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|a Sarkar, Dipanjan.
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|a Practical Machine Learning with Python
|h [electronic resource] :
|b A Problem-Solver's Guide to Building Real-World Intelligent Systems /
|c by Dipanjan Sarkar, Raghav Bali, Tushar Sharma.
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|a 1st ed. 2018.
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|a Chapter 1: Machine Learning Basics -- Chapter 2: The Python Machine Learning Ecosystem -- Chapter 3: Processing, Wrangling and Visualizing Data.-Chapter 4: Feature Engineering and Selection -- Chapter 5: Building, Tuning and Deploying Models.-Chapter 6: Analyzing Bike Sharing Trends -- Chapter 7: Analyzing Movie Reviews Sentiment -- Chapter 8: Customer Segmentation and Effective Cross Selling -- Chapter 9: Analyzing Wine Types and Quality -- Chapter 10: Analyzing Music Trends and Recommendations -- Chapter 11: Forecasting Stock and Commodity Prices -- Chapter 12: Deep Learning for Computer Vision.
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|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. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! You will: Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering.
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|a Sharma, Tushar.
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