Python for Data Analysis by Wes McKinney: This book provides a comprehensive introduction to data analysis using Python. It covers essential libraries such as NumPy, pandas, and Matplotlib, and includes practical examples and case studies.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron: This book focuses on machine learning and covers popular libraries like Scikit-Learn, Keras, and TensorFlow. It provides hands-on examples and guides you through building various machine learning models.
Python Data Science Handbook by Jake VanderPlas: This book offers a practical approach to data science with Python. It covers essential libraries such as NumPy, pandas, Matplotlib, and Scikit-Learn, and includes code examples and tutorials.
Applied Predictive Modeling by Max Kuhn and Kjell Johnson: This book focuses on practical aspects of predictive modeling and machine learning. It covers various algorithms and techniques and provides examples using R and Python.
Kaggle: Kaggle is a popular platform for data science competitions and provides a vast collection of datasets and notebooks. You can find numerous data science examples and practice projects shared by the community.
DataCamp: DataCamp offers interactive online courses in Python for data science. They provide hands-on exercises and projects to apply your knowledge and practice data science concepts.
Towards Data Science: This is a popular online platform that publishes articles and tutorials on various data science topics. It covers a wide range of practical examples, case studies, and code snippets.
I often use these materials for practice.
buran write Oct-13-2023, 05:07 PM:Spam link removed