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.
- الوصف
- المنهاج
- التعليمات
- المراجعات
-------------------- 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
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
5 نجوم
31731
4 نجوم
20923
3 نجوم
6240
2 نجوم
1225
1 نجوم
540
Course details
فيديو
41 hours
القراءات
27
Certificate of Completion
Popular courses
