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

1

Applications of Machine Learning

2

Why Machine Learning is the Future

3

Important notes, tips & tricks for this course

4

This PDF resource will help you a lot

5

Updates on Udemy Reviews

6

Installing Python and Anaconda (Mac, Linux & Windows)

7

Update: Recommended Anaconda Version

8

Installing R and R Studio (Mac, Linux & Windows)

9

BONUS: Meet your instructors

1

Welcome to Part 1 - Data Preprocessing

2

Get the dataset

3

Importing the Libraries

4

Importing the Dataset

5

For Python learners, summary of Object-oriented programming: classes & objects

6

Missing Data

7

Categorical Data

8

WARNING - Update

9

Splitting the Dataset into the Training set and Test set

10

Feature Scaling

11

And here is our Data Preprocessing Template!

12

Data Preprocessing

1

Welcome to Part 2 - Regression

1

How to get the dataset

2

Dataset + Business Problem Description

3

Simple Linear Regression Intuition - Step 1

4

Simple Linear Regression Intuition - Step 2

5

Simple Linear Regression in Python - Step 1

6

Simple Linear Regression in Python - Step 2

7

Simple Linear Regression in Python - Step 3

8

Simple Linear Regression in Python - Step 4

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Simple Linear Regression in R - Step 1

10

Simple Linear Regression in R - Step 2

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Simple Linear Regression in R - Step 3

12

Simple Linear Regression in R - Step 4

13

Simple Linear Regression

1

How to get the dataset

2

Dataset + Business Problem Description

3

Multiple Linear Regression Intuition - Step 1

4

Multiple Linear Regression Intuition - Step 2

5

Multiple Linear Regression Intuition - Step 3

6

Multiple Linear Regression Intuition - Step 4

7

Prerequisites: What is the P-Value?

8

Multiple Linear Regression Intuition - Step 5

9

Multiple Linear Regression in Python - Step 1

10

Multiple Linear Regression in Python - Step 2

11

Multiple Linear Regression in Python - Step 3

12

Multiple Linear Regression in Python - Backward Elimination - Preparation

13

Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !

14

Multiple Linear Regression in Python - Backward Elimination - Homework Solution

15

Multiple Linear Regression in Python - Automatic Backward Elimination

16

Multiple Linear Regression in R - Step 1

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Multiple Linear Regression in R - Step 2

18

Multiple Linear Regression in R - Step 3

19

Multiple Linear Regression in R - Backward Elimination - HOMEWORK !

20

Multiple Linear Regression in R - Backward Elimination - Homework Solution

21

Multiple Linear Regression in R - Automatic Backward Elimination

22

Multiple Linear Regression

1

Polynomial Regression Intuition

2

How to get the dataset

3

Polynomial Regression in Python - Step 1

4

Polynomial Regression in Python - Step 2

5

Polynomial Regression in Python - Step 3

6

Polynomial Regression in Python - Step 4

7

Python Regression Template

8

Polynomial Regression in R - Step 1

9

Polynomial Regression in R - Step 2

10

Polynomial Regression in R - Step 3

11

Polynomial Regression in R - Step 4

12

R Regression Template

1

How to get the dataset

2

SVR Intuition

3

SVR in Python

4

SVR in R

1

Decision Tree Regression Intuition

2

How to get the dataset

3

Decision Tree Regression in Python

4

Decision Tree Regression in R

1

Random Forest Regression Intuition

2

How to get the dataset

3

Random Forest Regression in Python

4

Random Forest Regression in R

1

R-Squared Intuition

2

Adjusted R-Squared Intuition

3

Evaluating Regression Models Performance - Homework's Final Part

4

Interpreting Linear Regression Coefficients

5

Conclusion of Part 2 - Regression

1

Welcome to Part 3 - Classification

1

Logistic Regression Intuition

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