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.
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
-------------------- Part 1: Data Preprocessing --------------------
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Applications of Machine Learning -
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Why Machine Learning is the Future
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Important notes, tips & tricks for this course -
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This PDF resource will help you a lot
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Updates on Udemy Reviews
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Installing Python and Anaconda (Mac, Linux & Windows)
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Update: Recommended Anaconda Version
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Installing R and R Studio (Mac, Linux & Windows)
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BONUS: Meet your instructors
-------------------- Part 2: Regression --------------------
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Welcome to Part 1 - Data Preprocessing
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Get the dataset
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Importing the Libraries
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Importing the Dataset
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For Python learners, summary of Object-oriented programming: classes & objects
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Missing Data
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Categorical Data
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WARNING - Update
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Splitting the Dataset into the Training set and Test set
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Feature Scaling
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And here is our Data Preprocessing Template!
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Data Preprocessing
Simple Linear Regression
Multiple Linear Regression
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How to get the dataset
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Dataset + Business Problem Description
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Simple Linear Regression Intuition - Step 1
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Simple Linear Regression Intuition - Step 2
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Simple Linear Regression in Python - Step 1
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Simple Linear Regression in Python - Step 2
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Simple Linear Regression in Python - Step 3
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Simple Linear Regression in Python - Step 4
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Simple Linear Regression in R - Step 1
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Simple Linear Regression in R - Step 2
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Simple Linear Regression in R - Step 3
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Simple Linear Regression in R - Step 4
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Simple Linear Regression
Polynomial Regression
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How to get the dataset
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Dataset + Business Problem Description
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Multiple Linear Regression Intuition - Step 1
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Multiple Linear Regression Intuition - Step 2
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Multiple Linear Regression Intuition - Step 3
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Multiple Linear Regression Intuition - Step 4
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Prerequisites: What is the P-Value?
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Multiple Linear Regression Intuition - Step 5
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Multiple Linear Regression in Python - Step 1
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Multiple Linear Regression in Python - Step 2
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Multiple Linear Regression in Python - Step 3
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Multiple Linear Regression in Python - Backward Elimination - Preparation
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Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !
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Multiple Linear Regression in Python - Backward Elimination - Homework Solution
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Multiple Linear Regression in Python - Automatic Backward Elimination
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Multiple Linear Regression in R - Step 1
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Multiple Linear Regression in R - Step 2
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Multiple Linear Regression in R - Step 3
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Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
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Multiple Linear Regression in R - Backward Elimination - Homework Solution
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Multiple Linear Regression in R - Automatic Backward Elimination
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Multiple Linear Regression
Support Vector Regression (SVR)
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Polynomial Regression Intuition
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How to get the dataset
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Polynomial Regression in Python - Step 1
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Polynomial Regression in Python - Step 2
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Polynomial Regression in Python - Step 3
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Polynomial Regression in Python - Step 4
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Python Regression Template
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Polynomial Regression in R - Step 1
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Polynomial Regression in R - Step 2
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Polynomial Regression in R - Step 3
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Polynomial Regression in R - Step 4
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R Regression Template
Decision Tree Regression
Random Forest Regression
Evaluating Regression Models Performance
-------------------- Part 3: Classification --------------------
Logistic Regression
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