Machine Learning A-Z™: Hands-On Python & R In Data Science

Online Self-Paced

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

Course Description

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.

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 that 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. Important updates (June 2020):

CODES ALL UP TO DATE
DEEP LEARNING CODED IN TENSORFLOW 2.0
TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST!

Requirements

Just some high school mathematics level.

This course includes:

* More than 7hr of learning time required

Course content

Welcome to the course!

Applications of Machine Learning
BONUS #1: Learning Paths
BONUS #2: ML vs. DL vs. AI - What’s the Difference?
BONUS #3: Regression Types
Why Machine Learning is the Future
Important notes, tips & tricks for this course
This PDF resource will help you a lot!
GET ALL THE CODES AND DATASETS HERE!
Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder
Installing R and R Studio (Mac, Linux & Windows)
BONUS: Meet your instructors
Some Additional Resources
FAQBot!
Your Shortcut To Becoming A Better Data Scientist!

Part 1: Data Preprocessing

Welcome to Part 1 - Data Preprocessing

Data Preprocessing in Python

Make sure you have your Machine Learning A-Z folder ready
Getting Started
Importing the Libraries
Importing the Dataset
For Python learners, summary of Object-oriented programming: classes & objects
Taking care of Missing Data
Encoding Categorical Data
Splitting the dataset into the Training set and Test set
Feature Scaling

Data Preprocessing in R

Welcome
Getting Started
Make sure you have your dataset ready
Dataset Description
Importing the Dataset
Taking care of Missing Data
Encoding Categorical Data
Splitting the dataset into the Training set and Test set
Feature Scaling
Data Preprocessing Template

Part 2: Regression

Welcome to Part 2 – Regression

Simple Linear Regression

Simple Linear Regression Intuition - Step 1
Simple Linear Regression Intuition - Step 2
Make sure you have your Machine Learning A-Z folder ready
Simple Linear Regression in Python - Step 1
Simple Linear Regression in Python - Step 2
Simple Linear Regression in Python - Step 3
Simple Linear Regression in Python - Step 4
Simple Linear Regression in Python - BONUS
Simple Linear Regression in R - Step 1
Simple Linear Regression in R - Step 2
Simple Linear Regression in R - Step 3
Simple Linear Regression in R - Step 4
Simple Linear Regression

Multiple Linear Regression

Dataset + Business Problem Description
Multiple Linear Regression Intuition - Step 1
Multiple Linear Regression Intuition - Step 2
Multiple Linear Regression Intuition - Step 3
Multiple Linear Regression Intuition - Step 4
Understanding the P-Value
Multiple Linear Regression Intuition - Step 5
Make sure you have your Machine Learning A-Z folder ready
Multiple Linear Regression in Python - Step 1
Multiple Linear Regression in Python - Step 2
Multiple Linear Regression in Python - Step 3
Multiple Linear Regression in Python - Step 4
Multiple Linear Regression in Python - Backward Elimination
Multiple Linear Regression in Python - BONUS
Multiple Linear Regression in R - Step 1
Multiple Linear Regression in R - Step 2
Multiple Linear Regression in R - Step 3
Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
Multiple Linear Regression in R - Backward Elimination - Homework Solution
Multiple Linear Regression in R - Automatic Backward Elimination
Multiple Linear Regression

Polynomial Regression

Polynomial Regression Intuition
Make sure you have your Machine Learning A-Z folder ready
Polynomial Regression in Python - Step 1
Polynomial Regression in Python - Step 2
Polynomial Regression in Python - Step 3
Polynomial Regression in Python - Step 4
Polynomial Regression in R - Step 1
Polynomial Regression in R - Step 2
Polynomial Regression in R - Step 3
Polynomial Regression in R - Step 4
R Regression Template

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Certificate of Completion

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Please note: free courses and courses that only include practice tests do not offer a certificate of completion.

What you'll learn

Master Machine Learning on Python & R
Make accurate predictions
Make robust Machine Learning models
Use Machine Learning for personal purpose
Handle advanced techniques like Dimensionality Reduction
Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Have a great intuition of many Machine Learning models
Make powerful analysis
Create strong added value to your business
Handle specific topics like Reinforcement Learning, NLP and Deep Learning
Know which Machine Learning model to choose for each type of problem

Student feedback

Prasanth Nutheti

I knew nothing about ML and AI before starting this course. Now, I am very confident in solving problems. Looking for an advanced course now. Kirill and Hadelin are awesome

Anuj Gupta

After going through courses on other platforms I realized that this course builds your foundation in Machine learning and not just tells you how to use the libs.

Luca Passani

I think it's a good overview of a huge topic. I feel I am very far from being a Data Scientist, but at least I can understands the big picture and discuss AI/ML/NN in polite company without looking stupid 😉

Jeff Colbeck

Lots of content and that part is great. Sometimes I was not able to match the results shown in the tutorial. Many people had the same problems but getting a clear response back in the Q&A section seems to be an issue.

Adarsh Tripathy

It was very insightful and extensive. The only problems that i faced were that my queries weren't solved when i posted them. So if you could work on that, it would be perfect!!! Thank You

Who this course is for:

Anyone interested in Machine Learning.
Students who have at least high school knowledge in math and who want to start learning Machine Learning.
Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
Any students in college who want to start a career in Data Science.
Any data analysts who want to level up in Machine Learning.
Any people who are not satisfied with their job and who want to become a Data Scientist.
Any people who want to create added value to their business by using powerful Machine Learning tools.

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