Book Description
Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model.
This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance.
By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.
What you will learn
Review fundamental concepts such as bias and variance
Extract features from categorical variables, text, and images
Predict the values of continuous variables using linear regression and K Nearest Neighbors
Classify documents and images using logistic regression and support vector machines
Create ensembles of estimators using bagging and boosting techniques
Discover hidden structures in data using K-Means clustering
Evaluate the performance of machine learning systems in common tasks
About the Author
Gavin Hackeling is a data scientist and author. He was worked on a variety of machine learning problems, including automatic speech recognition, document classification, object recognition, and semantic segmentation. An alumnus of the University of North Carolina and New York University, he lives in Brooklyn with his wife and cat.
Contents
Chapter 1. The Fundamentals of Machine Learning
Chapter 2. Simple linear regression
Chapter 3. Classification and Regression with K Nearest Neighbors
Chapter 4. Feature Extraction and Preprocessing
Chapter 5. From Simple Regression to Multiple Regression
Chapter 6. From Linear Regression to Logistic Regression
Chapter 7. Naive Bayes
Chapter 8. Nonlinear Classification and Regression with Decision Trees
Chapter 9. From Decision Trees to Random Forests, and other Ensemble Methods
Chapter 10. The Perceptron
Chapter 11. From the Perceptron to Support Vector Machines
Chapter 12. From the Perceptron to Artificial Neural Networks
Chapter 13. Clustering with K-Means
Chapter 14. Dimensionality Reduction with Principal Component Analysis
