Build a strong foundation of machine learning algorithms in 7 days
Key Features

Use Python and its wide array of machine learning libraries to build predictive models

Learn the basics of the 7 most widely used machine learning algorithms within a week

Know when and where to apply data science algorithms using this guide
Book Description
Machine learning applications are highly automated and selfmodifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various realworld 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 precluster 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 knearest neighbors, Naive Bayes, decision trees, random forest, kmeans, regression, and timeseries 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
What you will learn

Understand how to identify a data science problem correctly

Implement wellknown machine learning algorithms efficiently using Python

Classify your datasets using Naive Bayes, decision trees, and random forest with accuracy

Devise an appropriate prediction solution using regression

Work with time series data to identify relevant data events and trends

Cluster your data using the kmeans algorithm
Who this book is for
This book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You'll also find this book useful if you're currently working with data science algorithms in some capacity and want to expand your skill set
Table of Contents

Classification using K Nearest Neighbors

Naive Bayes

Decision Trees

Random Forests

Clustering into K clusters

Regression

Time Series Analysis

Python Reference

Statistics

Glossary of Algorithms and Methods in Data Science