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

4.5 (131330)
Learning paid
44.5 hours course
Course by Udemy
$ 129.99
$ 129.99
What will you 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
About the course

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 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):


Welcome to the course!
Applications of Machine Learning
Why Machine Learning is the Future
Important notes, tips & tricks for this course
This PDF resource will help you a lot
The whole code folder of the course
Updates on Udemy Reviews
Installing Python and Anaconda (Mac, Linux & Windows)
Update: Recommended Anaconda Version
Installing R and R Studio (Mac, Linux & Windows)
In this video, Kirill explains in details how to install R programming language and R studio on your computer so you can swiftly go through the rest of the course
BONUS: Meet your instructors
Some Additional Resources
  • Just some high school mathematics level.
Kirill Eremenko
Kirill Eremenko
Data Scientist
Hadelin de Ponteves
Hadelin de Ponteves
AI Entrepreneur
SuperDataScience Team
SuperDataScience Team
Helping Data Scientists Succeed
SuperDataScience Support
SuperDataScience Support
Answering All Your Questions
Udemy courses are suited to professional development. The platform is organized in such a way that it is experts themselves that decide the topic and when the course will start. All supporting documents are made available to you for lifetime access. On this platform, you can find a course on about any subject, and that is no exaggeration – from a tutorial on how to ride a motorcycle, to managing the financial markets. The language and the course format are established by the teacher. This is why it is important to read the information about the course carefully before parting with any money.
Comments (131330)
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