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

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. 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. 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. 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. 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. 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. 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.