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Master Machine Learning & Python: A Beginner’s Guide to ML Algorithms, Python Programming Basics, Supervised Learning, AI Essentials, Hands-On Coding & Real-World Applications (Set of 2 Books)
Estimated delivery between May 31 and June 02.
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Master Machine Learning & Python: A Beginner’s Guide to ML Algorithms, Python Programming Basics, Supervised Learning, AI Essentials, Hands-On Coding & Real-World Applications (Set of 2 Books)
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Python is the foundation of machine learning—and this combo helps you master both in the right sequence:
- Start mastering Python programming step by step
- Move on to applying your skills to real ML problems
- Learn concepts through practical examples and simplified theory
- Transition smoothly from beginner to confident ML coder
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
- Understand the core concepts of supervised and unsupervised learning, including key techniques like regression and classification
- 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
- Learn how to handle errors and exceptions, making sure your code runs smoothly and efficiently
- Stay ahead of the curve by diving into exciting fields like AI, Machine Learning, and IoT with Python
Pages: 574 pages
Paperback (ISBN): 9781636516035
Category: Business & Economics
Author: Dhairya Parikh, Shawn Peters, Vibrant Publishers
Click on individual book titles below to view their complete Table of Contents.
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.
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.
What a great introduction to this topic, especially for a non-programmer like myself! The examples provided are easy to understand and apply, allowing me to try it out for myself to further cement my understanding of the steps involved. Read, See, Do. A great way to learn. I really enjoyed the quizzes at the end of each chapter, as they emphasized what the important takeaways were and reinforced what I did know - and what I didn’t. I’ll probably never become a programmer, but understanding how programming works is an asset to any user.
-- Sharon Peach B.Sc, B.Ed, M.Ed
This beginner-friendly guide makes mastering this versatile language accessible for everyone. It provides clear explanations and practical examples while avoiding technical jargon, making it perfect for absolute beginners and those at various skill levels. Even as someone who isn’t usually into programming, I found it easy to follow!
-- Samantha Nichols
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I work as a business analyst in Mumbai and enrolled in an evening upskilling program focused on AI tools. This bundle was assigned as supplementary reading and it turned out to be the most useful resource of the course. The ML book's real-world examples from healthcare and education made abstract concepts stick, while the Python volume's chapter on handling exceptions made my early coding attempts far less frustrating. The progression from Python basics to ML applications feels natural rather than forced. A solid four-star bundle with a lot of practical value.
I run a coding bootcamp in Istanbul focused on preparing students for data roles, and finding resources that cover both Python fundamentals and ML concepts without requiring prior programming experience is always a challenge. This bundle solves that problem neatly. Students who worked through the Python Essentials book first came into our ML modules significantly better prepared than previous cohorts. The sections on supervised learning, regression, and classification in the ML volume align well with industry expectations. A dependable, well-authored set at a reasonable price point.
I was switching careers from marketing into tech and had no idea where to begin. This two-book bundle gave me exactly the structured foundation I needed. The Python Essentials volume walks through object-oriented programming, error handling, and real-world coding exercises in a genuinely beginner-friendly way. The Machine Learning Essentials book then builds on that base, covering supervised and unsupervised learning, decision trees, and KNN without overwhelming jargon. Shawn Peters' teaching background really shows — the pacing is excellent.
As a corporate trainer in Bengaluru who designs upskilling programs for IT professionals, I am always on the lookout for resources that balance theory with application. This set of two books does that admirably. The ML Essentials volume's treatment of ensemble methods like bagging and boosting is clear and concise, and the Python volume's section on OOP principles helps learners write cleaner code from day one. I have already recommended this bundle to two junior analyst cohorts at our firm. My only minor wish is more extended coding projects at the end of each chapter.
Dhairya Parikh's expertise as a University of Waterloo-trained data engineer comes through clearly in the Machine Learning volume. The explanations of how data fuels training algorithms and how predictions are evaluated are among the clearest I have read at this level. The Python book by Shawn Peters is equally strong — his 19 years of teaching experience are evident in how logically each concept builds on the last. I used this bundle as a self-study resource alongside my graduate coursework and it filled in several conceptual gaps quickly. Highly recommended for university students.
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