Book Image

Data Science Algorithms in a Week - Second Edition

By : David Natingga
Book Image

Data Science Algorithms in a Week - Second Edition

By: David Natingga

Overview of this book

Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
Table of Contents (16 chapters)
Title Page
Packt Upsell
Glossary of Algorithms and Methods in Data Science

Information theory

Information theory studies thequantification of information, its storage, and communication. We introduce concepts of information entropy and information gain, which are used to construct a decision tree using the ID3 algorithm.

Information entropy

The information entropy of any given piece data is a measure of the smallest amount of information necessary to represent a data item from that data. The units of information entropy are familiar - bits, bytes, kilobytes, and so on. The lower the information entropy, the more regular the data is, and the more patterns occur in the data, thus, the smaller the quantity of information required to represent it. That is how compression tools on computers can take large text files and compress them to a much smaller size, as words and word expressions keep reoccurring, forming a pattern.

Coin flipping

Imagine we flip an unbiased coin. We would like to know whether the result is heads or tails. How much information do we need to represent...