Book Image

Data Science Algorithms in a Week - Second Edition

By : David Natingga
Book Image

Data Science Algorithms in a Week - Second Edition

By: David Natingga

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
Table of Contents (16 chapters)
Title Page
Packt Upsell
Contributors
Preface
Glossary of Algorithms and Methods in Data Science
Index

Contributors

About the author

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.

 

 

About the reviewers

Surendra Pepakayala is a hands-on, seasoned technology professional with over 20 years of experience in the US and India. He has built enterprise software products at startups and multinational companies and has built and sold a technology business after five years in operation. He currently consults for small to medium businesses, helping them leverage cloud, data science, machine learning, AI, and cutting-edge technologies to gain an advantage over their competition. In addition to being an advisory board member for a couple of startups in the technology space, he holds numerous coveted certifications such as TOGAF, CRISC, and CGEIT.Jen Stirrup is a data strategist and technologist, Microsoft Most Valuable Professional (MVP) and Microsoft Regional Director,  tech community advocate, public speaker, blogger, published author, and keynote speaker. She is the founder of a boutique consultancy based in the UK, Data Relish, which focuses on delivering successful business intelligence and artificial intelligence solutions that add real value to customers worldwide. She has featured on the BBC as a guest expert on topics related to data.

 

 

 

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