A lot of popular classic machine learning framework based on learning before TensorFlow depth. For example the KNN, such as support vector machine, such as a random forest. Relative to the theoretical depth study of these classical machine learning algorithms built on more sophisticated mathematical derivation. Operations research, optimization theory, mathematical analysis, mathematical statistics and stochastic processes form the theoretical basis of these algorithms, but also the basis for further learning all kinds of deep learning algorithms based on neural network.
Recommended Machine Learning Primer "Python machine learning based tutorial," Introduction to Python language. The main contents include: basic concepts of machine learning and its applications; in practice most commonly used machine learning algorithms as well as the advantages and disadvantages of these algorithms; to be processed in machine learning the importance of data presentation, and what aspects should focus on data ; model evaluation and advanced methods of parameter adjustment to highlight the cross-validation and grid search; pipeline concept; how the previous chapters of the method applied to the text data, also presented a unique approach some text.
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"Python machine learning based tutorial" Chinese high-definition version of PDF, 306 pages, catalogs and bookmarks, text can be copied; HD in English PDF, 392 pages, catalogs and bookmarks, color with pictures, text can be copied; the English version two can Comparative study. Complete source code.
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