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Top 5 Best Books for Machine Learning with Python

Machine learning provides computers with the ability to learn without being explicitly programmed. It is a subfield of computer science and a type of artificial intelligence (AI). The most popular language for machine learning is Python Programming Language. Because Python is an accessible, powerful and flexible language for machine learning.ÃÂ

1. Python Machine Learning, 1st Edition Author: Sebastian Raschka
ISBN: 1783555130
If you want to find out the question how to use Python to start and answering critical questions of your data, then this book Python Machine Learning is exactly for you. If you want to get started from scratch or want to extend your data science knowledge, then this is an essential and valuable resource.

2. Learning scikit-learn: Machine Learning in Python Author: Raúl Garreta,Guillermo Moncecchi
Published at: 25/11/2013
ISBN: 1783281936

Learning scikit-learn: Machine Learning in Python book starts with a brief introduction to the core concepts of machine learning with a simple example. Then, using real-world applications and advanced features, it takes a deep dive into the various machine learning techniques.



3. Machine Learning in Python: Essential Techniques for Predictive Analysis Author: Michael Bowles
Published at: 27/04/2015
ISBN: 1118961749

Machine learning doesn't have to be complex and highly specialized. This book shows you how Python makes this technology more accessible to a much wider audience, using methods that are simpler, more effective, and well tested without requiring an extensive background in math or statistics.



4. Building Machine Learning Systems with Python Author: Willi Richert,Luis Pedro Coelho
Published at: 26/07/2013
Featuring a wealth of real-world examples, this book provides you with an accessible route into Python machine learning. You will learn the Iris dataset and find out how to build complex classifiers, and get to grips with clustering through practical examples that deliver complex ideas with clarity.

5. Machine Learning For Absolute Beginners: A Plain English Introduction Author: Oliver Theobald
ISBN: 1549617214
Machine Learning for Absolute Beginners Second Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.

This major new edition features many topics not covered in the First Edition, including Cross Validation, Data Scrubbing and Ensemble Modeling. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Readers of the First Edition should not feel compelled to purchase this Second Edition.

Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle coding and deep learning, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment—as a fully grown Simba looking over the Pride Lands of Africa—then this is the book to gently hoist you up and offer you a clear lay of the land.

In this step-by-step guide you will learn:

- How to download free datasets
- What tools and machine learning libraries you need
- Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data
- Preparing data for analysis, including k-fold Validation
- Regression analysis to create trend lines
- Clustering, including k-means and k-nearest Neighbors
- The basics of Neural Networks
- Bias/Variance to improve your machine learning model
- Decision Trees to decode classification
- How to build your first Machine Learning Model to predict house values using Python

Q: Do I need programming experience to complete this book?
A: This book is designed for absolute beginners, so no programming experience is required. However, two of the later chapters introduce Python to demonstrate an actual machine learning model, so you will see programming language used in this book.

Q: I have already purchased the First Edition of this book, should I purchase this Second Edition?
A: As majority of the topics from the First Edition are covered in the Second Edition, you may be better served reading a more advanced title on machine learning.



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