MATLAB for Machine Learning: Unlock the power of deep learning for swift and enhanced results. 2 ed

About the Author xv
About the Technical Reviewer xvii
Acknowledgments xix
Introduction xxi
Part I: Learn How to Learn....1
Chapter 1: Understand Relational Databases....3
Chapter 2: Create an Efficient Database Development Process....29
Chapter 3: Increase Confidence and Knowledge with Testing....51
Chapter 4: Find Reliable Sources....77
Chapter 5: Master the Entire Stack....87
Part II: Write Powerful SQL with Sets and Advanced Features....105
Chapter 6: Build Sets with Inline Views and ANSI Join Syntax....107
Chapter 7: Query the Database with Advanced SELECT Features....127
Chapter 8: Modify Data with Advanced DML....195
Chapter 9: Improve the Database with Advanced Schema Objects....223
Chapter 10: Optimize the Database with Oracle Architecture....271
Part III: Write Elegant SQL with Patterns and Styles....299
Chapter 11: Stop Coding and Start Writing....301
Chapter 12: Write Large SQL Statements....317
Chapter 13: Write Beautiful SQL Statements....335
Chapter 14: Use SQL More Often with Basic Dynamic SQL....349
Chapter 15: Avoid Anti-patterns....363
Part IV: Improve SQL Performance....389
Chapter 16: Understand SQL Performance with Algorithm Analysis....391
Chapter 17: Understand SQL Tuning Theories....421
Chapter 18: Improve SQL Performance....461
Part V: Solve Anything with Oracle SQL....521
Chapter 19: Solve Challenging Problems with Arcane SQL Features....523
Chapter 20: Use SQL More Often with Advanced Dynamic SQL....543
Chapter 21: Level Up Your Skills with PL/SQL....559
Part VI: Appendixes....603
Appendix A: SQL Style Guide Cheat Sheet....605
Appendix B: Computer Science Topics....607
Index....609

Discover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications.

By navigating the versatile machine learning tools in the MATLAB environment, you'll learn how to seamlessly interact with the workspace. You'll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you'll explore various classification and regression techniques, skillfully applying them with MATLAB functions.

This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You'll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you'll leverage MATLAB tools for deep learning and managing convolutional neural networks.

By the end of the book, you'll be able to put it all together by applying major machine learning algorithms in real-world scenarios.

What you will learn

• Discover different ways to transform data into valuable insights
• Explore the different types of regression techniques
• Grasp the basics of classification through Naive Bayes and decision trees
• Use clustering to group data based on similarity measures
• Perform data fitting, pattern recognition, and cluster analysis
• Implement feature selection and extraction for dimensionality reduction
• Harness MATLAB tools for deep learning exploration

Who this book is for

This book is for ML engineers, data scientists, DL engineers, and CV/NLP engineers who want to use MATLAB for machine learning and deep learning. A fundamental understanding of programming concepts is necessary to get started.