Grokking Machine Learning, Second Edition

Luis G. Serrano is a research scientist in quantum artificial intelligence, known for making complex topics accessible. With experience as a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple, Luis brings practical industry knowledge to every page. He translates complex machine learning concepts into clear guidance that helps developers build their skills.

• 1 WHAT IS MACHINE LEARNING? IT IS COMMON SENSE, EXCEPT DONE BY A COMPUTER 2 TYPES OF MACHINE LEARNING 3 DRAWING A LINE CLOSE TO OUR POINTS: LINEAR REGRESSION 4 OPTIMIZING THE TRAINING PROCESS: UNDERFITTING, OVERFITTING, TESTING, AND REGULARIZATION 5 USING LINES TO SPLIT OUR POINTS: THE PERCEPTRON ALGORITHM 6 A CONTINUOUS APPROACH TO SPLITTING POINTS: LOGISTIC CLASSIFIERS 7 HOW DO YOU MEASURE CLASSIFICATION MODELS? ACCURACY AND ITS FRIENDS 8 USING PROBABILITY TO ITS MAXIMUM: THE NAIVE BAYES MODEL 9 SPLITTING DATA BY ASKING QUESTIONS: DECISION TREES 10 COMBINING BUILDING BLOCKS TO GAIN MORE POWER: NEURAL NETWORKS 11 FINDING BOUNDARIES WITH STYLE: SUPPORT VECTOR MACHINES AND THE KERNEL METHOD 12 COMBINING MODELS TO MAXIMIZE RESULTS: ENSEMBLE LEARNING 13 PUTTING IT ALL IN PRACTICE: A REAL-LIFE EXAMPLE OF DATA ENGINEERING AND MACHINE LEARNING  14 AI FOR LANGUAGE AND IMAGE GENERATION APPENDICES APPENDIX A: SOLUTIONS TO THE EXERCISES APPENDIX B: THE MATH BEHIND GRADIENT DESCENT APPENDIX C: REFERENCES