The ebook Python 3 for Machine Learning (PDF) is designed to offer the reader basic Python3 programming concepts relevant to machine learning. The first four chapters offer a fast-paced introduction to NumPy, Python 3, and Pandas. The fifth chapter presents the fundamental concepts of machine learning. The sixth chapter is dedicated to machine learning classifiers, like logistic regression, k-NN, random forests, decision trees, and SVMs. The final chapter features material on NLP and RL. Keras-based code samples are included to complement the theoretical discussion. The ebook also includes separate appendices for regular expressions, Keras, and TensorFlow 2. C
Features
- Presents separate appendices for regular expressions, Keras, and TensorFlow 2
- Offers the reader with basic Python 3 programming concepts related to machine learning
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.
NOTE: The product only includes the ebook, Python 3 for Machine Learning in PDF. No access codes, samples, or coding files are included.
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