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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.
4.5 (3,344 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
29,675 students enrolled
Last updated 2/2017
$15 $200 92% off
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  • 36 hours on-demand video
  • 19 Articles
  • 71 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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 live 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.

Who is the target audience?
  • 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 confortable 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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What Will I Learn?
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
View Curriculum
  • Just some high school mathematics level
Curriculum For This Course
Expand All 231 Lectures Collapse All 231 Lectures 36:03:17
Welcome to the course!
5 Lectures 23:39

Why Machine Learning is the Future

Installing Python and Anaconda (MAC & Windows)

BONUS: Meet your instructors
-------------------------- Part 1: Data Preprocessing --------------------------
10 Lectures 01:38:58

Get the dataset

Importing the Libraries

Importing the Dataset

For Python learners, summary of Object-oriented programming: classes & objects

Missing Data

Categorical Data

Splitting the Dataset into the Training set and Test set

Feature Scaling

And here is our Data Preprocessing Template!

Data Preprocessing
5 questions
------------------------------ Part 2: Regression ------------------------------
1 Lecture 00:23
Welcome to Part 2 - Regression
Simple Linear Regression
11 Lectures 01:21:48
Dataset + Business Problem Description

Simple Linear Regression Intuition - Step 1

Simple Linear Regression Intuition - Step 2

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 R - Step 1

Simple Linear Regression in R - Step 3

Simple Linear Regression in R - Step 4

Simple Linear Regression
5 questions
Multiple Linear Regression
17 Lectures 02:18:28
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

Multiple Linear Regression Intuition - Step 5

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 - Backward Elimination - Preparation

Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !

Multiple Linear Regression in Python - Backward Elimination - Homework Solution

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
5 questions
Polynomial Regression
11 Lectures 02:05:48
Polynomial Regression Intuition

Polynomial Regression in Python - Step 1

Polynomial Regression in Python - Step 2

Polynomial Regression in Python - Step 3

Polynomial Regression in Python - Step 4

Python Regression Template

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
Support Vector Regression (SVR)
2 Lectures 31:41
SVR in Python

SVR in R
Decision Tree Regression
3 Lectures 45:45
Decision Tree Regression Intuition

Decision Tree Regression in Python

Decision Tree Regression in R
Random Forest Regression
3 Lectures 41:10
Random Forest Regression Intuition

Random Forest Regression in Python

Random Forest Regression in R
Evaluating Regression Models Performance
5 Lectures 35:03
R-Squared Intuition

Adjusted R-Squared Intuition

Evaluating Regression Models Performance - Homework's Final Part

Interpreting Linear Regression Coefficients

Conclusion of Part 2 - Regression
30 More Sections
About the Instructor
4.5 Average rating
17,059 Reviews
93,877 Students
25 Courses
Data Scientist & Forex Systems Expert

My name is Kirill Eremenko and I am super-psyched that you are reading this!

I teach courses in two distinct Business areas on Udemy: Data Science and Forex Trading. I want you to be confident that I can deliver the best training there is, so below is some of my background in both these fields.

Data Science

Professionally, I am a Data Science management consultant with over five years of experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and today I leverage Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.

From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. I am also passionate about public speaking, and regularly present on Big Data at leading Australian universities and industry events.

Forex Trading

Since 2007 I have been actively involved in the Forex market as a trader as well as running programming courses in MQL4. Forex trading is something I really enjoy, because the Forex market can give you financial, and more importantly - personal freedom.

In my other life I am a Data Scientist - I study numbers to analyze patterns in business processes and human behaviour... Sound familiar? Yep! Coincidentally, I am a big fan of Algorithmic Trading :) EAs, Forex Robots, Indicators, Scripts, MQL4, even java programming for Forex - Love It All!


To sum up, I am absolutely and utterly passionate about both Data Science and Forex Trading and I am looking forward to sharing my passion and knowledge with you!

4.5 Average rating
3,492 Reviews
31,293 Students
3 Courses
Data Scientist

Hi. My name is Hadelin de Ponteves. Always eager to learn, I invested a lot of my time in learning and teaching, covering a wide range of different scientific topics. 

Today I am passionate about data science, artificial intelligence and deep learning. I will do my very best to convey my passion for data science to you. I have gained diverse experience in this field. I have an engineering master's degree with a specialisation in data science. I spent one year doing research in machine learning, working on innovative and exciting projects. Then a work experience at Google where I implemented some machine learning models for business analytics. 

Eventually, I realised I spent most of my time doing analysis and I gradually needed to feed my creativity so I became an entrepreneur. My courses will combine the two dimensions of analysis and creativity, allowing you to learn all the analytic skills required in data science, by applying it on creative ideas. 

Looking forward to working together!

Hello, je m'appelle Hadelin de Ponteves et je suis un data scientist passionné. 

Etant particulièrement sensible au domaine de l'éducation, je suis déterminé à y apporter de grandes contributions. J'ai déjà investi beaucoup de mon temps dans la sphère de l'éducation, à étudier et enseigner divers sujets scientifiques. 

Aujourd'hui, je suis passionné de data sciences, d'intelligence artificielle et de deep learning. Et je ferai de mon mieux pour vous transmettre mes passions. Car c'est en étant passionné que l'on réussit le mieux dans un domaine, et que l'on est le plus heureux dans notre travail au quotidien.

J'ai acquis beaucoup d'expérience en data sciences. J'ai effectué mes études à l'école Centrale Paris, où j'ai suivi le parcours Data Sciences, en parallèle d'un master de recherche en machine learning à l'Ecole Normale Supérieure. Ma page étudiante s'est enchaînée avec une expérience chez Google où j'ai fait des data sciences pour résoudre des problèmes business. Puis j'ai réalisé que je passais la plupart de mon temps à analyser et je développais petit à petit un besoin de créer. Donc pour nourrir ma créativité, je suis devenu un entrepreneur.

Et justement, mes cours vont tous combiner ces deux dimensions d'analyse et de créativité, grâce auxquelles vous intégrerez toutes les compétences à avoir en data sciences, en les appliquant à des idées créatives.

J'ai hâte de vous retrouver dans mes cours et de partager mes passions avec vous!

Hadelin de Ponteves

4.5 Average rating
12,984 Reviews
72,646 Students
12 Courses
Helping Data Scientists Succeed

Hi there,

We are the SuperDataScience team. You will find us in the Data Science courses taught by Kirill Eremenko - we are here to help you out with any questions and make sure your journey through the courses is always smooth sailing!

The best way to get in touch is to post a discussion in the Q&A of the course you are taking. In most cases we will respond within 24 hours.

We're passionate about helping you enjoy the courses!

See you in class,


The Real People at SuperDataScience

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