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C O M P U T E R V I S I O N : F O U N D AT I O N S A N D A P P L I C AT I O N S

R A N J AY K R I S H N A

A foundational study of computer vision and image processing, covering how computers extract meaningful information from digital images and understand visual content. The material introduces core concepts such as pixels, color, linear algebra for vision, filtering, convolution, edge detection, feature detection, image segmentation, image recognition, object detection, tracking, and image manipulation. It emphasizes implementing computer-vision algorithms through practical programming projects and explores applications such as robotics, autonomous vehicles, medical imaging, image search, and augmented reality.

Computer Vision Deep Learning

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Neural Networks

StanFord University

A comprehensive introduction to neural networks and their role in machine learning and artificial intelligence. The material explains fundamental concepts such as artificial neurons, network architectures, weights, activation functions, learning algorithms, and backpropagation. It also introduces how neural networks can be trained to solve classification and prediction problems, providing a foundation for understanding modern deep learning techniques.

Deep Learning Neural Networks

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scikit-learn user guide

scikit-learn developers

A comprehensive technical guide to scikit-learn, covering the tools and techniques needed to build machine learning models in Python. It includes supervised and unsupervised learning, classification, regression, clustering, dimensionality reduction, preprocessing, feature selection, model evaluation, cross-validation, hyperparameter tuning, pipelines, and dataset utilities. The guide also provides practical examples and API references for implementing machine learning workflows with scikit-learn

Scikit-learn Python

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Deep Learning with PyTorch

Eli Stevens ,Luca Antiga ,Thomas Viehmann

A practical guide to building and training deep learning models using PyTorch. The book introduces PyTorch tensors, neural networks, data loading, model training, loss functions, monitoring, and visualization through hands-on projects. It focuses on applying deep learning to real-world problems, including image classification and medical imaging, while teaching techniques such as data augmentation, model improvement, and fine-tuning.

Deep Learning Python Pytorch

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Introduction to Machine Learning with Python

Andreas C. Müller & Sarah Guido

A practical, beginner-friendly guide to understanding and applying Machine Learning using Python and scikit-learn. The book covers fundamental ML concepts, supervised and unsupervised learning, data preprocessing, feature engineering, model evaluation, parameter tuning, pipelines, and working with text data. It emphasizes hands-on implementation and real-world workflows rather than focusing heavily on mathematical theory, making it useful for learners beginning their journey in Machine Learning and Data Science.

ML(Machine Learning) Python

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Artificial Intelligence A Modern Approach Third Edition

Stuart Russell and Peter Norvig

Artificial Intelligence: A Modern Approach, Third Edition by Stuart Russell and Peter Norvig is a comprehensive textbook covering the theory and practical foundations of Artificial Intelligence. It explores intelligent agents, problem-solving and search, logical reasoning, knowledge representation, probabilistic reasoning, machine learning, reinforcement learning, natural language processing, computer vision, robotics, and AI planning. The book presents AI through an agent-based approach, combining theoretical concepts with algorithms, examples, and exercises.

AI ML(Machine Learning) Deep Learning Robotics

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Learning Python, Fourth Edition

Mark Lutz

Learning Python, Fourth Edition by Mark Lutz is a comprehensive, self-paced guide to learning the core Python programming language. It covers Python’s built-in data types, statements, functions, modules, object-oriented programming, exception handling, and advanced language features. The book combines detailed explanations, practical examples, quizzes, and exercises to help learners develop a strong understanding of Python fundamentals. The fourth edition primarily focuses on Python 3.0 while also explaining differences with Python 2.6

Python Programming Python Basics OOPS Meta Class

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Python Programing

Hans-Petter Halvorsen

Python Programming by Hans-Petter Halvorsen is a practical and beginner-friendly resource for learning Python programming. It covers fundamental concepts such as variables, data types, operators, conditional statements, loops, functions, modules, classes, file handling, error handling, and Python libraries. The book combines clear explanations with practical examples and exercises, helping learners build strong programming fundamentals and apply Python to real-world problems.

Python Programming Python Basics Built-in Functions Data Visualization Numerical Computing

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