Practical Machine Learning with Python : A Problem-Solver's Guide to Building Real-World Intelligent Systems / by Dipanjan Sarkar, Raghav Bali, Tushar Sharma.

Format:

Book

Author/Creator:

Sarkar., Author.

Bali, Raghav, Author.

Sharma, Tushar, Author.

Contributor:

Dipanjan.

Language:

English

Subjects (All):

Artificial intelligence.

Python (Computer program language).

Open source software.

Computer programming.

Artificial Intelligence.

Python.

Open Source.

Local Subjects:

Artificial Intelligence.

Python.

Open Source.

Physical Description:

1 online resource (XXV, 530 p. 273 illus., 209 illus. in color.)

Edition:

1版. 2018.

Other Title:

Problem-solver's guide to building real-world intelligent systems

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2018.

System Details:

text file

Summary:

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.

Contents:

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.

Notes:

Includes index.

ISBN:

9781484232071

1484232070

OCLC:

1020493783