دانلود کتاب تسلط بر الگوریتم های طبقه بندی برای یادگیری ماشین

Creating models for Machine Learning is a time-consuming process involving hours of work on the machine to tune the models during the training phase. Every model has a mathematical foundation, meaning several hyperparameters must be altered before the final model can arrive. Understanding the mathematics behind every model makes it possible to formulate a scientific mechanism for trying different combinations of the hyperparameters to test. Also, understanding the mathematics behind the models helps understand how the different hyperparameters can be set so that the model converges relatively quickly. Another aspect is that all models do not suit all types of data. To understand which model can be suitable for building a model for a given dataset, one needs to understand the mathematical formulation of the models. In summary, to be successful in building machine-learning models, understanding the mathematical formulations is vital. This book provides details of the mathematics of every machine-learning model for solving classification problems. The book starts with simple models and proceeds to state-of-the-art complex models. A detailed algorithm walkthrough is provided with demonstration data for every algorithm discussed in the book. Last, but not least, every chapter discusses an industry problem solved using machine learning. The book discusses applying classification algorithms to solve Spam Detection, Customer Segmentation, Disease Diagnosis, Malware Detection, Emotion Detection from Speech, and Image Classification. All the code in this book has been written in Python. Python is by far the most popular programming language for data sciences. However, users of other popular programming languages, including R, Scala, and Octave, can use this book as the mathematics of the models remain the same. The main library used in this book is Scikit-Learn, which is by far the most popular library for machine learning. While Scikit-Learn is used for Python in this book, Scikit-Learn is also available for R language. I hope you will find this book informative and helpful.