The following list offers the Top 15 Best Python Machine Learning Books for Beginners I recommend you to 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?
The criteria I used to collect together in this list are:
All-Round Creation, Very Practical, Generative, Affordable, Authorship, Highest Reviewed, Stimulates the mind & imagination, Practical Problems, Career Opportunities, Highest Rated, Can be read over & over, Best Sellers, Newest, Without Bias.
Without encourage due, Let’s Get Started!
The Top 15 Best Books On Machine Learning with Python You Should Read Now
1.) Machine Learning For Absolute Beginners: A Plain English Introduction
This major new edition features many topics not covered in the First Edition, including Cross Validation, Ensemble Modeling, Grid Search, Feature Engineering, and One-hot Encoding.
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.
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2.) Python Machine Learning, 1st Edition
This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. You will learn all the important concepts such as exploratory data analysis, data pre-processing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation.
With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought.
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3.) Learning scikit-learn: Machine Learning in Python
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.
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4.) Machine Learning in Python: Essential Techniques for Predictive Analysis
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.
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5.) Building Machine Learning Systems with Python
This book is a scenario-based, example-driven tutorial. By the end of the book, you will have learned the 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.
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6.) Beginning Python Visualization: Crafting Visual Transformation Scripts (Books for Professionals by Professionals)
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.
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7.) Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists
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.
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8.) Machine Learning in Action
“Machine Learning in Action” is a unique book 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.
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9.) Mining The Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More, Second Edition
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 GitHub repository. It ís designed to be easily accessible through a turnkey virtual machine that facilitates interactive learning with an easy-to-use collection of IPython Notebooks.
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10.) Machine Learning: An Algorithmic Perspective – Stephen Marsland
This book is suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages 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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11.) Advance Machine Learning in Python
If you’ve ever considered building your own image or text-tagging solution, or of entering a Kaggle contest, for instance, this book is for you!
Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful.
A practical book that tackles real-world computing problems through a rigorous and effective approach.
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12.) MACHINE LEARNING WITH PYTHON- An Approach to Applied Machine Learning
This book provides a concept of machine learning with mathematical explanation and programming examples. Every chapter starts with the fundamentals of the technique and working example on real world datasets. Along with the advice on applying algorithms, each technique is provided with advantages and disadvantages to the data.
This book provides code examples in python. It contains a lot of libraries, so you don’t need to code everything. Also, it is scalable for a large amount of data and suitable for big data technologies.
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13.) Machine Learning for Hackers
This book will get you started with machine learning toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies.
Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.
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13.) Vital Introduction to Machine Learning with Python: Best Practices to Improve and Optimize Machine Learning Systems and Algorithms
This book serves as a comprehensive intermediate guide to programming in SQL using Python as a language.
Every new programmer needs a reference guide to coding and programming as they learn the basics. The goal of this book is to allow for the reader to get a step by step, comprehensive guide to formulating and gaining new skills.
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14.) Introduction to Machine Learning with Python: A Guide for Data Scientists
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams.
If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.
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14.) Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications
This Book A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices.
Learn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering, Master open source Python tools to build sophisticated predictive models.
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15.) Data Mining: Practical Machine Learning Tools and Techniques
This book covers all the recent changes and modernization of techniques of data mining. You will find extra material on ensemble learning, data transformation, massive data sets and multi-instance learning.
The book also includes Weka Software for machine learning that the authors have developed.
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FINAL WORDS
I seek this article will work for you. I mean picking a book with no direction is extremely a headache. Especially when you are looking for the best book to learn python for Machine Learning because this is a very specific topic.
I have encountered that nothing can replace a decent book. These books are very practical.
If you are new or Intermediate level Machine learning student. Reading a total book will give you expertise and confidence.
Note: Start Understanding these books One by One. Perhaps, a $5-10 book can change your Career for time everlasting.
Books changed my life in recent years. I was forced to peruse books in my formal education and avoided perusing books in my corporate activity.
In any case, now my life relies upon books so to speak.
The above book lists are some of most loved books that I would recommend you to enhance your career, a better person and a better individual.
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