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.
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
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.
We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:
- Part 1 – Data Preprocessing
- Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Part 4 – Clustering: K-Means, Hierarchical Clustering
- Part 5 – Association Rule Learning: Apriori, Eclat
- Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
- Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
- Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
- Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
- Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.
And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.
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
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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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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
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
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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
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
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Multiple Linear Regression in R - Step 3
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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
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Python Regression Template
8
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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Polynomial Regression in R - Step 4
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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
