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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.
Instrutor:
Kirill Eremenko
319.449 alunos matriculados
English [Auto-generated] Mais
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

Welcome to the course!

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 1: Data Preprocessing --------------------

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

-------------------- Part 2: Regression --------------------

1
Welcome to Part 2 - Regression

Simple Linear 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
9
Simple Linear Regression in R - Step 1
10
Simple Linear Regression in R - Step 2
11
Simple Linear Regression in R - Step 3
12
Simple Linear Regression in R - Step 4
13
Simple Linear Regression

Multiple 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
17
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

Polynomial 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

Support Vector Regression (SVR)

1
How to get the dataset
2
SVR Intuition
3
SVR in Python
4
SVR in R

Decision Tree Regression

1
Decision Tree Regression Intuition
2
How to get the dataset
3
Decision Tree Regression in Python
4
Decision Tree Regression in R

Random Forest Regression

1
Random Forest Regression Intuition
2
How to get the dataset
3
Random Forest Regression in Python
4
Random Forest Regression in R

Evaluating Regression Models Performance

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

-------------------- Part 3: Classification --------------------

1
Welcome to Part 3 - Classification

Logistic Regression

1
Logistic Regression Intuition
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