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Data Science
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Machine Learning A-Z™: Hands-On Python & R In Data Science
4.5 (114,688 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
619,809 students enrolled

Machine Learning A-Z™: Hands-On Python & R In Data Science

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
Bestseller
4.5 (114,688 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
619,478 students enrolled
Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, SuperDataScience Support
Last updated 4/2020
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Machine Learning A-Z™: Hands-On Python & R In Data Science
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This course includes
  • 39.5 hours on-demand video
  • 34 articles
  • 6 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Master Machine Learning on Python & R
  • Have a great intuition of many Machine Learning models
  • Make accurate predictions
  • Make powerful analysis
  • Make robust Machine Learning models
  • Create strong added value to your business
  • Use Machine Learning for personal purpose
  • Handle specific topics like Reinforcement Learning, NLP and Deep Learning
  • Handle advanced techniques like Dimensionality Reduction
  • Know which Machine Learning model to choose for each type of problem
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem
Course content
Expand all 276 lectures 40:03:52
+ – Welcome to the course!
11 lectures 23:37

Real-life examples of Machine Learning applications.

Applications of Machine Learning
Preview 03:22
BONUS: Learning Paths
00:58

The course introduction, the instructors, and the importance of Machine Learning.

Why Machine Learning is the Future
06:37

Important notes, tips & tricks for Machine Learning A-Z course.

Important notes, tips & tricks for this course
02:01

An important PDF. It contains the whole structure of Machine Learning A-Z course and the answers to important questions.

This PDF resource will help you a lot!
01:04
Updates on Udemy Reviews
01:09
GET ALL THE CODES AND DATASETS HERE!
01:07

In this video, Kirill explains in details how to install R programming language and R studio on your computer so you can swiftly go through the rest of the course.

Installing R and R Studio (Mac, Linux & Windows)
Preview 05:40

Greetings from instructors, and an SDS podcast about some machine learning concepts & an overview of popular machine learning algorithms.

BONUS: Meet your instructors
00:28
Some Additional Resources
00:10
FAQBot!
00:59
+ – Data Preprocessing in Python
9 lectures 01:15:27
Make sure you have your Machine Learning A-Z folder ready
00:09
Getting Started
06:17
Importing the Libraries
05:58
Importing the Dataset
15:41

A short written summary of what needs to know in Object-oriented programming, e.g. class, object, and method.

For Python learners, summary of Object-oriented programming: classes & objects
01:00
Taking care of Missing Data
11:53
Encoding Categorical Data
15:00
Feature Scaling
08:26
Splitting the dataset into the Training set and Test set
11:03
+ – Data Preprocessing in R
9 lectures 42:51
Getting Started
01:35
Make sure you have your dataset ready
00:08
Dataset Description
01:57
Importing the Dataset
02:44
Taking care of Missing Data
06:22
Encoding Categorical Data
06:02
Splitting the dataset into the Training set and Test set
09:34
Feature Scaling
09:14
Data Preprocessing Template
05:15
+ – Quizz
0 lectures 00:00
Data Preprocessing
5 questions
+ – -------------------- Part 2: Regression --------------------
1 lecture 00:22

What is regression? 6 types of regression models are taught in this course.

Welcome to Part 2 - Regression
00:22
+ – Simple Linear Regression
10 lectures 01:21:22

The math behind Simple Linear Regression.

Simple Linear Regression Intuition - Step 1
05:45

Finding the best fitting line with Ordinary Least Squares method to model the linear relationship between independent variable and dependent variable.

Simple Linear Regression Intuition - Step 2
03:09
Simple Linear Regression in Python - Step 1
12:48
Simple Linear Regression in Python - Step 2
07:56

Predicting the test set results with the SLR model in Python.

Simple Linear Regression in Python - Step 3
06:43

Visualizing the test set prediction results with python matplotlib module.

Simple Linear Regression in Python - Step 4
14:50

Data preprocessing for Simple Linear Regression in R.

Simple Linear Regression in R - Step 1
04:40

Fitting Simple Linear Regression (SLR) model to the training set using R function ‘lm’.

Simple Linear Regression in R - Step 2
Preview 05:58

Predicting the test set results with the SLR model using R function ‘predict’ .

Simple Linear Regression in R - Step 3
03:38

Visualizing the training set results and test set results with R package ‘ggplot2’.

Simple Linear Regression in R - Step 4
15:55
Simple Linear Regression
5 questions
+ – Multiple Linear Regression
20 lectures 02:16:26

An application of Multiple Linear Regression: profit prediction for Startups.

Dataset + Business Problem Description
03:44

The math behind Multiple Linear Regression: modelling the linear relationship between the independent (explanatory) variables and dependent (response) variable.

Multiple Linear Regression Intuition - Step 1
01:02

The 5 assumptions associated with a linear regression model: linearity, homoscedasticity, multivariate normality, independence of error, and lack of multicollinearity.

Multiple Linear Regression Intuition - Step 2
01:00

Coding categorical variables in regression with dummy variables.

Multiple Linear Regression Intuition - Step 3
07:21

Dummy variable trap and how to avoid it.

Multiple Linear Regression Intuition - Step 4
02:10

Prerequisites for Multiple Linear Regression: p-value and how it works.

Prerequisites: What is the P-Value?
00:17

An intuitive guide to 5 Stepwise Regression methods of building multiple linear regression models: All-in, Backward Elimination, Forward Selection, Bidirectional Elimination, and Score Comparison.

Multiple Linear Regression Intuition - Step 5
15:41

Data preprocessing for Multiple Linear Regression in Python: encoding categorical data with sklearn.preprocessing module, and splitting dataset with sklearn.cross_validation module.

Multiple Linear Regression in Python - Step 1
15:57

Fitting Multiple Linear Regression (MLR) model to the training set with sklearn.linear_model module.

Multiple Linear Regression in Python - Step 2
02:56

Predicting the test set results with the MLR model.

Multiple Linear Regression in Python - Step 3
05:28

Building optimal Multiple Linear Regression model in Python: datasets preparation.

Multiple Linear Regression in Python - Backward Elimination - Preparation
09:58

Building optimal Multiple Linear Regression model with Python StatsModels module: Backward Elimination step by step.

Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !
12:40

Optimizing MLR model using Backward Elimination: screening predictors with regression analysis results (p-value)

Multiple Linear Regression in Python - Backward Elimination - Homework Solution
09:10

Python codes for automatic implementations of Backward Elimination.

Multiple Linear Regression in Python - Automatic Backward Elimination
00:42
Multiple Linear Regression in R - Step 1
07:50
Multiple Linear Regression in R - Step 2
10:25
Multiple Linear Regression in R - Step 3
04:26
Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
17:51
Multiple Linear Regression in R - Backward Elimination - Homework Solution
07:33
Multiple Linear Regression in R - Automatic Backward Elimination
00:15
Multiple Linear Regression
5 questions
+ – Polynomial Regression
12 lectures 02:06:06

The math behind Polynomial Regression: modelling the non-linear relationship between independent variables and dependent variable.

Polynomial Regression Intuition
05:08
Polynomial Regression update for Python
00:18

Data preprocessing for Polynomial Regression in Python.

Polynomial Regression in Python - Step 1
11:38

Fitting Polynomial Regression model and Linear Regression model to the dataset in Python.

Polynomial Regression in Python - Step 2
11:45

Visualizing linear regression results and polynomial regression results in Python.

Polynomial Regression in Python - Step 3
19:57

Comparison of Linear Regression and Polynomial Regression results.

Polynomial Regression in Python - Step 4
05:45

Template for regression modelling in python.

Python Regression Template
10:58

Data preprocessing for Polynomial Regression in R.

Polynomial Regression in R - Step 1
09:12

Fitting Polynomial Regression model and Linear Regression model to the dataset in R.

Polynomial Regression in R - Step 2
09:58

Visualizing Linear Repression results and Polynomial Regression results and comparing the models' performance.

Polynomial Regression in R - Step 3
19:54

Predicting new results with Linear Regression model and Polynomial Regression model.

Polynomial Regression in R - Step 4
09:35

Template for regression modelling in R.

R Regression Template
11:58
+ – Support Vector Regression (SVR)
3 lectures 40:10

Understanding the math behind SVR for linear and non-linear regression.

SVR Intuition
08:29

Salary prediction with Support Vector Regression using sklearn.svm module in python: data preprocessing, fitting, predicting, and visualizing the SVR results.

SVR in Python
19:57

Salary prediction with Support Vector Regression using R package ‘e1071’: data preprocessing, fitting, predicting, and visualizing the SVR results.

SVR in R
11:44
Requirements
  • Just some high school mathematics level.
Description

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:

  • Part 1 - Data Preprocessing
  • Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
  • Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
  • Part 4 - Clustering: K-Means, Hierarchical Clustering
  • Part 5 - Association Rule Learning: Apriori, Eclat
  • Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
  • Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP
  • Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
  • Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA
  • Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.

Who this course is for:
  • Anyone interested in Machine Learning.
  • Students who have at least high school knowledge in math and who want to start learning Machine Learning.
  • Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
  • Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
  • Any students in college who want to start a career in Data Science.
  • Any data analysts who want to level up in Machine Learning.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.
  • Any people who want to create added value to their business by using powerful Machine Learning tools.
Featured review
Aman Kumar
Aman Kumar ( 8 courses, 2 reviews )
3 months ago
Amazing course on Machine Learning I have done so far. Everything is explained clearly by the instructor. The only thing that was not explained clearly was Support Vector Regressor. I think the SVR intuition part can be improved. Other than this everything in the course was awesome. The templates provided in the course are very much useful when it comes to applying the algorithms to the real M.L. projects.