Amazon cover image
Image from Amazon.com
Image from Google Jackets

Machine Learning for Beginners

By: Material type: TextTextPublication details: New Delhi BPB 2025Edition: 2ndDescription: xxiii, 360p. 24 x 18 cmISBN:
  • 9789355515636
DDC classification:
  • 006.31 BHA
Summary: Section I: Fundamentals 1. An Introduction to Machine Learning 2. The Beginning: Data Pre-Processing 3. Feature Selection 4. Feature Extraction 5. Model Development Section II: Supervised Learning 6. Regression 7. K-Nearest Neighbors 8. Classification: Logistic Regression and Naïve Bayes Classifier 9. Neural Network I: The Perceptron 10. Neural Network II: The Multi-Layer Perceptron 11. Support Vector Machines 12. Decision Trees 13. An Introduction to Ensemble Learning Section III: Unsupervised Learning and Deep Learning 14. Clustering 15. Deep Learning Appendix 1: Glossary Appendix 2: Methods/Techniques Appendix 3: Important Metrics and Formulas Appendix 4: Visualization- Matplotlib Answers to Multiple Choice Questions Bibliography
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Collection Call number Status Date due Barcode
Reference Reference Raj Kumar Goel Institute of Technology MCA Collection 006.31 BHA (Browse shelf(Opens below)) Not for loan 96502
Reference Reference Raj Kumar Goel Institute of Technology Computer Science 006.31 BHA (Browse shelf(Opens below)) Not for loan 96800
Reference Reference Raj Kumar Goel Institute of Technology Computer Science 006.31 BHA (Browse shelf(Opens below)) Not for loan 96801
Reference Reference Raj Kumar Goel Institute of Technology IOT (Internet of Things) 006.31 BHA (Browse shelf(Opens below)) Not for loan 96184
Reference Reference Raj Kumar Goel Institute of Technology IOT (Internet of Things) 006.31 BHA (Browse shelf(Opens below)) Not for loan 96185

Section I: Fundamentals
1. An Introduction to Machine Learning
2. The Beginning: Data Pre-Processing
3. Feature Selection
4. Feature Extraction
5. Model Development
Section II: Supervised Learning
6. Regression
7. K-Nearest Neighbors
8. Classification: Logistic Regression and Naïve Bayes Classifier
9. Neural Network I: The Perceptron
10. Neural Network II: The Multi-Layer Perceptron
11. Support Vector Machines
12. Decision Trees
13. An Introduction to Ensemble Learning
Section III: Unsupervised Learning and Deep Learning
14. Clustering
15. Deep Learning
Appendix 1: Glossary
Appendix 2: Methods/Techniques
Appendix 3: Important Metrics and Formulas
Appendix 4: Visualization- Matplotlib
Answers to Multiple Choice Questions
Bibliography

There are no comments on this title.

to post a comment.
Implemented & Customized by: BestBookBuddies

Powered by Koha