Data science algorithms in a week : top 7 algorithms for scientific computing, data analysis, and machine learning / Dávid Natingga.

Format:

Book

Author/Creator:

Natingga, Dávid, author.

Language:

English

Subjects (All):

Machine learning.

Computer algorithms.

Physical Description:

1 online resource (207 pages) : color illustrations, maps

Edition:

Second edition.

Place of Publication:

Birmingham : Packt, 2018.

Biography/History:

Natingga David: Dvid 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 references 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.

Summary:

Build a strong foundation of machine learning algorithms in 7 daysKey FeaturesUse Python and its wide array of machine learning libraries to build predictive modelsLearn the basics of the 7 most widely used machine learning algorithms within a weekKnow when and where to apply data science algorithms using this guideBook 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 problemWhat you will learnUnderstand how to identify a data science problem correctlyImplement well-known machine learning algorithms efficiently using PythonClassify your datasets using Naive Bayes, decision trees, and random forest with accuracyDevise an appropriate prediction solution using regressionWork with time series data to identify relevant data events and trendsCluster your data using the k-means algorithmWho 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

Contents:

Data Science Algorithms in a Week: Top 7 algorithms for scientific computing, data analysis, and machine learning

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed February 20, 2019).

ISBN:

9781789800968

178980096X

OCLC:

1463578638