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Machine Learning, Python & Data Analytics Essentials: Master AI, Data Science, and Analytics for Career Growth | Comprehensive Combo for Beginners & Professionals (Set of 3 Books)
Estimated delivery between May 31 and June 02.
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Machine Learning, Python & Data Analytics Essentials: Master AI, Data Science, and Analytics for Career Growth | Comprehensive Combo for Beginners & Professionals (Set of 3 Books)
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Most data science journeys start with Python, move into data analytics, and lead to machine learning. This combo brings all three together in one easy-to-follow package:
- Python Essentials gives you coding confidence from scratch
- Data Analytics Essentials teaches you how to find insights in data
- Machine Learning Essentials shows you how to make predictions and automate decisions
What each book offers?
Book 1: Machine Learning Essentials You Always Wanted to Know
With this book, you will:
- Learn how data fuels modern machine learning systems, from training algorithms to making accurate predictions
- Tackle complex topics like deep learning and neural networks—made easy with clear, beginner-friendly explanations
- Discover how ML is applied in the real world, with examples from industries like healthcare, education, and more
Click here to learn more about the book
Book 2: Python Essentials You Always Wanted to Know
With this book, you will:
- Gain a solid foundation in Python, making it easier to progress to more advanced topics with simple and step-by-step explanations
- Get a deep understanding of object-oriented programming principles, which are vital skills for writing clean, scalable, and reusable Python code
- Stay ahead of the curve by diving into exciting fields like AI, Machine Learning, and IoT with Python
Click here to learn more about the book
Book 3: Data Analytics Essentials You Always Wanted to Know
With this book, you will:
- Get a clear understanding of key concepts, terminology, and the evolution of data analytics
- Understand the transformative role of Big Data and how it reshapes industries globally
- Explore how industry giants like Walmart and Netflix use data analytics to drive business decisions
Pages: 792 pages
Paperback (ISBN): 9781636516028
Category: Business & Economics
Author: Dhairya Parikh, Shawn Peters, Dr. Bianca Szasz, Vibrant Publishers
Click on individual book titles below to view their complete Table of Contents.
Machine Learning Essentials You Always Wanted to Know
Dhairya Parikh is a seasoned data engineer, a graduate of the University of Waterloo, and a technical writer with expertise in AI, data science, and practical ML applications.
Shawn Peters has 19 years of teaching experience, is certified in Python Programming Teaching from the College of the North Atlantic, and also specializes in JavaScript and Java.
Dr. Bianca Szasz is a Ph.D. holder in Space Engineering. In over 14 years of experience in engineering and a dedicated focus of 4 years in data analytics, she has used data analytics in a variety of innovative projects, like post-processing of the wind tunnel test results and the analysis of high enthalpy heating test results. Her enthusiasm for data analytics eventually expanded beyond using it for work. Now she is passionate about educating the future generation of data analysts.
Vibrant Publishers is focused on presenting the best texts for learning about technology and business as well as books for test preparation. Categories include programming, operating systems and other texts focused on IT. In addition, a series of books helps professionals in their own disciplines learn the business skills needed in their professional growth.
Vibrant Publishers has a standardized test preparation series covering the GMAT, GRE and SAT, providing ample study and practice material in a simple and well organized format, helping students get closer to their dream universities.
The Self-Learning Management Series is designed to help students, new managers, career switchers, and entrepreneurs learn essential management lessons and covers every aspect of business, from HR to Finance to Marketing to Operations across any and every industry. Each book includes basic fundamentals, important concepts, and standard and well-known principles as well as practical ways of application of the subject matter.
This book offers a beginner-friendly approach to learning Python, focusing not only on syntax but also on problem-solving, which is key for effective programming. The author’s clear intent is to make coding enjoyable and relevant, providing examples that are simple yet significant to help readers follow along. The emphasis is on applying coding skills practically, with quizzes and case studies included to solidify understanding. By the end, readers will grasp essential concepts such as Python syntax, data structures, error handling, and object-oriented programming, all while gaining the confidence to solve real-world problems. Having an understanding of these concepts, you will be well-prepared to tackle more advanced Python topics and apply your skills to real-life coding challenges.
-- LooYee NG, Solutions Architect at CTMG
I think this text is a comprehensive yet succinct manual. It gives a usable introduction to data analytics and is easy to read and comprehend. An added benefit is that the book contains real-world case studies and interesting facts to help enhance existing knowledge.
A key strength of this text is the option to choose the topics that you want to focus on and the resources that you want to use. This flexibility makes self-directed learning a great option for busy professionals who want to add data analytics skills to their repertoire. If you're interested in self-directed learning for data analytics, there are a few things you need to do to get started, and this text will help!
-- David E Reva Instructor - CIS, Kalamazoo Valley Community College
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I've spent two years trying to piece together a self-study curriculum across free tutorials, YouTube channels, and disconnected books. This three-book set gave me what all of that couldn't — a coherent, structured path from coding to analytics to machine learning. Python Essentials built my programming foundation cleanly, covering OOP principles and error handling before moving into AI and IoT applications. Data Analytics Essentials showed me how real companies like Walmart and Netflix use data to drive decisions. Machine Learning Essentials then gave me the algorithmic understanding and hands-on coding exercises to build and implement ML models. The logical flow across all three books is the set's greatest strength.
I'm a final-year computer science engineering student and my elective paper required self-study across Python, data analytics, and machine learning. This set covered all three topics at exactly the right depth for an advanced undergraduate — rigorous enough to be genuinely educational, accessible enough to not require a faculty guide for every chapter. The Big Data section in Data Analytics Essentials and the deep learning coverage in Machine Learning Essentials were both stronger than what my course notes provided. Dr. Bianca Szasz's engineering background gives the data analytics book a precision that purely business-focused analytics books often lack. An excellent academic supplement at a very reasonable price for three books.
I'm a busy professional with limited study time and can't always work through books linearly. One of the strengths of this trilogy — particularly Data Analytics Essentials — is how well it supports topic-focused, self-directed study. The chapter structure allows you to go deep on specific areas without having read everything before it. Python Essentials and Machine Learning Essentials are also modular enough to revisit specific concepts without re-reading entire sections. The expert editorial reviewer from Kalamazoo Valley Community College specifically mentioned this flexibility, and it's something I've found genuinely useful in practice. A thoughtfully structured set for working professionals who learn in bursts rather than blocks.
Most data analytics books feel abstract because they use generic or fictional examples. The inclusion of how industry giants like Walmart and Netflix actually use data analytics to drive business decisions in Data Analytics Essentials makes the content feel immediately relevant and real. It shifted my thinking from "data analytics as a technical skill" to "data analytics as a business capability." Python Essentials gave me the coding tools to work with data, and Machine Learning Essentials showed me how those tools power predictive systems. A 4 rather than a 5 because I'd have liked more advanced Python data science libraries covered, but overall this is a strong and well-matched set.
I work in IT support and have been asked by several colleagues how to break into data science. Previously I gave them a list of different resources. Now I just recommend this set. Python Essentials by Shawn Peters — with 19 years of teaching behind it — is the most beginner-friendly Python book I've come across. Data Analytics Essentials covers the conceptual landscape of data analytics clearly, including the Big Data transformation that's reshaping every industry. Machine Learning Essentials makes supervised and unsupervised learning genuinely understandable through real-world examples from healthcare and education. The chapter quizzes across all three books help with retention without feeling like busywork.
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