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Are you thinking of learning Python machine learning?

If you are looking for a beginner book to learn how to apply machine learning models in Python, this book is for you.
The book presents a practical Machine Learning cases studies using Python. This includes Regression, Classification, Clustering and segmentation.
Regardless of the level of expertise of the reader, there is lots of distilled knowledge available in these pages, which would give the reader a new perspective on what machine learning is all about.

Who Should Read This?

This book presents applications of machine learning algorithms. It also present many examples and illustrations. The following groups of people would benefit maximally from from this book:

• The reader who has heard about the impact data science is set to make across industries but isn’t quite sure what skills are required to get a footing in the field. This set of readers can expect to profit from the clear explanations of basic concepts and build intuitions that enable them to transition on to more complex topics.
• The practitioner who has intermediate level skills in the related fields of statistics, mathematics, and computer science but wants to understand in what ways machine learning is a different discipline. This type of reader would understand the concepts presented in this book quickly as machine learning is an interdisciplinary field that sits at the intersection between many well established scientific fields.
• The practicing data scientist or experienced veteran would appreciate this book for providing a refresher on many common concepts and a whirlwind tour of what is currently obtainable in terms of best practices. The breadth of this book is such that this reader would have a reference manual of sorts for how to master main machine learning techniques.

What’s Inside This Book?

• Basics of Python for Beginners and Setting up an Environment
• Libraries and Packages
• Numpy
• Pandas
• Matplotlib
• Seaborn
• Scikit Learn
• Scipy
• Keras
• Tensorflow
• Concepts of Machine Learning using Python
• Steps Involved in Machine Learning
• Training Data
• Validation Data
• Testing Data
• Selecting Training, Validation and Testing Data
• Performance Measures: Bias and Variance
• Accuracy, Precision and Recall
• Machine Learning Case Studies with Python
• Classification Case Studies
• Regression Case Studies
• Clustering and Dimensionality Reduction Case Studies
• Python Machine Learning Applications
• Summary & Conclusion

From AI Sciences Publishing

Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. Readers are advised to adopt a hands on approach, which would lead to better mental representations.

Frequently Asked Questions

Q: Does this book include everything I need to become a machine learning expert?
A: Unfortunately, no. This book is designed for readers taking their first steps in machine learning and further learning will be required beyond this book to master all aspects.

Q: Can I have a refund if this book doesn’t fit for me?
A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform.

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