“Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms
Key Features
Understand the types of Machine learning.
Get familiar with different Feature extraction methods.
Get an overview of how Neural Network Algorithms work.
Learn how to implement Decision Trees and Random Forests.
The book not only explains the Classification algorithms but also discusses the deviations/ mathematical modeling.
Description
This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature Selection. The book covers algorithms and implementations of the most common Feature Selection Techniques. The book then focuses on Linear Regression and Gradient Descent. Some of the important Classification techniques such as K-nearest neighbors, logistic regression, Naive Bayesian, and Linear Discriminant Analysis are covered in the book. It then gives an overview of Neural Networks and explains the biological background, the limitations of the perceptron, and the backpropagation model. The Support Vector Machines and Kernel methods are also included in the book. It then shows how to implement Decision Trees and Random Forests.
Towards the end, the book gives a brief overview of Unsupervised Learning. Various Feature Extraction techniques, such as Fourier Transform, STFT, and Local Binary patterns, are covered. The book also discusses Principle Component Analysis and its implementation.
What will you learn
Learn how to prepare Data for Machine Learning.
Learn how to implement learning algorithms from scratch.
Use scikit-learn to implement algorithms.
Who this book is for
The book is designed for Undergraduate and Postgraduate Computer Science students and for the professionals who intend to switch to the fascinating world of Machine Learning. This book requires basic know-how of programming fundamentals, Python, in particular.
Table of Contents
1. An introduction to Machine Learning
2. The beginning: Pre-Processing and Feature Selection
3. Regression
4. Classification
5. Neural Networks- I
6. Neural Networks-II
7. Support Vector machines
8. Decision Trees
9. Clustering
10. Feature Extraction
Appendix
A1. Cheat Sheets
A2. Face Detection
A3.Biblography
About the Author
Harsh Bhasin is an Applied Machine Learning researcher. Mr. Bhasin worked as Assistant Professor in Jamia Hamdard, New Delhi, and taught as a guest faculty in various institutes including Delhi Technological University. Before that, he worked in C# Client-Side Development and Algorithm Development.
He has authored a few books including Programming in C#, Oxford University Press; Algorithms, Oxford University Press; Python Basics, Mercury; Python for Beginners, New Age International.
Mr. Bhasin has authored a few papers published in renowned journals including Soft Computing, Springer, BMC Medical Informatics and Decision Making, AI and Society, etc. He is the reviewer of prominent journals and has been the editor of a few special issues. He has been a recipient of a distinguished fellowship.
Outside work, he is deeply interested in Hindi Poetry, progressive era; Hindustani Classical Music, percussion instruments.
Features
- Get familiar with different inbuilt data structures, functional programming, and datetime objects.
- Handling heavy datasets to optimize the data types for memory management, reading files in chunks, dask, and modin pandas.
- Time-series analysis to find trends, seasonality, and cyclic components.
- Seaborn to build aesthetic plots with high-level interfaces and customized themes.
- Exploratory data analysis with real-time datasets to maximize the insights about data.
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