#### 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
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
Classification Using K-Nearest Neighbors
Time Series Analysis
Python Reference
Statistics
Glossary of Algorithms and Methods in Data Science
Other Books You May Enjoy
Index

## Gradient descent algorithm and its implementation

To understand how we may be able to predict a value by using linear regression from first principles in an even better way, we need to study the gradient descent algorithm and then implement it in Python.

A gradient descent algorithm is an iterative algorithm that updates the variables in the model to fit the data, making as few errors as possible. More generally, it finds the minimum of a function.

We would like to express the weight in terms of height by using a linear formula:

We estimate the parameter,

, using n data samples

to minimize the following square error:

The gradient descent algorithm does this by updating the pi parameter in the direction of (∂/∂ pj) E(p), in particular:

Here, learning_rate determines that the speed of the convergence of E(p) is at the minimum. Updating the p parameter will result in the convergence of E(p) to a certain value, providing that learning_rate is sufficiently small. In the...