In the field of data science, Python stands out as a popular and flexible programming language. Numerous books can assist you in exploring new topics, picking up new abilities, and performing better—regardless of your level of experience. These ten Python data science books, which cover a variety of topics in computer vision, natural language processing, deep learning, machine learning, and data analysis, are essential reading for 2024.
1. Python Data Science Handbook: Using Python, this book provides an extensive overview of the fundamental tools and methods of data science. It covers the essentials of machine learning using Scikit-learn, TensorFlow, and PyTorch, as well as the fundamentals of data manipulation, visualization, and exploration with NumPy, pandas, Matplotlib, and Seaborn.
2. Hands-on Machine Learning Using and Scikit-Learn, Keras and TensorFlow: This book serves as a useful, hands-on introduction to Python machine learning. It teaches you how to create and train a variety of machine learning models, including generative adversarial networks, recurrent neural networks, convolutional neural networks, linear regression, classification, clustering, dimensionality reduction, and reinforcement learning, using Scikit-learn, Keras, and TensorFlow.
3. Python for Data Analysis: This book offers a thorough and useful introduction to using Python for data analysis. It teaches you how to perform statistical analysis, data visualization, and exploratory data analysis in addition to teaching you how to clean, transform, and manipulate data using pandas, NumPy, and IPython.
4. François Chollet’s Deep Learning with Python (Second Edition): This book provides a thorough and detailed introduction to Python deep learning. You learn how to create and train many kinds of deep learning models, including feedforward neural networks, convolutional neural networks, recurrent neural networks, attention mechanisms, transformers, and autoencoders, using Keras and TensorFlow.
5. Natural Language Processing with Python: This book provides a thorough and hands-on introduction to Python-based natural language processing (NLP). It teaches you how to analyze and process text using the Natural Language Toolkit (NLTK), including named entity recognition, sentiment analysis, text classification, tokenization, tagging, parsing, stemming, lemmatizing, chunking, and text creation.
6. Python Machine Learning: This book offers a thorough and hands-on introduction to Python deep learning and machine learning. It teaches you how to create and train a variety of machine learning and deep learning models using Scikit-learn and TensorFlow, including autoencoders, convolutional neural networks, recurrent neural networks, ensemble models, linear models, tree-based models, support vector machines, and neural networks.
7. Jose Portilla’s Python for Computer Vision with OpenCV and Deep Learning: This book provides a thorough and useful introduction to Python-based computer vision. It shows you how to utilize OpenCV and TensorFlow for a variety of computer vision tasks and applications, including segmentation, tracking, face recognition, object detection, optical character recognition, image processing, and style transfer.
8. Data Science from Scratch: This book offers a distinctive and enjoyable overview to Python data science. It shows you how to use only Python and its standard library to implement the fundamental ideas and algorithms of data science from scratch. Along with learning how to create different kinds of machine learning models, like k-nearest neighbors, logistic regression, decision trees, neural networks, and clustering, you will also learn how to work with data, including vectors, matrices, statistics, probability, linear algebra, and calculus.
9. Python Crash Course: This book offers a quick and simple introduction to Python programming. It shows you how to use Python for a variety of tasks, including web development, data visualization, and game development, as well as the fundamentals of the language, including variables, data types, functions, classes, modules, files, exceptions, testing, and debugging.
10. Automate the Boring Stuff with Python: This book offers an enjoyable and useful introduction to Python programming. It shows you how to use Python to automate a variety of jobs and operations, including file searches and downloads, file renaming and organization, email and text message sending, form filling, web page scraping, keyboard and mouse control, and more.
