Machine Learning for Beginners

Learn how to build a complete machine learning pipeline by mastering feature extraction, feature selection, and algorithm training Key Features ● Develop a solid understanding of foundational principles in machine learning. ● Master regression and classification methods for accurate data prediction and categorization in machine learning. ● Dive into advanced machine learning topics, including unsupervised learning and deep learning. Description The second edition of “Machine Learning for Beginners” addresses key concepts and subjects in machine learning. The book begins with an introduction to the foundational principles of machine learning, followed by a discussion of data preprocessing. It then delves into feature extraction and feature selection, providing comprehensive coverage of various techniques such as the Fourier transform, short-time Fourier transform, and local binary patterns. Moving on, the book discusses principal component analysis and linear discriminant analysis. Next, the book covers the topics of model representation, training, testing, and cross-validation. It emphasizes regression and classification, explaining and implementing methods such as gradient descent. Essential classification techniques, including k-nearest neighbors, logistic regression, and naive Bayes, are also discussed in detail. The book then presents an overview of neural networks, including their biological background, the limitations of the perceptron, and the backpropagation model. It also covers support vector machines and kernel methods. Decision trees and ensemble models are also discussed. The final section of the book provides insight into unsupervised learning and deep learning, offering readers a comprehensive overview of these advanced topics. By the end of the book, you will be well-prepared to explore and apply machine learning in various real-world scenarios. What you will learn ● Acquire skills to effectively prepare data for machine learning tasks. ● Learn how to implement learning algorithms from scratch. ● Harness the power of scikit-learn to efficiently implement common algorithms. ● Get familiar with various Feature Selection and Feature Extraction methods. ● Learn how to implement clustering algorithms. Who this book is for This book is for both undergraduate and postgraduate Computer Science students as well as professionals looking to transition into the captivating realm of Machine Learning, assuming a foundational familiarity with Python. Table of Contents Section Fundamentals 1. An Introduction to Machine Learning 2. The Data Pre-Processing 3. Feature Selection 4. Feature Extraction 5. Model Development Section Supervised Learning 6. Regression 7. K-Nearest Neighbors 8. Logistic Regression and Naïve Bayes Classifier 9. Neural Network The Perceptron 10. Neural Network The Multi-Layer Perceptron 11. Support Vector Machines 12. Decision Trees 13. An Introduction to Ensemble Learning Section 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