Python 3 for Machine Learning
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Description
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.
Features:
Provides the reader withbasic Python3 programming concepts related to machine learning
Includes separate appendices for regular expressions, Keras, and TensorFlow 2
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Features:
Provides the reader withbasic Python3 programming concepts related to machine learning
Includes separate appendices for regular expressions, Keras, and TensorFlow 2
show more
Product details
- Paperback | 200 pages
- 178 x 229 x 22.86mm | 521.63g
- 02 Mar 2020
- MERCURY LEARNING & INFORMATION
- United States
- English
- 1683924959
- 9781683924951
Table of contents
1: Introduction to Python 3
2: Conditional Logic, Loops, and Functions
3: Python Collections
4: Working with Pandas
5: Introductionto Machine Learning
6: Classifiers in Machine Learning
7: NLP and RL
Appendices
A: Introduction to Regular Expressions
B: Introduction to Keras
C: Introduction to TensorFlow 2
Index
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2: Conditional Logic, Loops, and Functions
3: Python Collections
4: Working with Pandas
5: Introductionto Machine Learning
6: Classifiers in Machine Learning
7: NLP and RL
Appendices
A: Introduction to Regular Expressions
B: Introduction to Keras
C: Introduction to TensorFlow 2
Index
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Review quote
Programmers who want to get up to speed in Python 3will appreciate O. Campesato's Python 3 for Machine Learning, a survey of basic Python 3 programmingconcepts, applications, expressions, and machine learning relationships. Thisintroduction is packed with supporting mathematical, programming, andstatistical information and summarizes each chapter's machine learningcomponents, making it an excellent self study guide."-- "Midwest Book Review"
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About Oswald Campesato
Oswald Campesato specializes in Deep Learning, Data Cleaning, Java, Android, and TensorFlow. He is the author/co-author of over twenty-fivebooks including Android Pocket Primer, Angular4 Pocket Primer, and the Python Pocket Primer (Mercury Learning).
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