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 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

What you will learn

Understand how to identify a data science problem correctly
Implement well-known 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 k-means 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

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

"About this title" may belong to another edition of this title.

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