What this book covers
Chapter 1, The Realm of Supervised Learning, covers various supervised-learning techniques for regression. We will learn how to analyze bike-sharing patterns and predict housing prices.
Chapter 2, Constructing a Classifier, covers various supervised-learning techniques for data classification. We will learn how to estimate the income brackets and evaluate a car based on its characteristics.
Chapter 3, Predictive Modeling, discusses predictive-modeling techniques using Support Vector Machines. We will learn how to apply these techniques to predict events occurring in buildings and traffic on the roads near sports stadiums.
Chapter 4, Clustering with Unsupervised Learning, explains unsupervised learning algorithms, including k-means and Mean Shift clustering. We will learn how to apply these algorithms to stock market data and customer segmentation.
Chapter 5, Building Recommendation Engines, teaches you about the algorithms that we use to build recommendation engines. We will learn how to apply these algorithms to collaborative filtering and movie recommendations.
Chapter 6, Analyzing Text Data, explains the techniques that we use to analyze text data, including tokenization, stemming, bag-of-words, and so on. We will learn how to use these techniques to perform sentiment analysis and topic modeling.
Chapter 7, Speech Recognition, covers the algorithms that we use to analyze speech data. We will learn how to build speech-recognition systems.
Chapter 8, Dissecting Time Series and Sequential Data, explains the techniques that we use to analyze time series and sequential data including Hidden Markov Models and Conditional Random Fields. We will learn how to apply these techniques to text sequence analysis and stock market predictions.
Chapter 9, Image Content Analysis, covers the algorithms that we use for image content analysis and object recognition. We will learn how to extract image features and build object-recognition systems.
Chapter 10, Biometric Face Recognition, explains the techniques that we use to detect and recognize faces in images and videos. We will learn about dimensionality reduction algorithms and build a face recognizer.
Chapter 11, Deep Neural Networks, covers the algorithms that we use to build deep neural networks. We will learn how to build an optical character recognition system using neural networks.
Chapter 12, Visualizing Data, explains the techniques that we use to visualize various types of data in machine learning. We will learn how to construct different types of graphs, charts, and plots.
