Twitter is a highly popular social network with over 300 million monthly active users. The platform has been developed around short posts (limited to a number of characters; currently, the limit is 280 characters). The posts themselves are called tweets. On average, 6000 tweets are tweeted every second, which equates to around 200 billion tweets per year. This constitutes a huge amount of data that contains an equal amount of information. As is obvious, it is not possible to analyze this volume of data by hand. Thus, automated solutions have been employed, both by Twitter and third parties. One of the hottest topics involves a tweet's sentiment, or how the user feels about the topic that they tweets. Sentiment analysis comes in many flavors. The most common approach is a positive or negative classification of each tweet. Other approaches involve...
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Hands-On Ensemble Learning with Python
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Hands-On Ensemble Learning with Python
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Overview of this book
Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model.
With its hands-on approach, you'll not only get up to speed with the basic theory but also the application of different ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. In addition to this, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models.
By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios.
Table of Contents (20 chapters)
Preface
Free Chapter
Section 1: Introduction and Required Software Tools
A Machine Learning Refresher
Getting Started with Ensemble Learning
Section 2: Non-Generative Methods
Voting
Stacking
Section 3: Generative Methods
Bagging
Boosting
Random Forests
Section 4: Clustering
Section 5: Real World Applications
Classifying Fraudulent Transactions
Predicting Bitcoin Prices
Evaluating Sentiment on Twitter
Recommending Movies with Keras
Clustering World Happiness
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