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


In this chapter, we learned about regression analysis. We can think of variables as being dependent on each other in a functional way. For example, the y variable is a function of x denoted by y=f(x). The f(x) function has constant parameters. If y depends on x linearly, then f(x)=a*x+b, where a and b are constant parameters in the f(x) function.

We saw that regression is a method to estimate these constant parameters in such a way that the estimated f(x) follows y as closely as possible. This is formally measured by the squared error between f(x) and y for x data samples.

We also covered the gradient descent method, which minimizes this error by updating the constant parameters in the direction of the steepest descent (that is, the partial derivative of the error), ensuring that the parameters converge to the values resulting in minimal errors in the quickest possible way.

Finally, we learned about the scipy.linalgPython library which supports the estimation of linear regression using...