We went through a bunch of fundamental machine learning concepts in the last chapter. We learned them along with analogies the fun way, such as studying for the exams, designing driving schedule, and so on. As promised, starting from this chapter as the second step of our learning journal, we will be discovering in detail several import machine learning algorithms and techniques. Beyond analogies, we will be exposed to and will solve real-world examples, which makes our journal more interesting. We start with a classic natural language processing problem--newsgroups topic modeling in this chapter. We will gain hands-on experience in working with text data, especially how to convert words and phrases into machine-readable values. We will be tackling the project in an unsupervised learning manner, using clustering algorithms, including k-means clustering...
Python Machine Learning By Example
By :
Python Machine Learning By Example
By:
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.
Related Content you might be interested in
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