Machine Learning with Python: A Practical Guide from Fundamentals to Deep Learning for Beginners & Developers

Herbert, Bruce; Publishing, Machine Learning

ISBN 13: 9798252777733

Edité par Independently published, 2026

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N° de réf. du vendeur I-9798252777733

Détails bibliographiques

• Titre: Machine Learning with Python: A Practical Guide from Fundamentals to Deep Learning for Beginners & Developers
• Éditeur: Independently published
• Date de parution: 2026
• Langue: anglais
• ISBN à 13 chiffres: 9798252777733
• Reliure: Couverture souple
• État de l'article: New

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Synopsis

Machine Learning with Python — A Practical Guide for Beginners & Developers

Want to break into Machine Learning and AI using Python, but don’t know where to start?

Machine Learning with Python (2026 Edition) is a complete, beginner-friendly guide that takes you from understanding basic concepts to building real-world machine learning systems using modern tools and frameworks. This book is designed to help you learn step-by-step, without overwhelming theory or unnecessary complexity.

Machine learning is transforming industries — from recommendation systems and fraud detection to self-driving cars and AI assistants. This book shows you how to understand, build, and apply machine learning models using Python’s powerful ecosystem.

What You Will Learn

Inside this book, you will learn how to:

• Understand the fundamentals of machine learning and AI
• Work with real-world datasets and perform data analysis
• Use Python libraries like NumPy, Pandas, Matplotlib, and Scikit-Learn
• Clean, preprocess, and transform data effectively
• Build supervised learning models (Regression, Classification)
• Apply unsupervised learning techniques (Clustering, PCA)
• Evaluate and optimize machine learning models
• Perform feature engineering to improve model performance
• Understand neural networks and deep learning basics
• Explore modern AI concepts like Generative AI and Large Language Models

Learn by Building Real Projects

This book focuses on practical learning, not just theory.

You will work on:

• Real-world datasets and case studies
• Machine learning pipelines and workflows
• Model training, testing, and evaluation
• End-to-end machine learning projects
• Deployment basics and MLOps concepts

By the end of the book, you will be able to build your own machine learning models and apply them to real-world problems.

Designed for Modern AI Learning (2026)

Machine learning is evolving rapidly with the rise of AI assistants, generative models, and large language models (LLMs).

This book introduces you to:

• Generative AI concepts (GANs, VAEs, diffusion models)
• Prompt engineering and AI tools
• Real-world AI applications and industry trends

You will not only learn machine learning — you will understand how modern AI systems work and where the future is heading.

Who This Book Is For

This book is perfect for:

• Beginners entering machine learning and AI
• Python developers expanding into data science
• Students and professionals learning AI skills
• Developers building intelligent applications
• Anyone who wants a practical, real-world approach to ML

Start your journey into Machine Learning and build the skills that power the future of technology.