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Machine Learning A-Z (Python & R in Data Science Course)

(12 customer reviews)
Product is rated as #37 in category Python

What you’ll 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

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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 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!

 

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.

12 reviews for Machine Learning A-Z (Python & R in Data Science Course)

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  1. Shine Goh

    clear explanation of dummy variables and the rule of thumb to drop one dummy column. Statistical significance was illustrated very clearly using the coin toss concept.

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  2. Stuart Claghorn

    Very challenging and informative course with lots of examples and thorough demonstration and explanation. Not for the faint-of-heart, but worth the effort. I very much appreciated and enjoyed this course.

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  3. Link

    I appreciate the fact that the course provides an introduction to many different fields of machine learning. However, I constantly find myself asking whether or not the level at which they teach these concepts will actually help me or not. For the most part, I feel as though I do not even have a basic understanding of the topics that were taught. In the intuition lectures, the explanation is usually rather shallow and almost completely lacks information about the math behind the concepts. I often look at the comments and the impression that I’m getting is that comments are seldom answered by instructors. Sometimes, I see 2 year-old comments pointing out mistakes in the lectures and yet the mistakes remain (even though the teaching assistants see and acknowledge them in the comments).
    The problems with the comments and the lectures mean that I often have to look up other sources just to understand what was said in the lecture.
    Near the end of the course, they mostly stop explaining the intuition behind the concepts. The last model they introduce in the course (XGBoost) doesn’t get any explanation as to how it works.

    This course might be a good introduction to machine learning, but you will probably have to do a lot of extra work just to comprehend the superficial explanation that they provide you with.

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  4. Roger Collet

    Hadelin and Kirill made an amazing job with their tutorials. Before the training, I knew a bit of theory on few ML tools. But now, I have a much larger knowledge of these, and importantly, I can practice! Thanks.

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  5. Hsin-Yi Duan

    Good course for people who want to have initial understanding about machine learning with simple explanation without too complicated mathematics.
    But still having some space to improve, ex: part9 dimensionality was not sufficient explanation in tutorial.

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  6. Parth Patel

    This is a basic introductory course on machine learning. For me, the value-driven sections were regression, classification, and clustering. After that, it went downhill but if you want to quickly learn about the three topics, this is a great resource.

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  7. Taha Marefati

    Coding tutorial videos could be much shorter. The end part of the course is left very unexplained. Looks like they have been in a rush to finish it.

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  8. Harshit Nigam

    Coding Part was good. But for Theory part if you provide the Documentation also then it will be much better. There is no Documentation.

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  9. Morley Amsellem

    This course was very well presented. It is quite complex, but the concepts were simplified as much as possible.

    Thank you!

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  10. Indra Wijaya

    It seems that the “theory” slides (intuition) are off in some places, especially the beginning, and I see many comments that think there should be more maths, but overall this course is a good course to get my feet wet in ML, and with the Udemy discount that happens every few days, it is very good for the price.

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  11. Saurabh Yadav

    what the hell is this!
    taking one example that too is the simplest example out there.
    you will learn nothing from this, don’t waste 20 to 30 hours on this

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  12. Ashalatha Fimson

    As I am new to Machine Learning, I learned many new things from this course. Thank you for doing such a wonderful course.

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    Machine Learning A-Z (Python & R in Data Science Course)
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