Data Science Algorithms in a Week
Packt Publishing Limited (Verlag)
978-1-78980-607-6 (ISBN)
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 DescriptionMachine 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 forThis 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
Dávid Natingga graduated with a master's in engineering in 2014 from Imperial College London, specializing in artificial intelligence. In 2011, he worked at Infosys Labs in Bangalore, India, undertaking research into the optimization of machine learning algorithms. In 2012 and 2013, while at Palantir Technologies in USA, he developed algorithms for big data. In 2014, while working as a data scientist at Pact Coffee, London, he created an algorithm suggesting products based on the taste preferences of customers and the structures of the coffees. In order to use pure mathematics to advance the field of AI, he is a PhD candidate in Computability Theory at the University of Leeds, UK. In 2016, he spent 8 months at Japan's Advanced Institute of Science and Technology as a research visitor.
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
| Erscheinungsdatum | 05.11.2018 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-78980-607-0 / 1789806070 |
| ISBN-13 | 978-1-78980-607-6 / 9781789806076 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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