کتاب یادگیری ماشین پیشرفته با پایتون

Solve challenging data science problems by mastering cutting-edge machinelearning techniques in Python About This Book Resolve complex machine learningproblems and explore deep learning Learn to use Python code for implementing arange of machine learning algorithms and techniques A practical tutorial thattackles real-world computing problems through a rigorous and effectiveapproach Who This Book Is For This title is for Python developers and analystsor data scientists who are looking to add to their existing skills byaccessing some of the most powerful recent trends in data science. If you’veever considered building your own image or text-tagging solution, or ofentering a Kaggle contest for instance, this book is for you! Prior experienceof Python and grounding in some of the core concepts of machine learning wouldbe helpful. What You Will Learn Compete with top data scientists by gaining apractical and theoretical understanding of cutting-edge deep learningalgorithms Apply your new found skills to solve real problems, throughclearly-explained code for every technique and test Automate large sets ofcomplex data and overcome time-consuming practical challenges Improve theaccuracy of models and your existing input data using powerful featureengineering techniques Use multiple learning techniques together to improvethe consistency of results Understand the hidden structure of datasets using arange of unsupervised techniques Gain insight into how the experts solvechallenging data problems with an effective, iterative, and validation-focusedapproach Improve the effectiveness of your deep learning models further byusing powerful ensembling techniques to strap multiple models together InDetail Designed to take you on a guided tour of the most relevant and powerfulmachine learning techniques in use today by top data scientists, this book isjust what you need to push your Python algorithms to maximum potential. Clearexamples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more – all whilst working with real-worldapplications that include image, music, text, and financial data. The machinelearning techniques covered in this book are at the forefront of commercialpractice. They are applicable now for the first time in contexts such as imagerecognition, NLP and web search, computational creativity, andcommercial/financial data modeling. Deep Learning algorithms and ensembles ofmodels are in use by data scientists at top tech and digital companies, butthe skills needed to apply them successfully, while in high demand, are stillscarce. This book is designed to take the reader on a guided tour of the mostrelevant and powerful machine learning techniques. Clear descriptions of howtechniques work and detailed code examples demonstrate deep learningtechniques, semi-supervised learning and more, in real world applications. Wewill also learn about NumPy and Theano. By this end of this book, you willlearn a set of advanced Machine Learning techniques and acquire a broad set ofpowerful skills in the area of feature selection & feature engineering. Styleand approach This book focuses on clarifying the theory and code behindcomplex algorithms to make them practical, useable, and well-understood. Eachtopic is described with real-world applications, providing both broadcontextual coverage and detailed guidance.”words : 72376