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Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

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This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality redu This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You'll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), na�ve Bayes, clustering, and neural networks Saving and loading trained models


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This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality redu This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You'll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), na�ve Bayes, clustering, and neural networks Saving and loading trained models

30 review for Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

  1. 5 out of 5

    Reza Rahutomo

    Chris Albon helped my early days at my job as data engineer back in 2016 through his website. And all practical knowledge of him is compiled in this book. It is recommended for those who love learning python data science through practice and straight explanation.

  2. 5 out of 5

    Stein Karlsen

    Great book with excellent examples and discussion

  3. 4 out of 5

    Karl

    Excellent handbook. Not for beginners.

  4. 4 out of 5

    Guilherme

    Great book to beginners start to understand a complete machine learning workflow. It will definitely serve me as reference in my machine learning projects.

  5. 5 out of 5

    Vinay Mittal

    The best starter kit for machine learning.

  6. 4 out of 5

    Nishant

  7. 4 out of 5

    Mancan

  8. 5 out of 5

    Martin Mckenna

  9. 5 out of 5

    Sungwoo Nam

  10. 5 out of 5

    Adham Ehab

  11. 4 out of 5

    Laimis

  12. 5 out of 5

    Ben8t

  13. 5 out of 5

    Chathura Perera

  14. 5 out of 5

    Adwitiya Trivedi

  15. 5 out of 5

    Martijn

  16. 4 out of 5

    Jovany Agathe

  17. 5 out of 5

    Matthew

  18. 4 out of 5

    Elliot Henry

  19. 5 out of 5

    Yang Yu

  20. 4 out of 5

    Praveena

  21. 5 out of 5

    Mahmoud Rabie

    A very good book on “How to do machine learning”, the book don’t explain any topics in deep but only shows how to do different stuff using python libraries, mainly scikit-learn and keras The book contains 21 chapters and each chapter contains a number of “Problem - Solution - Code - Discussion” sections All the book sections have the same style, “Problem” (i.e. Handling Imbalanced Classes in Support Vector Machines), “Solution” (i.e. Increase the penalty for misclassifying the smaller class using A very good book on “How to do machine learning”, the book don’t explain any topics in deep but only shows how to do different stuff using python libraries, mainly scikit-learn and keras The book contains 21 chapters and each chapter contains a number of “Problem - Solution - Code - Discussion” sections All the book sections have the same style, “Problem” (i.e. Handling Imbalanced Classes in Support Vector Machines), “Solution” (i.e. Increase the penalty for misclassifying the smaller class using class_weight) and then few lines of code to show solution, then a discussion section that explain the solution and any alternative approaches The book contains 183 different problem that cover a lot of topics (i.e. Data Wrangling, Handling Numerical Data, Handling Categorical Data, Handling Text, Handling Dates and Times, Handling Images, Dimensionality Reduction Using Feature Extraction, Model Evaluation, Model Selection, Linear Regression, Trees and Forests, K-Nearest Neighbors) The book can be a very good reference for the new Data Scientists and will save a lot of time on their daily activities

  22. 4 out of 5

    Krzysztof

  23. 5 out of 5

    Blake Pengelly

  24. 5 out of 5

    Luis

  25. 4 out of 5

    Paco Nathan

  26. 4 out of 5

    Rajesh Chengalpathy

  27. 4 out of 5

    Stephen A. Ridley

  28. 5 out of 5

    Rich

  29. 4 out of 5

    Steve

  30. 4 out of 5

    Nadezhda

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