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.
Instructor
Kirill Eremenko
319,449
Students
enrolled
- Description
- Curriculum
- FAQ
- Reviews
-------------------- Part 1: Data Preprocessing --------------------
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1
Applications of Machine Learning -
2
Why Machine Learning is the Future
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3
Important notes, tips & tricks for this course -
4
This PDF resource will help you a lot
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5
Updates on Udemy Reviews
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6
Installing Python and Anaconda (Mac, Linux & Windows)
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7
Update: Recommended Anaconda Version
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8
Installing R and R Studio (Mac, Linux & Windows)
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9
BONUS: Meet your instructors
-------------------- Part 2: Regression --------------------
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10
Welcome to Part 1 - Data Preprocessing
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11
Get the dataset
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12
Importing the Libraries
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13
Importing the Dataset
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14
For Python learners, summary of Object-oriented programming: classes & objects
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15
Missing Data
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16
Categorical Data
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17
WARNING - Update
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18
Splitting the Dataset into the Training set and Test set
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19
Feature Scaling
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20
And here is our Data Preprocessing Template!
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21
Data Preprocessing
Simple Linear Regression
Multiple Linear Regression
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23
How to get the dataset
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24
Dataset + Business Problem Description
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25
Simple Linear Regression Intuition - Step 1
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26
Simple Linear Regression Intuition - Step 2
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27
Simple Linear Regression in Python - Step 1
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28
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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32
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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35
Simple Linear Regression
Polynomial Regression
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36
How to get the dataset
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37
Dataset + Business Problem Description
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38
Multiple Linear Regression Intuition - Step 1
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39
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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43
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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51
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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56
Multiple Linear Regression in R - Automatic Backward Elimination
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57
Multiple Linear Regression
Support Vector Regression (SVR)
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58
Polynomial Regression Intuition
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59
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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68
Polynomial Regression in R - Step 4
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69
R Regression Template
Decision Tree Regression
Random Forest Regression
Evaluating Regression Models Performance
-------------------- Part 3: Classification --------------------
Logistic Regression
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Course details
Lectures
27
Quizzes
1
Video
41 hours
Certificate of Completion
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