More Information
  • Tips and tricks to speed up your modeling process and obtain better results
  • Make predictions using advanced regression analysis with Python 
  • Modern techniques for solving supervised learning problems
  • Various ways to use ensemble learning with Python to derive optimum results
  • Build your own recommendation engine and perform collaborative filtering
  • Give your production machine learning system improved reliability

Machine learning allows us to interpret data structures and fit that data into models to identify patterns and make predictions. Python makes this easier with its huge set of libraries that can be easily used for machine learning. In this course, you will learn from a top Kaggle master to upgrade your Python skills with the latest advancements in Python.

It is essential to keep upgrading your machine learning skills as there are immense advancements taking place every day. In this course, you will get hands-on experience of solving real problems by implementing cutting-edge techniques to significantly boost your Python Machine Learning skills and, as a consequence, achieve optimized results in almost any project you are working on.

Each technique we cover is itself enough to improve your results. However; combining them together is where the real magic is. Throughout the course, you will work on real datasets to increase your expertise and keep adding new tools to your machine learning toolbox.

By the end of this course, you will know various tips, tricks, and techniques to upgrade your machine learning algorithms to reduce common problems, all the while building efficient machine learning models.

All the code and supporting files for this course are available on GitHub at:

Style and Approach

We practice real datasets from different fields, progressively increasing our expertise and putting new tools at our disposal. With a combination of these tools, almost any machine learning problem can be solved much faster and with far better overall results.

  • Learn from a Kaggle competition master and a Team Lead at the largest search engine company in Russia—a great mixture of competition experience and Industrial knowledge
  • Learn the techniques currently used among Kaggle top-tier competitors and best practices in real-life projects to upgrade your skills
  • We guide you through supervised learning from basic linear to ensemble models, by extending the capabilities of your ML system to build high-performance models
Course Length 2 hours 46 minutes
ISBN 9781789135817
Date Of Publication 28 Jun 2018


Valeriy Babushkin

Valeriy Babushkin has done an M. Sc. and has 5+ years' experience in industrial data science and academia. He is a Kaggle competition master and a 2018 IEEE SP Cup finalist. He has been a Data Science Team Lead at Yandex (the largest search engine in Russia; it outperforms Google) and runs an online taxi service (he acquired Uber in Russia and 15 other countries) and the biggest e-commerce platform in Russia.

He was also a Head of Data Science at Monetha. Monetha is creating a universal, transferable, immutable trust, and reputation system combined with a payment solution. Finally, he is decentralized and empowered by the Ethereum Blockchain.