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Ensemble Methods for Machine Learning

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In Ensemble Methods for Machine Learning you'll learn to implement the most important ensemble machine learning methods from scratch. Each chapter contains a new case study, taking you hands-on with a fully functioning ensemble method for medical diagnosis, sentiment analysis, handwriting classification, and more. There's no complex math or theory—each method is taught in a practical and visuals-first manner. Best of all, all code is provided in Jupyter notebooks for your easy experimentation! By the time you're done, you’ll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems.

Learn more about the book here: http://mng.bz/YxwN

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Ensemble Methods for Machine Learning

  1. 1. Making machine learning models work together with Ensemble Methods for Machine Learning. Take 40% off by entering slkunapuli into the discount code box at checkout at manning.com.
  2. 2. Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem by aggregating their output, allowing them to deliver rich and accurate results.
  3. 3. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real-world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models.
  4. 4. In Ensemble Methods for Machine Learning you'll learn to implement the most important ensemble machine learning methods from scratch. By the time you're done, you’ll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems.
  5. 5. What people are saying about the book: The definitive and complete guide on ensemble learning. A must read! -Al Krinker The examples are clear and easy to reproduce, the writing is engaging and clear, and the reader is not bogged down by details which might be unimportant for beginners in the field! -Or Golan
  6. 6. About the author: Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry. He has developed several novel algorithms for diverse application domains including social network analysis, text and natural language processing, behavior mining, educational data mining, and biomedical applications. He has also published papers exploring ensemble methods in relational domains and with imbalanced data.
  7. 7. Take 40% off Ensemble Methods for Machine Learning by entering slkunapuli into the discount code box at checkout at manning.com. If you want to see more, check out the book on our browser-based liveBook platform here.

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