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Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python (English Edition)
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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.
Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python (English Edition)
Vare #: 55013186

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

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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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Hvad der skiller sig ud

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.

Produktdetaljer

Discover how to build machine learning systems using Python with Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python English Edition at Ubuy Greenland.
  • Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms Key FeaturesUnderstand the types of Machine learning. Get familiar with different Feature extraction methods. Get an overview of how Neural Network Algorithms work.Learn how to implement Decision Trees and Random Forests. The book not only explains the Classification algorithms but also discusses the deviations/ mathematical modeling.Description 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. The book covers algorithms and implementations of the most common Feature Selection Techniques. The book then focuses on Linear Regression and Gradient Descent. Some of the important Classification techniques such as K-nearest neighbors, logistic regression, Naïve Bayesian, and Linear Discriminant Analysis are covered in the book. It then gives an overview of Neural Networks and explains the biological background, the limitations of the perceptron, and the backpropagation model. The Support Vector Machines and Kernel methods are also included in the book. It then shows how to implement Decision Trees and Random Forests. Towards the end, the book gives a brief overview of Unsupervised Learning. Various Feature Extraction techniques, such as Fourier Transform, STFT, and Local Binary patterns, are covered. The book also discusses Principle Component Analysis and its implementation. What will you learnLearn how to prepare Data for Machine Learning.Learn how to implement learning algorithms from scratch.Use scikit-learn to implement algorithms.Use various Feature Selection and Feature Extraction methods.Learn how to develop a Face recognition system. Who this book is for The book is designed for Undergraduate and Postgraduate Computer Science students and for the professionals who intend to switch to the fascinating world of Machine Learning. This book requires basic know-how of programming fundamentals, Python, in particular.Table of Contents 1. An introduction to Machine Learning 2. The beginning: Pre-Processing and Feature Selection 3. Regression 4. Classification 5. Neural Networks- I 6. Neural Networks-II 7. Support Vector machines 8. Decision Trees 9. Clustering 10. Feature Extraction Appendix A1. Cheat Sheets A2. Face Detection A3.Biblography About the Author Harsh Bhasin is an Applied Machine Learning researcher. Mr. Bhasin worked as Assistant Professor in Jamia Hamdard, New Delhi, and taught as a guest faculty in various institutes including Delhi Technological University. Before that, he worked in C# Client-Side Development and Algorithm Development.Mr. Bhasin has authored a few papers published in renowned journals including Soft Computing, Springer, BMC Medical Informatics and Decision Making, AI and Society, etc. He is the reviewer of prominent journals and has been the editor of a few special issues. He has been a recipient of a distinguished fellowship.Outside work, he is deeply interested in Hindi Poetry, progressive era; Hindustani Classical Music, percussion instruments.His areas of interest include Data Structures, Algorithms Analysis and Design, Theory of Computation , Python, Machine Learning and Deep learning. Your LinkedIn Profile:https://in.linkedin.com/in/harsh-bhasin-69134426
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)

Hvem bør købe?

Suitable For
  • 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.

Not Suitable For
  • Advanced Practitioners

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

Produktbeskrivelse

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Kundespørgsmål og svar

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

    svar: 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.
  • spørgsmål: Who is the target audience for this book?

    svar: 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.
  • spørgsmål: What programming language does this book focus on?

    svar: 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.
  • spørgsmål: What topics are covered in this book?

    svar: 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.
  • spørgsmål: Can I use this book for self-study?

    svar: 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.
  • spørgsmål: Does the book provide practical applications and projects?

    svar: 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.
  • spørgsmål: Is prior programming knowledge necessary to understand this book?

    svar: 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.
  • spørgsmål: How does machine learning impact various industries?

    svar: 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.
  • spørgsmål: Are there any prerequisites before starting this book?

    svar: 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.
  • spørgsmål: Where can I buy 'Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python English Edition' in Greenland?

    svar: You can purchase 'Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python English Edition' from Ubuy, a trusted online platform that offers a variety of books and resources. Their user-friendly interface makes it easy to find this book and other educational materials that can help you in your learning journey. Ubuy also provides options for browsing through related products that complement your reading experience.

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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  • 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

Ulemper

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

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Egenskaber og fordele

  • 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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