000			03272nam a22002297a 4500
020			_a9781484232064
041			_aENG
082			_a006.3 _bDIP
100			_aSarkar, Dipanjan
245			_aPractical machine learning with Python : _bA Problem-solver's guide to building real-world intelligent systems / _cDipanjan Sarkar, Raghav Bali and Tushar Sharma.
260			_aCalifornia : _bApress , _c2018.
300			_axxv, 530 pages : _billustrations ; _c26 cm.
504			_aIncludes index
505			_aIntroduction Part 1. Understanding machine learning. Machine learning basics The Python machine learning ecosystem Part 2. The machine learning pipeline. Processing, wrangling, and visualizing data Feature engineering and selection Building, tuning, and deploying models Part 3. Real-world case studies. Analyzing bike sharing trends Analyzing movie reviews sentiment Customer segmentation and effective cross selling Analyzing wine types and quality Analyzing music trends and recommendations Forecasting stock and commodity prices Deep learning for computer vision
520			_a"Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. - Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today"--Provided by publisher
650			_aArtificial intelligence
650			_aComputer science
650			_aMachine learning
700			_aRaghav, Bali.
700			_aTushar, Sharma
942			_2ddc _cBK
999			_c23709 _d23709