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Learn Python: A Crash Course On Python Programming And How To Start Coding With It. Learn The Basics Of Machine Learning And Data Analysis
Paperback
ISBN13: 9781675618073
Publisher: Independently Published
Published: Dec 16 2019
Pages: 192
Weight: 0.58
Height: 0.41 Width: 6.00 Depth: 9.00
Language: English
This book will provide you overarching guidance on how you can use Python to develop machine learning models using Scikit-Learn, the most widely used Python-based machine learning library. Python programming, data analysis, and machine learning are widely credited in the birth of the powerhouse, which is the Silicon Valley. To become a machine learning expert, a sound and in-depth understanding of every nuance of this area is critical. With the use of free and open-source machine learning library, Scikit-Learn, you can easily access free data sets and get hands-on experience on the fundamentals of machine learning and the underlying challenges and limitations of engineering machines to 'think.' Some of the highlights of the book include:
- Key features and advantages of learning to code Python as well as the history of how Python programming was created.
- Step by step instructions on how to install Python on your operating systems (Windows, Mac, and Linux).
- Concept of Python data types is presented in exquisite detail with various examples of each data type.
- Learn how to create Python variables and assign desired data type to them.
- Includes comprehensive lists of a variety of built-in functions and methods supported by Python.
- Basic concepts of writing efficient and effective Python codes, focusing on various programming elements such as Booleans, Tuples, Sets, Dictionaries, and much more.
- Learn how to write if and else statements to retrieve desired information from your data.
- For and While loops are explained with explicit details in an easy to understand language.
- Learn the basic concepts of big data analysis and machine learning algorithms.
- Brief overview of various renowned machine learning libraries such as Scikit-Learn, NumPy, SciPy, and more.
All the concepts are explained with standard Python coding syntax supported with relevant examples and followed by exercises to help you test and verify your understanding of those concepts. Finally, as an added bonus you will learn some Python tips and tricks to take your machine learning programming game to the next level. Remember, knowledge is power, and with the great power you will gather from this book, you will be armed to make sound personal and professional technological choices. Your Python programming skillset will improve drastically, and you will be poised to develop your very own machine learning model!
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