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


Cross-validation is a method of validating a hypothesis about data. At the beginning of the analysis process, the data is split into learning data and testing data. A hypothesis is fit to the learning data, and then its actual error is measured on the testing data. This way, we can estimate how well a hypothesis may perform on future data. Reducing the amount of learning data can also be beneficial as it reduces the chance of hypothesis over-fitting. This is where a hypothesis is trained to a particularly narrow subset of the data.

K-fold cross-validation

The original data is partitioned randomly into k folds. One fold is used for validation, while k-1 folds of data are used for hypothesis training.