Beschreibung:
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 casestudies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance.
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.