Python programming language has rendered itself as the language of choice for coding beginners and advanced software programmers alike.
This book is written to help you master the basic concepts of Python coding and how you can utilize your coding skills to analyze a large volume of data and uncover valuable information that can otherwise be easily lost in the volume. Python was designed primarily to emphasize readability of the programming code, and its syntax enables programmers to convey ideas using fewer lines of code. Python programming language increases the speed of operation while allowing for higher efficiency in creating system integrations.
This book will provide you overarching guidance on how you can use Python to develop machine learning models using Scikit-Learn, 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).
● 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 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!
Don't you think it can be that easy? If you really want to have proof of all this, don't waste any more time!
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