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"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

machine-learning deep-learning text-analytics classification clustering natural-language-processing computer-vision data-science spacy nltk scikit-learn prophet time-series-analysis convolutional-neural-networks tensorflow keras statsmodels pandas deep-neural-networksMars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and many other libraries. More details about installing Mars can be found at installation section in Mars document.

machine-learning tensorflow numpy scikit-learn pandas pytorch xgboost lightgbm tensor dask ray dataframe statsmodels joblibCheck out this article I wrote on Medium: Essential Math for Data Science.

statistics numpy pandas numerical-analysis analytics machine-learning bayesian-statistics inferential-statistics statsmodelsRecently, I was reading through A/B Testing with Machine Learning - A Step-by-Step Tutorial written by Matt Dancho of Business Science. I have been always fascinated by the idea of A/B Testing and the amount of impact it can bring in businesses. The tutorial is very definitive and Matt has explained each and every step in the tutorial. He has detailed about each and every decision taken while developing the solution. Even though the tutorial is written in R, I was able to scram through his code and my knowledge of Data Science helped me to understand the concepts very quickly. I will have to thank Matt for putting together all the key ingredients of the Data Science world and or using them to solve a real problem.

sklearn xgboost statsmodels
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