In this chapter, we continued working on the online advertising click-through prediction project. This time, we overcame the categorical feature challenge with the one-hot encoding technique. We then resorted to a new classification algorithm logistic regression for its high scalability to large datasets. The in-depth discussion of the logistic regression algorithm started with the introduction of the logistic function, which led to the mechanics of the algorithm itself. It followed with how to train a logistic regression via gradient descent. After implementing a logistic regression classifier by hand and testing it on our click-through dataset, we learned how to train the logistic regression model via a more advanced manner, stochastic gradient descent, and adjusted our algorithm accordingly. We also practiced how to use the SGD-based logistic regression classifier from scikit-learn and applied it to our...
Python Machine Learning By Example
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Python Machine Learning By Example
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Overview of this book
Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning.
This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques.
Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.
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Current Title:
Python Machine Learning By Example
Table of Contents (9 chapters)
Preface
Free Chapter
Getting Started with Python and Machine Learning
Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms
Spam Email Detection with Naive Bayes
News Topic Classification with Support Vector Machine
Click-Through Prediction with Tree-Based Algorithms
Click-Through Prediction with Logistic Regression
Stock Price Prediction with Regression Algorithms
Customer Reviews