Comprehensive guide to mastering Python and machine learning, with hands-on examples and practical projects.

Key Features

• Comprehensive Python 3 coverage
• Detailed machine learning algorithms
• Hands-on examples and projects

Book Description

This book introduces basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter covers fundamental machine learning concepts. The sixth chapter dives into machine learning classifiers, such as logistic regression, k-NN, decision trees, random forests, and SVMs. The final chapter includes material on natural language processing (NLP) and reinforcement learning (RL). Keras-based code samples supplement the theoretical discussion.The course begins with Python basics, including conditional logic, loops, functions, and collections. It then explores data manipulation with NumPy and Pandas. The journey continues with an introduction to machine learning, focusing on essential concepts and classifiers. Advanced topics like NLP and RL are covered, ensuring a comprehensive understanding of machine learning.These concepts are crucial for developing machine learning applications. This book transitions readers from basic Python programming to advanced machine learning techniques, blending theory with practical skills. Appendices for regular expressions, Keras, and TensorFlow 2, along with companion files, enhance learning, making this an essential resource for mastering Python and machine learning.

What you will learn

• Master Python basics and advanced techniques
• Implement efficient loops and functions
• Use Python collections for data handling
• Manipulate data with NumPy and Pandas
• Build and evaluate machine learning models
• Apply NLP and reinforcement learning techniques

Who this book is for

Ideal for aspiring data scientists, machine learning enthusiasts, and Python programmers. No prior knowledge of Python or machine learning is required, making it suitable for beginners. Basic programming understanding is helpful.

Comprehensive guide to mastering Python and machine learning, with hands-on examples and practical projects.

Key Features

• Comprehensive Python 3 coverage
• Detailed machine learning algorithms
• Hands-on examples and projects

Book Description

This book introduces basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter covers fundamental machine learning concepts. The sixth chapter dives into machine learning classifiers, such as logistic regression, k-NN, decision trees, random forests, and SVMs. The final chapter includes material on natural language processing (NLP) and reinforcement learning (RL). Keras-based code samples supplement the theoretical discussion.The course begins with Python basics, including conditional logic, loops, functions, and collections. It then explores data manipulation with NumPy and Pandas. The journey continues with an introduction to machine learning, focusing on essential concepts and classifiers. Advanced topics like NLP and RL are covered, ensuring a comprehensive understanding of machine learning.These concepts are crucial for developing machine learning applications. This book transitions readers from basic Python programming to advanced machine learning techniques, blending theory with practical skills. Appendices for regular expressions, Keras, and TensorFlow 2, along with companion files, enhance learning, making this an essential resource for mastering Python and machine learning.

What you will learn

• Master Python basics and advanced techniques
• Implement efficient loops and functions
• Use Python collections for data handling
• Manipulate data with NumPy and Pandas
• Build and evaluate machine learning models
• Apply NLP and reinforcement learning techniques

Who this book is for

Ideal for aspiring data scientists, machine learning enthusiasts, and Python programmers. No prior knowledge of Python or machine learning is required, making it suitable for beginners. Basic programming understanding is helpful.

Comprehensive guide to mastering Python and machine learning, with hands-on examples and practical projects.

Key Features

• Comprehensive Python 3 coverage
• Detailed machine learning algorithms
• Hands-on examples and projects

Book Description

This book introduces basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter covers fundamental machine learning concepts. The sixth chapter dives into machine learning classifiers, such as logistic regression, k-NN, decision trees, random forests, and SVMs. The final chapter includes material on natural language processing (NLP) and reinforcement learning (RL). Keras-based code samples supplement the theoretical discussion.The course begins with Python basics, including conditional logic, loops, functions, and collections. It then explores data manipulation with NumPy and Pandas. The journey continues with an introduction to machine learning, focusing on essential concepts and classifiers. Advanced topics like NLP and RL are covered, ensuring a comprehensive understanding of machine learning.These concepts are crucial for developing machine learning applications. This book transitions readers from basic Python programming to advanced machine learning techniques, blending theory with practical skills. Appendices for regular expressions, Keras, and TensorFlow 2, along with companion files, enhance learning, making this an essential resource for mastering Python and machine learning.

What you will learn

• Master Python basics and advanced techniques
• Implement efficient loops and functions
• Use Python collections for data handling
• Manipulate data with NumPy and Pandas
• Build and evaluate machine learning models
• Apply NLP and reinforcement learning techniques

Who this book is for

Ideal for aspiring data scientists, machine learning enthusiasts, and Python programmers. No prior knowledge of Python or machine learning is required, making it suitable for beginners. Basic programming understanding is helpful.