Who is the target audience for this book?

The target audience includes individuals with little to no prior knowledge of machine learning or programming. It's ideal for students, professionals looking to upskill, or hobbyists interested in data science. The book breaks down complex concepts into manageable sections, ensuring that readers who may feel overwhelmed by technical jargon can still grasp the key ideas and implement machine learning projects effectively.

What programming language does this book focus on?

The primary focus is on Python, a widely-used programming language in machine learning and data science. Python's simplicity and versatility make it an excellent choice for beginners. The book includes code examples and hands-on exercises that help readers familiarize themselves with Python libraries commonly used in machine learning, such as Pandas, NumPy, and Scikit-learn, ensuring practical understanding alongside theoretical knowledge.

What topics are covered in this book?

The book covers various fundamental topics, including the types of machine learning (supervised, unsupervised, reinforcement), model evaluation techniques, and specific algorithms like decision trees and neural networks. Readers will also learn about data preprocessing, feature engineering, and the practical implementation of projects to solidify their understanding. The comprehensive coverage prepares readers to tackle real-world challenges in machine learning.

Can I use this book for self-study?

Absolutely! It is structured to allow for self-paced learning, with clear explanations and practical examples. Readers can follow along at their own speed, enabling them to revisit complex concepts as needed. The exercises and projects included in the book further enhance the self-study experience, providing opportunities to apply what has been learned and build confidence in developing machine learning systems.

Does the book provide practical applications and projects?

Yes, this book includes a variety of practical applications and projects that help reinforce the concepts taught. Through hands-on exercises, readers can implement machine learning models for real-life scenarios, such as predictive analytics or classification tasks. Engaging in these projects not only enhances retention of the material but also builds a portfolio of work that can be showcased to potential employers or for personal growth.

Is prior programming knowledge necessary to understand this book?

Not at all! The book is specifically designed for beginners and assumes no prior programming experience. It introduces readers to programming principles alongside machine learning concepts, ensuring that even those new to coding can follow along. The approachable language and step-by-step instructions make it accessible for individuals coming from various backgrounds, fostering a welcoming environment for all learners.

How does machine learning impact various industries?

Machine learning has transformative effects across many industries including healthcare, finance, retail, and technology. For instance, in healthcare, ML algorithms can analyze patient data to predict outcomes, while in finance, they can detect fraudulent transactions. The skills learned in this book enable readers to innovate within their fields, applying machine learning techniques to improve efficiency, reduce costs, and enhance decision-making processes.

Are there any prerequisites before starting this book?

While there are no formal prerequisites, a basic understanding of mathematics, particularly statistics, can be beneficial. Familiarity with Python is also advantageous, though not required, as the book includes fundamental programming concepts. Readers willing to invest some time in learning these basics will find it easier to grasp more complex topics as they progress through the material.

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Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python (English Edition)

This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature Selection.

Item #: 55013186

Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python (English Edition)

Q: What is 'Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python' about?

This book serves as an introduction to machine learning, specifically designed for beginners. It covers essential concepts and practical applications of machine learning using Python programming. Readers can expect to learn about various algorithms, data preprocessing techniques, and how to develop real-world machine learning models. The step-by-step approach allows for gradual comprehension, making it suitable for those without prior experience in data science or programming.

Q: Where can I buy 'Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python English Edition' in North Macedonia?

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Item #: 55013186

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This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature Selection.

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What Stands Out

Beginner-Friendly

Designed for newcomers, this book simplifies complex machine learning concepts, ensuring a smooth learning curve for those starting with Python and data science.

Hands-On Projects

Includes practical projects that allow readers to apply their knowledge in real-world scenarios, enhancing understanding and retention of machine learning techniques.

Comprehensive Resource

Covers essential topics and tools in machine learning, making it a valuable reference for ongoing learning and development well beyond initial exposure.

Product Details

Publisher	BPB Publications
Publication date	August 21, 2020
Language	English
Print length	262 pages
ISBN-10	9389845424
ISBN-13	978-9389845426
Item Weight	1.01 pounds (460 grams)
Dimensions	7.5 x 0.6 x 9.25 inches (19.1 x 1.5 x 23.5 cm)

Who Should Buy?

Aspiring Data Scientists
Ideal for individuals looking to start a career in data science with practical tools and methods.
Programming Enthusiasts
Great for those with a Python background who want to explore machine learning concepts in depth.
Students and Learners
Perfect for students seeking introductory resources to understand machine learning and its applications.

Advanced Practitioners
Not suitable for experienced data scientists who require in-depth, complex methodologies beyond foundational principles.

Product Description

Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python (English Edition)

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Python Editorial Review

Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python English Edition is an insightful book that offers a beginner's guide to building machine learning systems using Python. The author, Mr Bhasin, has done an excellent job of explaining the topic in a clear, concise, and easy-to-understand manner. This book not only covers general to advanced topics of machine learning, but it also explains the working of algorithms with the help of required mathematics and implementation of algorithms in Python. The book is a must-read for both beginner and experienced machine learning professionals who want to brush up their concepts. With a good balance of theory and practical implementation, the book provides an excellent resource for individuals who want to learn about machine learning.

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Pros

Clear, concise, and easy-to-understand manner
Good balance of theory and practical implementation
Covers general to advanced topics of machine learning
Explains the working of algorithms with the help of required mathematics and implementation of algorithms in Python

Cons

One review mentioned that the book felt like a random class notes and suggested improvements in quality and content

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Features & Benefits

Learn important concepts and topics in Machine Learning.
Get an overview of Feature Selection and Data Cleansing
Learn about training and testing, and cross-validation
Covers common Classification and Regression techniques
Explains implementation of Neural Networks and Support Vector Machines
Includes techniques in Unsupervised Learning

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Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python (English Edition)

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