Details about Python for Probability, Statistics, and Machine Learning
Python for Probability, Statistics, and Machine Learning 2nd edition PDF free download – This second edition is updated for Python version 3.6+. Furthermore, many existing sections have been revised for clarity based on feedback from the first version. The book is now over thirty percent larger than the original with new material about important probability distributions, including key derivations and illustrative code samples. Additional important statistical tests are included in the statistics chapter including the Fisher Exact test and the Mann–Whitney–Wilcoxon Test. A new section on survival analysis has been included. The most significant addition is the section on deep learning for image processing with a detailed discussion of gradient descent methods that underpin all deep learning work. There is also substantial discussion regarding generalized linear models. As before, there are more Programming Tips that illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks that have been tested for accuracy so you can try these out for yourself in your own codes.
Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as NumPy, Scikit-learn, SymPy, SciPy, lifelines, CVXPY, Theano, Matplotlib, Pandas, TensorFlow, StatsModels, and Keras. As with the first edition, all of the key concepts are developed mathematically and are reproducible in Python, to provide the reader with multiple perspectives on the material. As before, this book is not designed to be exhaustive and reflects the author’s eclectic industrial background. The focus remains on concepts and fundamentals for day-to-day work using Python in the most expressive way possible.