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Ensemble Machine Learning Techniques [Video]

More Information
  • Apply ensemble techniques in your models with the power of machine-learning algorithms and the simplicity of Python
  • Apply decision trees to real datasets
  • Develop robust models using the bagging technique
  • Transform your weak models to strong models using boosting
  • Learn how to combine different types of model sequentially

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.

  • You will get an intuitive understanding of ensemble learning and we supply practical solutions to real-world problems 
  • Get hands-on with various machine learning techniques along with real-world examples
  • Get hands-on exposure to many different machine learning models and learn to combine them to solve problems
Course Length 1 hours 23 minutes
Date Of Publication 28 Sep 2018


Arish Ali

Arish Ali started his machine learning journey 5 years ago by winning an all India machine learning competition conducted by IISC and Microsoft. He was a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has worked on some of the cutting-edge problems of 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 its course in Predictive Business Analytics along with Northwestern University (SPS).
He worked at a mental health start-up called Bemo as an AI developer where his role was to help automate the therapy provided to users and make it more personalized. He is currently the CEO at Neurofy Pvt Ltd, a people analytics start-up.

LinkedIn: https://www.linkedin.com/in/arish813/