#### 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

## Distributions

The probability distribution is a function from a set of possible outcomes (for example, heads and tails) to a set of probabilities of those outcomes (that is, 50% for both heads and tails).

### Normal distribution

Random variables of many natural phenomena are modeled by a normal distribution. A normal distribution has the following probability density:

Here, μ is the mean of the distribution and

is the variation of the distribution. A normal distribution graph has the shape of a bell curve; for example, refer to the following graph of a normal distribution with a mean of 10 and a standard deviation of 2:

Normal distribution with a mean of 10 and a standard deviation of 2