Can anyone recommend Machine learning textbook or any material for analysis/data modeling?

One highly recommended textbook for machine learning is "Pattern Recognition and Machine Learning" by Christopher Bishop. This book provides a comprehensive introduction to the field of pattern recognition and machine learning, covering both classical and modern techniques. Another excellent resource for data modeling and analysis is "Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. This book covers fundamental concepts in statistical learning and includes practical examples using R programming. These textbooks provide a solid foundation for understanding machine learning algorithms and techniques for data modeling. They are highly regarded within the field and are commonly used as reference materials in academic settings.

Safiul Haque Chowdhury

There are many excellent textbooks and resources available for learning about machine learning and data modeling. Here are some widely recommended options:

"Pattern Recognition and Machine Learning" by Christopher M. Bishop: This book provides a comprehensive introduction to pattern recognition and machine learning concepts, with a focus on probabilistic models and Bayesian methods.

"Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani: This introductory textbook covers fundamental concepts of statistical learning and machine learning, including supervised and unsupervised learning techniques, regression, classification, resampling methods, and more.

"Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman: This book is a more in-depth treatment of statistical learning theory and methods, covering topics such as linear methods, tree-based methods, support vector machines, and unsupervised learning.

"Machine Learning: A Probabilistic Perspective" by Kevin P. Murphy: This textbook provides a probabilistic framework for understanding machine learning algorithms and techniques, covering topics such as Bayesian networks, graphical models, and probabilistic graphical models.

"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: For those interested in deep learning, this book offers a comprehensive overview of deep learning theory and applications, covering topics such as neural networks, convolutional networks, recurrent networks, and more.

"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron: This practical guide focuses on hands-on learning with popular machine learning libraries such as Scikit-Learn, Keras, and TensorFlow, covering topics such as classification, regression, clustering, neural networks, and deep learning.

"Python for Data Analysis" by Wes McKinney: While not strictly a machine learning textbook, this book is essential for anyone working with data in Python. It covers data manipulation, visualization, and analysis techniques using the pandas library, making it a valuable resource for data modeling and machine learning projects.

These are just a few recommendations to get you started. Depending on your background, interests, and learning style, you may find other textbooks and resources that suit your needs better. Many of these books also offer supplementary materials such as lecture slides, code examples, and online tutorials to enhance your learning experience.

Please follow me if it's helpful. All the very best. Regards, Safiul

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