- k-Nearest Neighbors algorithm: An algorithm that estimates an unknown data item to be like the majority of the k-closest neighbors to that item.
- Naive Bayes classifier: A way to classify a data item using Bayes' theorem about the conditional probabilities, P(A|B)=(P(B|A) * P(A))/P(B), and in addition, assuming the independence between the given variables in the data.
- Decision Tree: A model classifying a data item into one of the classes at the leaf node, based on the matching properties between the branches on the tree and the actual data item.
- Random Decision Tree: A decision tree in which every branch is formed using only a random subset of the available variables during its construction.
- Random Forest: An ensemble of random decision trees constructed on the random subset of the data with the replacement, where a data...
Data Science Algorithms in a Week
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Data Science Algorithms in a Week
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Overview of this book
<p>Machine learning applications are highly automated and self-modifying, and they continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly. Data science helps you gain new knowledge from existing data through algorithmic and statistical analysis.</p>
<p>This book will address the problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on the existing trends in your datasets.</p>
<p>This book covers algorithms such as: k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-series. On completion of the book, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem.</p>
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Data Science Algorithms in a Week
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Table of Contents (12 chapters)
Preface
Free Chapter
Classification Using K Nearest Neighbors
Naive Bayes
Decision Trees
Random Forest
Clustering into K Clusters
Regression
Time Series Analysis
R Reference
Python Reference
Glossary of Algorithms and Methods in Data Science
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