This book is designed to provide the reader with basic Python3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth chapter is devoted to machine learning classifiers, such as logistic regression, k-NN, decision trees, random forests, and SVMs. The final chapter includes material on NLP and RL. Keras-based code samples are included to supplement the theoretical discussion. The book also contains separate appendices for regular expressions, Keras, and TensorFlow 2. Companion files with code examples and figures may be downloaded (with Amazon proof of purchase) by writing to
[email protected]
Features
+Provides the reader with basic Python 3 programming concepts related to machine learning
+Includes separate appendices for regular expressions, Keras, and TensorFlow 2
+Companion files with code examples and figures may be downloaded (with Amazon proof of purchase) by writing to
[email protected]
Brief Table of Contents
1: Introduction to Python 3. 2: Conditional Logic, Loops, and Functions. 3: Python Collections.
4: Introduction to NumPy and Pandas. 5: Introduction to Machine Learning. 6: Classifiers in Machine Learning.
7: Natural Language Processing and Reinforcement Learning. Appendices. A: Introduction to Regular Expressions. B: Introduction to Keras. C: Introduction to TensorFlow 2. Index.
About The Author
Oswald Campesato (San Francisco, CA) specializes in Deep Learning, Java, Android, and TensorFlow. He is the author/co-author of over twenty-five books including TensorFlow 2 Pocket Primer, Artificial Intelligence, Machine Learning, and Deep Learning, and the Python Pocket Primer (Mercury Learning) .
