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.

Created by: Kirill Eremenko

Produced in 2020

What you will 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

Quality Score

Content Quality
Video Quality
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Overall Score : 90 / 100

Course Description

machine learning Awards Best Paid 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 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.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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Instructor Details

Kirill Eremenko

My name is Kirill Eremenko and I am super-psyched that you are reading this!
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.
To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you!Hadelin is the co-founder and Director of Technology at BlueLife AI, which leverages the power of cutting edge Artificial Intelligence to empower businesses to make massive profits by optimizing processes, maximizing efficiency and increasing profitability. Hadelin is also an online entrepreneur who brings educational e-courses to the world on topics such as Machine Learning, Deep Learning, Artificial Intelligence and Blockchain, which have reached over 600,000 subscribers in 204 countries.Hi there,
We are the SuperDataScience Social team. You will be hearing from us when ne

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By Muhlis Cm on a week ago

It is a wonderful course for beginners and explained lots of complex theories.Practical exercisers are very interesting.Even though lots of codes get depreciation warning and some changes in syntax.Please update the codes accordingly.

By Sudeep Mohan M on a month ago

Good Course. It will provide all the required basics to get you started on Machine learning. Hadelin and Kirill have explained the concepts very clearly and has made it simple to understand

By Dushyant Rai Tara on a month ago

This is one of the best courses I have completed around Machine Learning.It is a great refresher.Great work team!!

By Andrea Amato on a month ago

Very straightforward and interesting course to learn about machine learning and deep learning from scratch. It goes through very interesting arguments building very useful templates reusable for personal projects. A lot of insight and additional lectures suggestion given during the learn by doing lessons. Very comprehensible theory lessons that explain complex concept in a very simple way.

By Amy Li on a month ago

The intuition is easy to understand for a beginner like me, and the explanation of the steps in python and R are very clear. The template is especially helpful! I learnt more than I expected! Thank you!!

By Sayantan Mitra on 2 months ago

Do not waste your money. Some lectures are AWFUL! Reading from slides. Datasets are too small (claiming to be real world data sciecne projects). MOST IMPORTANTLY PEOPLE KEEP ASKING ABOUT SLIDES, and same COPY-PASTE answer. Absolute wastage of money :(

By Yinghao Zhang on 2 weeks ago

This is a good course for every new rookie enthusiastic about the machine learning area and its application in the business world. Both Kirill and Hadelin illustrate the intuition and code well. Here is my review:Good things:- This course covers nearly every important topic in machine learning. Most algorithms intuition is very clear and easy to understand.- Even if the speaking speed of Hadelin is a little bit slow, it's very clear and well-structured.- The method and tool in this course can be applied in the real-world problem.What needs to be improved:- Some algorithm illustration is poor, especially for support vector regression. I cannot understand the content of this part.- Some videos are obviously not updated, including PCA, LDA, and kernel PCA.- Some important parts for sklearn is missing, including how to build a machine learning pipeline.Overall, this is a good introductory course, but this is not enough if we want to go deep into the machine learning. I strongly hope new courses can be developed related to the mathematical principle in these algorithms.

By Mihiretu Kebede on 3 weeks ago

I thought this course was to be good. The reviews misled me. The instructions were somewhat good although there were some misleading points.Overall, I found out this course was totally useless! The same repetitive copy and paste codes throughout. Very superficial. Almost all of the machine learning algorithms were based on only one dependent variable and one independent variable. The name of the course (A-Z) is totally misleading. This is not what I expected! Total waste of money! :(

By Kubra Burcak Bulut on 3 months ago

Great start to Machine Learning! I would have liked it more if it had explained more Math behind the ML models.

By Ting Cheong on a month ago

The tutorials were very informative, the pace was very good. Each steps of code were explained very clearly. Before the tutorials there is some intuition lectures plus links for additional reading which were very helpful. The instructors are experienced and knew their stuff. It would be nice to have some challenges with feedback but overall it was a great journey and I definitely learnt A LOT in these few days.

By Kwamy Mick ADJIWANOU on 3 months ago

Off course It wasIt really highlighted my knowledge of Machine LearningI really enjoyed so far and I can't wait to learn more about NLP and Deep Learning

By Omji Mishra on a month ago

I purchased this course 18 months ago but because of the buzz about Machine Learning, I was afraid that this is not for me. But at some point of time you realize that you need to do this. Hence I pushed myself and started this. And Voila ! I completed the course in just 3 weeks !! I must say thanks to both the instructors(and maybe the third one) for the lectures. I have Python background and hence completed only that one and I can say they are awesome. If you are beginner then go ahead without any doubt.