Top 10 Books on Machine Learning with Python
The following list offers the Top 10 Books on Machine Learning with Python for Beginners we recommend you read. Once you’re done, you will have a very solid handle on the field.
What would you be able to anticipate from reading these books on this list?
Machine Learning for Absolute Beginners 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.
Python Machine Learning tackles the challenges data scientists face today when working with artificial intelligence, machine learning and deep learning in Python.
In the book you’ll find out how to unlock trends and patterns in business-critical data with some of the most popular and important techniques for building sophisticated algorithms and statistical models. You’ll also go deeper into the world of predictive analytics as you learn how to not only extract and model data, but also automate processes to uncover detailed insights quickly.
The book adopts a tutorial-based approach to introduce the user to Scikit-learn.If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.
Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code.
The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used.
The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions.
This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.
This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to provide Machine Learning support to their existing projects, and see them get implemented effectively .
We are visual animals. But before we can see the world in its true splendor, our brains, just like our computers, have to sort and organize raw data, and then transform that data to produce new images of the world.
It discusses turning many types of small data sources into useful visual data. And, you will learn Python as part of the bargain.
Author Philipp Janert teaches you how to think about data: how to effectively approach data analysis problems, and how to extract all of the available information from your data.
Janert covers univariate data, data in multiple dimensions, time series data, graphical techniques, data mining, machine learning, and many other topics.
“Machine Learning in Action” is a uniquebook that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis.
It uses the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.
This book covers how to acquire, analyze, and summarize data from all corners of the social web, including Facebook, Twitter, LinkedIn, Google+, GitHub, email, websites, and blogs.
This book is maintained in a public Git Hub repository. It ís designed to be easily accessible through a turnkey virtual machine that facilitates interactive learning with an easy-to-use collection of I-Python Notebooks.
This book is suitable for both an introductory one-semester course and more advanced courses, the text stronglyencourages students to practice with the code.
Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.
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