Machine Learning With Python For Everyone | By Mark Fenner | 1st Edition | Pearson Publication ( English Medium )

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Machine Learning With Python For Everyone | By Mark Fenner  | 1st Edition | Pearson Publication ( English Medium ).

Book Description:

“Machine Learning with Python for Everyone” by Mark Fenner is an accessible and practical introduction to machine learning, designed for readers of all backgrounds who want to learn how to build intelligent applications using Python. The book emphasizes hands-on learning, guiding readers through fundamental machine learning concepts, techniques, and algorithms, with practical coding examples.

Unlike books focused on heavy mathematics or theory, this book takes a user-friendly approach that enables beginners and professionals alike to apply machine learning methods effectively. It covers a broad range of supervised and unsupervised learning algorithms, data preprocessing, evaluation methods, and model deployment.

The book provides clear explanations combined with Python code snippets, empowering readers to quickly implement machine learning workflows using popular libraries like scikit-learn and pandas. It is suitable for students, data enthusiasts, and professionals aiming to develop machine learning skills for real-world problem solving.

Key Features:

Beginner Friendly: No prior experience in machine learning required; easy-to-understand explanations.

Hands-on Approach: Practical Python code examples for immediate application.

Wide Coverage: Includes supervised learning (regression, classification), unsupervised learning (clustering), and evaluation techniques.

Focus on Python: Uses popular libraries such as scikit-learn, pandas, and matplotlib.

Data Preparation: Covers data cleaning, feature engineering, and preprocessing.

Model Evaluation: Explains metrics and methods for assessing model performance.

Clear Illustrations: Visual explanations of concepts and algorithms.

Real-world Applications: Examples across diverse domains.

Step-by-step Tutorials: Guides readers through building machine learning models from scratch.

Project-based Learning: Encourages building projects to reinforce concepts.

Students are crushing to master powerful machine learning techniques for improving decision-making and scaling analysis to immense datasets. Machine learning with Python for everyone brings together all they'll need to succeed: a practical understanding of the machine learning process, accessible code, skills for implementing that process with Python and the scikit-learn library, and real expertise in using learning systems intelligently.Reflecting 20 years of experience teaching non-specialists, the author teaches through carefully-crafted datasets that are complex enough to be interesting, but simple enough for non-specialists. Building on this foundation, the book presents real-world case studies that apply his lessons in detailed, nuanced ways. Throughout, he offers clear narratives, practical “code-alongs,” and easy-to-understand images -- focusing on Mathematics only where it’s necessary to make connection and deepen insight. table of Contents: Chapter 

1: Let’s discuss learning Chapter 2: predicting categories: getting started with classification Chapter 3: predicting numerical values: getting started with regression Chapter 4: evaluating and comparing learners Chapter 5: evaluating classifiers Chapter 6: evaluating Regressors Chapter 7: more classification methods Chapter 8: more regression methods Chapter 9: manual feature engineering: manipulating data for fun and Profit Chapter 10: models that engineer features for us Chapter 11: feature engineering for domains: domain-specific learning online chapters Chapter 12: tuning hyperparameters and pipelines Chapter 13: combining learners Chapter 14: connecting, extensions, and further

About the Author

Dr. Mark Fenner, owner of Fenner Training and Consulting, LLC, has taught computing and mathematics to diverse adult audiences since 1999, and holds a PhD in computer science. His research has included design, implementation, and performance of machine learning and numerical algorithms; developing learning systems to detect user anomalies; and probabilistic modeling of protein function.

Search Key - Machine Learning With Python For Everyone | By Mark Fenner  | 1st Edition | Pearson Publication ( English Medium ), Publisher ‏ : ‎ Pearson Education; First edition, Language ‏ : ‎ English, Paperback ‏ : ‎ 504 pages, ISBN-10 ‏ : ‎ 9353944902, Machine Learning With Python For Everyone, Artificial Intelligence, Electrical & Electronic Engineering, Electrical Engineering, Electronic Engineering, Engineering, Engineering Book, Higher Studies, College Book, University Book, Higher Education.

Product Details

SKU / BOOK Code:	Prs-Macn-Lrng-With-Pthn-For-Evryne-(E)
Publisher:	Pearson Publication
Author:	Mark Fenner
Binding Type:	Paperback
No. of Pages:	504
ISBN-10:	9353944902
ISBN-13:	978-9353944902
Edition:	1st Edition
Language:	English Medium
Publish Year:	2025-06
Weight (g):	2000
Product Condition:	New
Reading Age:	Above 10 Years
Country of Origin:	India
Genre:	Textbooks & Study Guides
Manufacturer:	Pearson Publication
Importer:	Pearson Publication
Packer:	Fullfilled by Supplier

Product Description

Machine Learning With Python For Everyone | By Mark Fenner  | 1st Edition | Pearson Publication ( English Medium ).

Book Description:

“Machine Learning with Python for Everyone” by Mark Fenner is an accessible and practical introduction to machine learning, designed for readers of all backgrounds who want to learn how to build intelligent applications using Python. The book emphasizes hands-on learning, guiding readers through fundamental machine learning concepts, techniques, and algorithms, with practical coding examples.

Unlike books focused on heavy mathematics or theory, this book takes a user-friendly approach that enables beginners and professionals alike to apply machine learning methods effectively. It covers a broad range of supervised and unsupervised learning algorithms, data preprocessing, evaluation methods, and model deployment.

The book provides clear explanations combined with Python code snippets, empowering readers to quickly implement machine learning workflows using popular libraries like scikit-learn and pandas. It is suitable for students, data enthusiasts, and professionals aiming to develop machine learning skills for real-world problem solving.

Key Features:

Beginner Friendly: No prior experience in machine learning required; easy-to-understand explanations.

Hands-on Approach: Practical Python code examples for immediate application.

Wide Coverage: Includes supervised learning (regression, classification), unsupervised learning (clustering), and evaluation techniques.

Focus on Python: Uses popular libraries such as scikit-learn, pandas, and matplotlib.

Data Preparation: Covers data cleaning, feature engineering, and preprocessing.

Model Evaluation: Explains metrics and methods for assessing model performance.

Clear Illustrations: Visual explanations of concepts and algorithms.

Real-world Applications: Examples across diverse domains.

Step-by-step Tutorials: Guides readers through building machine learning models from scratch.

Project-based Learning: Encourages building projects to reinforce concepts.

Students are crushing to master powerful machine learning techniques for improving decision-making and scaling analysis to immense datasets. Machine learning with Python for everyone brings together all they'll need to succeed: a practical understanding of the machine learning process, accessible code, skills for implementing that process with Python and the scikit-learn library, and real expertise in using learning systems intelligently.Reflecting 20 years of experience teaching non-specialists, the author teaches through carefully-crafted datasets that are complex enough to be interesting, but simple enough for non-specialists. Building on this foundation, the book presents real-world case studies that apply his lessons in detailed, nuanced ways. Throughout, he offers clear narratives, practical “code-alongs,” and easy-to-understand images -- focusing on Mathematics only where it’s necessary to make connection and deepen insight. table of Contents: Chapter 

1: Let’s discuss learning Chapter 2: predicting categories: getting started with classification Chapter 3: predicting numerical values: getting started with regression Chapter 4: evaluating and comparing learners Chapter 5: evaluating classifiers Chapter 6: evaluating Regressors Chapter 7: more classification methods Chapter 8: more regression methods Chapter 9: manual feature engineering: manipulating data for fun and Profit Chapter 10: models that engineer features for us Chapter 11: feature engineering for domains: domain-specific learning online chapters Chapter 12: tuning hyperparameters and pipelines Chapter 13: combining learners Chapter 14: connecting, extensions, and further

About the Author

Dr. Mark Fenner, owner of Fenner Training and Consulting, LLC, has taught computing and mathematics to diverse adult audiences since 1999, and holds a PhD in computer science. His research has included design, implementation, and performance of machine learning and numerical algorithms; developing learning systems to detect user anomalies; and probabilistic modeling of protein function.

Search Key - Machine Learning With Python For Everyone | By Mark Fenner  | 1st Edition | Pearson Publication ( English Medium ), Publisher ‏ : ‎ Pearson Education; First edition, Language ‏ : ‎ English, Paperback ‏ : ‎ 504 pages, ISBN-10 ‏ : ‎ 9353944902, Machine Learning With Python For Everyone, Artificial Intelligence, Electrical & Electronic Engineering, Electrical Engineering, Electronic Engineering, Engineering, Engineering Book, Higher Studies, College Book, University Book, Higher Education.

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