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Python Machine Learning in 7 Days

Build powerful Machine Learning models using Python with hands-on practical examples in just a week.


Description

Machine learning is one of the most sought-after skills in the market. But have you ever wondered where to start or found the course not so easy to follow. With this hands-on and practical machine learning course, you can learn and start applying machine learning in less than a week without having to be an expert mathematician.

In this course, you will be introduced to a new machine learning aspect in each section followed by a practical assignment as a homework to help you in efficiently implement the learnings in a practical manner. With the systematic and fast-paced approach to this course, learn machine learning using Python in the most practical and structured way to develop machine learning projects in Python in a week.

This course is structured to unlock the potential of Python machine learning in the shortest amount of time. If you are looking to upgrade your machine learning skills using Python in the quickest possible time, then this course is for you!

This course uses Python 3.6 while not the latest version available, it provides relevant and informative content for legacy users of Python.

About the Author

Arish Ali started his machine learning journey 5 years ago by winning an all India machine learning competition conducted by Indian Institute of Science and Microsoft. He was a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has also worked on some of the cutting edge problems of Multi-Touch Attribution Modelling, Market Mix Modelling, and Deep Neural Networks. He has also been an Adjunct faculty for Predictive Business Analytics at Bridge School of Management, which offers its course in Predictive Business Analytics along with North-western University (SPS). Currently, he is working at a mental health startup called Bemo as an AI developer where his role is to help automate the therapy provided to users and make it more personalized.

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