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.
-------------------- Part 1: Data Preprocessing --------------------
- 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
-------------------- Part 2: Regression --------------------
- 10 Welcome to Part 1 - Data Preprocessing
- 11 Get the dataset
- 12 Importing the Libraries
- 13 Importing the Dataset
- 14 For Python learners, summary of Object-oriented programming: classes & objects
- 15 Missing Data
- 16 Categorical Data
- 17 WARNING - Update
- 18 Splitting the Dataset into the Training set and Test set
- 19 Feature Scaling
- 20 And here is our Data Preprocessing Template!
- 21
Data Preprocessing
Simple Linear Regression
Multiple Linear Regression
- 23 How to get the dataset
- 24 Dataset + Business Problem Description
- 25 Simple Linear Regression Intuition - Step 1
- 26 Simple Linear Regression Intuition - Step 2
- 27 Simple Linear Regression in Python - Step 1
- 28 Simple Linear Regression in Python - Step 2
- 29 Simple Linear Regression in Python - Step 3
- 30 Simple Linear Regression in Python - Step 4
- 31 Simple Linear Regression in R - Step 1
- 32 Simple Linear Regression in R - Step 2
- 33 Simple Linear Regression in R - Step 3
- 34 Simple Linear Regression in R - Step 4
- 35 Simple Linear Regression
Polynomial Regression
- 36 How to get the dataset
- 37 Dataset + Business Problem Description
- 38 Multiple Linear Regression Intuition - Step 1
- 39 Multiple Linear Regression Intuition - Step 2
- 40 Multiple Linear Regression Intuition - Step 3
- 41 Multiple Linear Regression Intuition - Step 4
- 42 Prerequisites: What is the P-Value?
- 43 Multiple Linear Regression Intuition - Step 5
- 44 Multiple Linear Regression in Python - Step 1
- 45 Multiple Linear Regression in Python - Step 2
- 46 Multiple Linear Regression in Python - Step 3
- 47 Multiple Linear Regression in Python - Backward Elimination - Preparation
- 48 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !
- 49 Multiple Linear Regression in Python - Backward Elimination - Homework Solution
- 50 Multiple Linear Regression in Python - Automatic Backward Elimination
- 51 Multiple Linear Regression in R - Step 1
- 52 Multiple Linear Regression in R - Step 2
- 53 Multiple Linear Regression in R - Step 3
- 54 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
- 55 Multiple Linear Regression in R - Backward Elimination - Homework Solution
- 56 Multiple Linear Regression in R - Automatic Backward Elimination
- 57 Multiple Linear Regression
Support Vector Regression (SVR)
- 58 Polynomial Regression Intuition
- 59 How to get the dataset
- 60 Polynomial Regression in Python - Step 1
- 61 Polynomial Regression in Python - Step 2
- 62 Polynomial Regression in Python - Step 3
- 63 Polynomial Regression in Python - Step 4
- 64 Python Regression Template
- 65 Polynomial Regression in R - Step 1
- 66 Polynomial Regression in R - Step 2
- 67 Polynomial Regression in R - Step 3
- 68 Polynomial Regression in R - Step 4
- 69 R Regression Template
Decision Tree Regression
Random Forest Regression
Evaluating Regression Models Performance
-------------------- Part 3: Classification --------------------
Logistic Regression
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Instructor
Kirill Eremenko
Course details
Lectures 27
Quizzes 1
Video 41 hours
Certificate of Completion
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