'Data Science', 'Supervised Learning'] |
Researchers from Zhejiang University have proposed a new method combining Monte Carlo tree search with Neural Fictitious Self-Play (NFSP) to improve performance on large-scale zero-sum imperfect-information games. Facing the incomplete information environment, the asynchronous neural virtual self-play (ANFSP) method al... | ['Artificial Intelligence', 'Gaming', 'Deepmind', 'Supervised Learning', 'Technology'] |
Large labeled datasets are critical for developing machine learning applications and better training the modern machine learning models in the AI field. Creating these datasets however is a process that requires considerable time and money. A new Google AI paper published in collaboration with Stanford and Brown Univer... | ['Artificial Intelligence', 'Supervised Learning', 'Machine Learning', 'Google', 'Technology'] |
Using the one-hot encoding
This is one of the interesting parts in solving the problem. Let me start by answering the question why do we use one-hot encoding? the answer is
Typically, learning algorithms expect input to be numeric, Generally learning algorithms expect the input variables to be in numeric form. So we ... | ['Supervised Learning', 'Machine Learning', 'Data Science'] |
Introduction
In this project, demographics data for customers of a mail-order sales company in Germany, was analyzed and compared against demographics information for the general population. The data was provided Bertelsmann Arvato Analytics.
Customer segmentation was used to identify the parts of the population that... | ['Customer Segmentation', 'Supervised Learning', 'Machine Learning', 'Marketing Analysis', 'Unsupervised Learning'] |
In order to test my knowledge of supervised machine learning, I was assigned a project to create a classification model. For this project, I was tasked with choosing my own dataset, one with sufficient complexity, a minimum of 1,000 rows and a minimum of 10 predictor columns. With this dataset, I would ask a question f... | ['Xgboost', 'Supervised Learning', 'Classification Models', 'Confusion Matrix', 'Feature Importance'] |
Predicting Visitor-to-Customer Conversion for an Online Store via Supervised Machine Learning- Part 1: Introduction to Business Case and Exploratory Data Analysis Bharat Sethuraman Sharman Oct 27, 2020·10 min read
In this two part series, I will write about my experience working on a Kaggle Data Challenge (Here’s the ... | ['Predictive Analytics', 'Data Science', 'Supervised Learning', 'Machine Learning', 'Digital Marketing'] |
1. Prepare data 📦
If you want to follow along with the code on your computer, make sure you have numpy, pandas, seaborn, sklearn and xgboost installed.
Let’s imagine we want to find a suitable machine learning algorithm for a classification problem. For our example, we will use a subset of features from titanic data... | ['Getting Started', 'Data Science', 'Supervised Learning', 'Machine Learning', 'Python'] |
A (basic) project example
When I build my projects I like to automate as much as possible. That is, I try to repeat myself as little as possible and I like to change things like models and hyperparameters with as little code as I can. Let's look to build a very simple model to classify the MNIST dataset. For those of ... | ['Towards Data Science', 'Data Science', 'Supervised Learning', 'Python', 'Tutorial'] |
2. Result and theory
So based on the result, you can see BayesSearch provides the best result, even it only needs 60 iterations, while GridSearch and RandomizedSearch are searching for 120 iterations, but BayesSearch needs much more time.
So, what can we learn from this simple experiment?
RandomizedSearch defeats Gr... | ['Data Science', 'Supervised Learning', 'Machine Learning', 'Optimization Algorithms', 'Bayesian Machine Learning'] |
Deep Learning explained in single sentences!
Supervised Learning
Supervised Learning is used to train a model when we have a set of feature X and Target Y. It is also used when we are asked to predict target outputs for new feature x_test.
There are two types of supervised learning problems: classification and regre... | ['Deep Learning', 'Supervised Learning', 'Machine Learning', 'Glossary', 'Neural Networks'] |
Machine Learning Part_2
Machine Learning can be divided into 3 categorizations: Supervised, Unsupervised and Reinforcement Learning.
Let see the difference between Supervised, Unsupervised and Reinforcement in simply 😊.
Supervised Machine Learning
The below table shows a data set which is consist with rows of data... | ['Learning', 'Reinforcement Learning', 'Supervised Learning', 'Unsupervised Learning', 'Machıne'] |
Machine learning allows the computers learn automatically without human intervention or assistance and adjust actions accordingly. Let’s think about a newly born baby. That baby learn the things around him day by day. Then perform the actions based on past experiences.
Main thing we can identified in Machine learning ... | ['Regression', 'Supervised Learning', 'Machine Learning', 'Unsupervised Learning', 'Classifier'] |
Regression algorithms are a subset of Supervised Machine Learning technique which are used to predict output values based on the input labeled data.
Opposite to classification technique which is used to make predictions where the output variable is a category (yes/no, spam/not spam), regression models are used to pred... | ['Supervised Learning', 'Machine Learning', 'Regression'] |
Blog-1 of Machine Learning Series.
Introduction to Machine Learning
Introduction
In this series of blogs, we will be learning about Machine learning and its applications. Don’t worry if you are not good at mathematics or programming, We will learn things as we go.
What is Machine Learning in Layman’s term?
Imagine... | ['Artificial Intelligence', 'Computer Science', 'Supervised Learning', 'Machine Learning', 'Unsupervised Learning'] |
Machine Learning is a subfield of Artificial Intelligence. The main idea behind machine learning is to make the machines to learn by itself from data and use the learnt knowledge to predict and determine an unknown state. As you can see from the diagram below, machine learning is a combination of computer science and m... | ['Supervised Learning', 'Machine Learning', 'Unsupervised Learning', 'Introduction'] |
Machine Learning is a powerful technology having the potential to find practical solutions to problems in diverse fields. In machine learning, there are many techniques used to train the data and get the output.
Supervised Learning
In Supervised Learning, an algorithm learns from a dataset. We give the data as well a... | ['Semi Supervised Learning', 'Reinforcement Learning', 'Supervised Learning', 'Machine Learning', 'Unsupervised Learning'] |
Tackling Bias-Variance Problems | Towards AI
In statistics and machine learning, the bias-variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples and vice versa. The bias-variance dilemma o... | ['Supervised Learning', 'Machine Learning', 'Linear Regression', 'Bias Variance Tradeoff', 'Python'] |
Rep. Alexandria Ocasio-Cortez’s ‘@AOC’ Twitter account is a flashpoint for political discussions. The experienced internet surfer warns ‘don’t read the comments,’ but I’ve spent much time there, reading, baffled, fortunately only once baited into replying (someone said she could only get votes in Brooklyn).
On politic... | ['Naturallanguageprocessing', 'Dataset', 'Supervised Learning', 'Machine Learning', 'Unsupervised Learning'] |
In this blog we will be looking into how to handle text data with Naive Bayes algorithm, its pros and cons. We will also look into Bernouli Naive Bayes algorithm.
How to handle text data?
Spam vs Ham
Naive Bayes algorithm can be used for solving problems like detecting if an email is spam/not spam, classifying revie... | ['Supervised Learning', 'Machine Learning', 'Text Classification', 'Naive Bayes'] |
PC:Packtpub
SVM is a simple classification algorithm every machine learning practitioner should have in their toolbox. Let’s first understand how it works then see its pros and cons.
As said earlier it is a classification algorithm used in supervised learning when we have categorical data. SVM takes the labeled data ... | ['Artificial Intelligence', 'Supervised Learning', 'Machine Learning', 'Classification', 'Svm'] |
Photo by Kevin Gent on Unsplash
Probing the Self for Fun and Profit
Self-Supervision is in the air (and the talks). Explaining the difference between self-, un-, weakly-, semi-, distantly-, and fully-supervised learning (and of course, RL) just got exponentially tougher. :) Nevertheless, we are gonna try.
The proble... | ['Deep Learning', 'Supervised Learning', 'Machine Learning', 'Data Augmentation', 'Few Shot Learning'] |
Many of us would come across the name Random Forest while reading about machine learning techniques. It is one of the most popular machine learning algorithms that uses an ensemble technique-bagging. In this blog we are going to discuss about what are ensemble methods, what is bagging, how bagging is beneficial, what i... | ['Ensemble Learning', 'Data Science', 'Supervised Learning', 'Machine Learning', 'Random Forest'] |
human-learn: Create a Human Learning Model by Drawing
GIF by Author
Nowadays, data scientists often give machine learning model data with labels so that it can figure out the rules. These rules can be used to predict the labels of new data.
Image by Author
This is convenient, but some information may be lost in thi... | ['Data Visualization', 'Semi Supervised Learning', 'Data Science', 'Supervised Learning', 'Machine Learning'] |
Introduction to Classification Using K Nearest Neighbours
As machine learning practitioners, we come across a wide array of machine learning algorithms that we may exert to build a particular predictive model. In this article, I will be focusing on one of the most sophisticated learning algorithm known as K Nearest Ne... | ['Knn', 'Towards Data Science', 'Supervised Learning', 'Machine Learning', 'Classification'] |
THE NAIVE BAYES GUIDE
How to Improve Naive Bayes?
Reference How to Implement Naive Bayes? Section 2: Building the Model in Python, prior to continuing…
[10] Define Grid Search Parameters
param_grid_nb = {
'var_smoothing': np.logspace(0,-9, num=100)
}
var_smoothing is a stability calculation to widen (or smooth) ... | ['Data Science', 'Supervised Learning', 'Machine Learning', 'Naive Bayes', 'Classification Algorithms'] |
THE NAIVE BAYES GUIDE
How to Implement Naive Bayes?
Reference What is Naive Bayes? Section 1: Defining the Model, prior to continuing…
[1] Import Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
NumPy is a Python library used for working with arrays.
is a Python library used for ... | ['Data Science', 'Supervised Learning', 'Machine Learning', 'Naive Bayes', 'Classification Algorithms'] |
Our next segment involves Machine Learning with Anomaly Detection. There are 3 main approaches to detect anomalies.
Determination of outliers without previous data information (anomalies). This is analogous to unsupervised clustering. Supervised machine learning issue with usable labels for both regular and anomalous ... | ['Anomaly Detection', 'Data Science', 'Supervised Learning', 'Machine Learning', 'Outliers'] |
Part 3 — Supervised Learning
After performing cluster analysis on the German demographics and customer data sets to determine which customers are more likely to be customers of the mail-order company, the next stage is to apply supervised learning to a separate data file. This data set is like the previous two, but ev... | ['Udacity', 'Oversampling', 'Data Science', 'Supervised Learning', 'Unsupervised Learning'] |
In this blogpost we will look at three popular metrics (accuracy, recall and sensitivity) and three examples. For simplicity, we limit the models to predict one of two classes (binary classification problems).
The results of the model on the test data are represented as a confusion matrix. In a confusion matrix, the c... | ['Metrics', 'Beginner', 'Supervised Learning', 'Machine Learning', 'Classification'] |
Supervised Learning: Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled and classified data.
In supervised learning, we are given the data sets and already know what our correct output should loo... | ['Artificial Intelligence', 'Supervised Learning', 'Machine Learning', 'Algorithms', 'Python'] |
Linear Regression
Linear Regression is a famous supervised learning algorithm used to predict a real-valued output. The linear regression model is a linear combination of the features of the input examples.
A note on the notation. x_{i} means x subscript i and x_{^th} means x superscript th.
Representation of the Da... | ['Linear Regression', 'Machine Learning', 'Supervised Learning'] |
In Machine Learning, there are two main ways you could train your model depending on the type of data you are provided with:
Supervised Learning: you are provided with a set of inputs and outputs, hence your data are already labeled and you know the ‘ground truth’ behind them. It means that, once your algorithm is tra... | ['Reinforcement Learning', 'Data Science', 'Supervised Learning', 'Machine Learning', 'Unsupervised Learning'] |
Logistic Regression
Contrary to its name logistic regression is a classification algorithm. Given an input example, a logistic regression model assigns the example to a relevant class.
A note on the notation. x_{i} means x subscript i and x_{^th} means x superscript th.
Quick Review of Linear Regression
Linear Regr... | ['Supervised Learning', 'Machine Learning', 'Logistic Regression'] |
K-Nearest Neighbors (KNN) Algorithm
Simple Analogy for K-Nearest Neighbors (K-NN)
In this blog, we’ll talk about one of the most widely used machine learning algorithms for classification, which is the K-Nearest Neighbors (KNN) algorithm. K-Nearest Neighbor (K-NN) is a simple, easy to understand, versatile and one of... | ['Artificial Intelligence', 'Data Science', 'Supervised Learning', 'Machine Learning', 'K Nearest Neighbours'] |
Neural Knapsack
In some cases of data science, it is needed to run a specific algorithm on the output of the model to get the result. Sometimes it is as simple as finding the index of the maximum output, other times, more advanced algorithms are needed. You may run the algorithm after running the inference. However, d... | ['Deep Learning', 'Supervised Learning', 'Unsupervised Learning', 'Knapsack Problem', 'Neural Networks'] |
In the 1st plot, we can see that decision tree with one-hot encoded features creates many many splits and the tree is all left-sided. The tree is still getting the work done and is able to separate the data. We observe split values to be 66, 99, 0, 88, 11 respectively from root to leaf. At each split, information gain ... | ['Supervised Learning', 'Machine Learning', 'Catboost', 'Categorical Data'] |
Decision Tree Classification
A Decision Tree is a simple representation for classifying examples. It is a Supervised Machine Learning where the data is continuously split according to a certain parameter.
Decision Tree consists of :
Nodes : Test for the value of a certain attribute. Edges/ Branch : Correspond to the... | ['Artificial Intelligence', 'Data Science', 'Supervised Learning', 'Machine Learning', 'Decision Tree Classifier'] |
Photo by Franki Chamaki on Unsplash
Data Science 101
What is data science?
Well, if you have just woken up from a 10-year coma and have no idea what is data science, don’t worry, there’s still time. Many years ago, statisticians had some pretty good ideas for analysing data and getting insights from it, but they lac... | ['Data', 'Data Science', 'Supervised Learning', 'Science', 'Analytics'] |
supervised learning is a learning in which we train the machine using data which is well labeled (i.e the questions with answers). After that, the machine is provided with a new set of questions(data) so that supervised learning algorithm analyses the training data(set of training examples) and produces a correct outco... | ['Supervised Learning', 'Machine Learning'] |
Geoffrey E. Hinton: Capsule Network using Autoencoders
Don Hinton’s presentation was about the limitations of CNN and how to overcome them. In his opinion, CNNs do not recognize the object the same way we do. People use coordinate frame and always assign intrinsic coordinate frame when they perceive shapes — this is a... | ['AI', 'Artificial Intelligence', 'NLP', 'Supervised Learning', 'Self Supervised Learning'] |
K-Nearest Neighbors (kNN) — Explained
K-nearest neighbors (kNN) is a supervised machine learning algorithm that can be used to solve both classification and regression tasks. I see kNN as an algorithm that comes from real life. People tend to be effected by the people around them. Our behaviour is guided by the friend... | ['Predictive Analytics', 'Data Science', 'Supervised Learning', 'Machine Learning', 'Data Analysis'] |
Hands-on
Now, we have some ideas about machine learning and we are ready to move on to the practical aspect to start training a machine learning model.
1. Objective
The objective of this project is to perform value estimation of house prices in Sindian District., New Taipei City, Taiwan. To achieve the objective, we... | ['Supervised Learning', 'Machine Learning', 'Python', 'Data Science'] |
Let’s go back to the sigmoid function and show it in a different way:
Taking the natural log of both sides:
In equation (1), instead of x, we can use a linear equation z:
Then equation (1) becomes:
Assume y is the probability of positive class. If z is 0, then y is 0,5. For positive values of z, y is higher than 0,... | ['Logistic Regression', 'Data Science', 'Supervised Learning', 'Machine Learning', 'Data Analysis'] |
A beautiful picture of a manifold (taken from the internet)
I wrote this paper back in the Spring of 2013, when dealt with a problem in cancer detection through tumour images and was faced with the issue of little or no data. This is quite a common issue among most machine learning tasks (especially in industry)— and ... | ['Data', 'Data Science', 'Supervised Learning', 'Machine Learning', 'Classification Algorithms'] |
Naive Bayes Classifier — Explained
Naive Bayes is a supervised learning algorithm used for classification tasks. Hence, it is also called Naive Bayes Classifier.
As other supervised learning algorithms, naive bayes uses features to make a prediction on a target variable. The key difference is that naive bayes assumes... | ['Data Science', 'Supervised Learning', 'Machine Learning', 'Naive Bayes', 'Data Analysis'] |
Is the Future of Cyber Security in the Hands of Artificial Intelligence (AI)? — 1
It is a fact that machine-learning has taken great steps in recent years, from autonomous tools to virtual assistants, from chatbots to face/object recognition. What about cybersecurity? Ensar Seker Jun 10, 2020·6 min read
Chinese philo... | ['Cybersecurity', 'Towards Data Science', 'Supervised Learning', 'Machine Learning', 'Unsupervised Learning'] |
Let’s us now start with coding 😍.
Step 1: If you remember, our first step is to collect the data & visualize. So, let’s execute the below command to collect data, flightdata.csv.
Now with the help of pandas library, we will try to read the data in the dataset & explore by storing as a dataframe, 2D data array.
The ... | ['Data Science', 'Supervised Learning', 'Machine Learning', 'Microsoft Azure', 'Sklearn'] |
Supervised Machine Learning is a currently used buzz word by many organizations to identify and solve business problems. There are two types of algorithms commonly used — Classification and Regression. In this article, we will focus on Regression. Regression analysis are the models which predict a continuous outcome. A... | ['AI', 'Statistics', 'Data Science', 'Supervised Learning', 'Machine Learning'] |
Understanding Classification Learning, especially Logistic Regression, will help us lay down some major concepts that are fundamental to Neural Networks and its computationally intensive processes.
In this series, we’ll go through an overview of Supervised Learning then dive deeper with basic examples into how and whe... | ['Supervised Learning', 'Classification', 'Neural Networks', 'Logistic Regression'] |
What’s the contrast of ‘ k ’ in k-Means and kNN ?
Often times, k-Means and kNN algorithms are interpreted in same manner although there is a distinct difference between the two. Today, we look into the major contrasts in implementing these algorithms.
k-Means
k-means clustering algorithm is one of the popular unsupe... | ['Knn', 'K Means', 'Supervised Learning', 'Machine Learning', 'Clustering'] |
Source: Flickr
“Well, here’s another nice mess you’ve gotten me into!”
This week I go even further back in time with my references, all the way back to the 1930s. In case some of the Millennial readers just thought “OK Boomer”, I’d like to clarify that I am a Millennial myself. It’s just that sometimes old-timey refe... | ['Supervised Learning', 'Machine Learning', 'Bias Variance Tradeoff', 'Data Science'] |
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