Building Machine Learning Systems Using Python: Practice to Train Predictive Models and Analyze Machine Learning Results with Real Use-Cases
- Length: 136 pages
- Edition: 1
- Language: English
- Publisher: BPB Publications
- Publication Date: 2021-05-07
- ISBN-10: 9389423619
- ISBN-13: 9789389423617
- Sales Rank: #3676544 (See Top 100 Books)
Explore Machine Learning Techniques, Different Predictive Models, and its Applications
Key Features
- Extensive coverage of real examples on implementation and working of ML models.
- Includes different strategies used in Machine Learning by leading data scientists.
- Focuses on Machine Learning concepts and their evolution to algorithms.
Description
This book covers basic concepts of Machine Learning, various learning paradigms, different architectures and algorithms used in these paradigms.
You will learn the power of ML models by exploring different predictive modeling techniques such as Regression, Clustering, and Classification. You will also get hands-on experience on methods and techniques such as Overfitting, Underfitting, Random Forest, Decision Trees, PCA, and Support Vector Machines. In this book real life examples with fully working of Python implementations are discussed in detail.
At the end of the book you will learn about the unsupervised learning covering Hierarchical Clustering, K-means Clustering, Dimensionality Reduction, Anomaly detection, Principal Component Analysis.
What you will learn
- Learn to perform data engineering and analysis.
- Build prototype ML models and production ML models from scratch.
- Develop strong proficiency in using scikit-learn and Python.
- Get hands-on experience with Random Forest, Logistic Regression, SVM, PCA, and Neural Networks.
Who this book is for
This book is meant for beginners who want to gain knowledge about Machine Learning in detail. This book can also be used by Machine Learning users for a quick reference for fundamentals in Machine Learning. Readers should have basic knowledge of Python and Scikit-Learn before reading the book.
About the Authors
Dr Deepti Chopra is working as an Assistant Professor (IT) at Lal Bahadur Shastri Institute of Management, Delhi. She has around 7 years of teaching experience. Her areas of interest include Natural Language Processing, Computational Linguistics, and Artificial Intelligence. She is the author of three books and has written several research papers in various international conferences and journals.
Cover Page Title Page Copyright Page Dedication Page About the Author About the Reviewer Acknowledgement Preface Errata Table of Contents 1. Introduction Structure Objectives History of machine learning Classification of machine learning Challenges faced in adopting machine learning Applications Conclusion Questions 2. Linear Regression Structure Objectives Linear regression in one variable Linear regression in multiple variables Gradient descent Polynomial regression Conclusion Questions 3. Classification Using Logistic Regression Introduction Structure Objectives Binary classification Logistic regression Multiclass classification Conclusion Questions 4. Overfitting and Regularization Structure Objectives Overfitting and regularization in linear regression Overfitting and regularization in logistic regression Conclusion Questions 5. Feasibility of Learning Introduction Structure Objectives Feasibility of learning an unknown target function In-sample error and out-of-sample error Conclusion Questions 6. Support Vector Machine Introduction Structure Objectives Margin and Large Margin methods Kernel methods Conclusion Questions 7. Neural Network Introduction Structure Objectives Early models Perceptron learning Back propagation Stochastic Gradient Descent Conclusion Questions 8. Decision Trees Introduction Structure Objectives Decision trees Regression trees Stopping criterion and pruning loss functions in decision trees Categorical attributes, multiway splits, and missing values in decision trees Instability in decision trees Conclusion Questions 9. Unsupervised Learning Introduction Structure Objectives Clustering K-means clustering Hierarchical clustering Principal Component Analysis (PCA) Conclusion Questions 10. Theory of Generalization Introduction Structure Objectives Training versus testing Bounding the testing error VC dimension Conclusion Questions 11. Bias and Fairness in Machine Learning Introduction Structure Objectives Introduction How to detect bias? How to fix biases or achieve fairness in ML? Conclusion Questions Index
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