Combine multiple machine learning algorithms to build models with higher accuracy
Ensemble is a powerful way to upgrade your model as it combines models and doesn’t assume a single model is the most accurate. But what if we combine these models as a way to drop those limitations to produce a much more powerful classifier or regressor?
This course will show you how to combine various models to achieve higher accuracy than base models can. This has been the case in various contests such as Netflix and Kaggle, where the winning solutions used ensemble methods.
If you want more than a superficial look at machine learning models and wish to build reliable models, then this course is for you.
The code bundle is placed at this link https://github.com/PacktPublishing/Ensemble-Machine-Learning-Techniques-
Style and Approach
This fast-paced course offers practical and hands-on guidance with step-by-step instructions. This course will enable you to develop your own ensemble learning models and methods to use them efficiently.
What You Will Learn
Arish Ali started his machine learning journey 5 years ago by winning an All-India machine learning competition conducted by the Indian Institute of Science and Microsoft. He worked as a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has also worked on some cutting-edge problems in Multi-Touch Attribution Modeling, Market Mix Modeling, and Deep Neural Networks. He has also been an Adjunct faculty for Predictive Business Analytics at Bridge School of Management, which offers a 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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