John Mueller has produced hundreds of books and articles on topics ranging from networking to home security and from database management to heads-down programming.Luca Massaron is a senior expert in data science who has been involved with quantitative methods since 2000. He is a Google Developer Expert (GDE) in machine learning.

• Introduction   1Part 1: Introducing How Machines Learn 5Chapter 1: Getting the Real Story about AI 7Chapter 2: Learning in the Age of Big Data 23Chapter 3: Having a Glance at the Future 37Part 2: Preparing Your Learning Tools   47Chapter 4: Installing a Python Distribution 49Chapter 5: Beyond Basic Coding in Python   67Chapter 6: Working with Google Colab   87Part 3: Getting Started with the Math Basics   115Chapter 7: Demystifying the Math Behind Machine Learning   117Chapter 8: Descending the Gradient   139Chapter 9: Validating Machine Learning   153Chapter 10: Starting with Simple Learners   175Part 4: Learning from Smart and Big Data   197Chapter 11: Preprocessing Data 199Chapter 12: Leveraging Similarity 221Chapter 13: Working with Linear Models the Easy Way   243Chapter 14: Hitting Complexity with Neural Networks 271Chapter 15: Going a Step Beyond Using Support Vector Machines 307Chapter 16: Resorting to Ensembles of Learners   319Part 5: Applying Learning to Real Problems 339Chapter 17: Classifying Images   341Chapter 18: Scoring Opinions and Sentiments   361Chapter 19: Recommending Products and Movies 383Part 6: The Part of Tens   405Chapter 20: Ten Ways to Improve Your Machine Learning Models   407Chapter 21: Ten Guidelines for Ethical Data Usage 415Chapter 22: Ten Machine Learning Packages to Master   423Index   431

John Mueller has produced hundreds of books and articles on topics ranging from networking to home security and from database management to heads-down programming.Luca Massaron is a senior expert in data science who has been involved with quantitative methods since 2000. He is a Google Developer Expert (GDE) in machine learning.

• Introduction   1Part 1: Introducing How Machines Learn 5Chapter 1: Getting the Real Story about AI 7Chapter 2: Learning in the Age of Big Data 23Chapter 3: Having a Glance at the Future 37Part 2: Preparing Your Learning Tools   47Chapter 4: Installing a Python Distribution 49Chapter 5: Beyond Basic Coding in Python   67Chapter 6: Working with Google Colab   87Part 3: Getting Started with the Math Basics   115Chapter 7: Demystifying the Math Behind Machine Learning   117Chapter 8: Descending the Gradient   139Chapter 9: Validating Machine Learning   153Chapter 10: Starting with Simple Learners   175Part 4: Learning from Smart and Big Data   197Chapter 11: Preprocessing Data 199Chapter 12: Leveraging Similarity 221Chapter 13: Working with Linear Models the Easy Way   243Chapter 14: Hitting Complexity with Neural Networks 271Chapter 15: Going a Step Beyond Using Support Vector Machines 307Chapter 16: Resorting to Ensembles of Learners   319Part 5: Applying Learning to Real Problems 339Chapter 17: Classifying Images   341Chapter 18: Scoring Opinions and Sentiments   361Chapter 19: Recommending Products and Movies 383Part 6: The Part of Tens   405Chapter 20: Ten Ways to Improve Your Machine Learning Models   407Chapter 21: Ten Guidelines for Ethical Data Usage 415Chapter 22: Ten Machine Learning Packages to Master   423Index   431

John Mueller has produced hundreds of books and articles on topics ranging from networking to home security and from database management to heads-down programming.Luca Massaron is a senior expert in data science who has been involved with quantitative methods since 2000. He is a Google Developer Expert (GDE) in machine learning.

• Introduction   1Part 1: Introducing How Machines Learn 5Chapter 1: Getting the Real Story about AI 7Chapter 2: Learning in the Age of Big Data 23Chapter 3: Having a Glance at the Future 37Part 2: Preparing Your Learning Tools   47Chapter 4: Installing a Python Distribution 49Chapter 5: Beyond Basic Coding in Python   67Chapter 6: Working with Google Colab   87Part 3: Getting Started with the Math Basics   115Chapter 7: Demystifying the Math Behind Machine Learning   117Chapter 8: Descending the Gradient   139Chapter 9: Validating Machine Learning   153Chapter 10: Starting with Simple Learners   175Part 4: Learning from Smart and Big Data   197Chapter 11: Preprocessing Data 199Chapter 12: Leveraging Similarity 221Chapter 13: Working with Linear Models the Easy Way   243Chapter 14: Hitting Complexity with Neural Networks 271Chapter 15: Going a Step Beyond Using Support Vector Machines 307Chapter 16: Resorting to Ensembles of Learners   319Part 5: Applying Learning to Real Problems 339Chapter 17: Classifying Images   341Chapter 18: Scoring Opinions and Sentiments   361Chapter 19: Recommending Products and Movies 383Part 6: The Part of Tens   405Chapter 20: Ten Ways to Improve Your Machine Learning Models   407Chapter 21: Ten Guidelines for Ethical Data Usage 415Chapter 22: Ten Machine Learning Packages to Master   423Index   431