Data Science Algorithms in a Week: Top 7 algorithms for scientific computing,data analysis,and machine learning,2nd Edition
Authors: David Natingga
ISBN-10: 1789806070
ISBN-13: 9781789806076
Publication Date 出版日期: 2018-10-31
Print Length 页数: 214 pages
Book Description
By finelybook
Build a strong foundation of machine learning algorithms in 7 days
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
contents
1 Classification Using K-Nearest Neighbors
2 Naive Bayes
3 Decision Trees
4 Random Forests
5 Clustering into K Clusters
6 Regression
7 Time Series Analysis
