Nandan worked with Allianz in the Global Data Office. Previously, he worked with Uniper (an energy generation company) as a Data Engineer dealing with solving the problems of power plants by applying Machine Learning. Having completed his Masters in Data Engineering, applying the concepts learnt in a practical environment is really something he likes to do.
Before this, Nandan worked with Microsoft, Germany as a working student where he also completed his Master Thesis on Recommender systems.
Along with his masters studies in Bremen, he worked as a working student in LittelFuse and joined XING as a Data Engineering Summer School Intern.
He also has experience of working in two large consulting companies in India where he solved various client problems ranging from banking to healthcare.
While pursuing Bachelors in Electrical Engineering, Nandan also coauthored and published research papers (IEEE & Springer) on application of AI in the field of control systems.
My Mentoring Topics
- Data Science,
- Data Engineering,
- Career Advice,
- Internship Applications
The Hundred-page Machine Learning Book
Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics - both theory and practice - that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field." Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: "The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field." Karolis Urbonas, Head of Data Science at Amazon: "A great introduction to machine learning from a world-class practitioner." Chao Han, VP, Head of R&D at Lucidworks: "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning." Sujeet Varakhedi, Head of Engineering at eBay: "Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.'' Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: "A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.'' Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: "I would highly recommend "The Hundred-Page Machine Learning Book" for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."View
Approaching (Almost) Any Machine Learning Problem
This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option. This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along. Table of contents: - Setting up your working environment - Supervised vs unsupervised learning - Cross-validation - Evaluation metrics - Arranging machine learning projects - Approaching categorical variables - Feature engineering - Feature selection - Hyperparameter optimization - Approaching image classification & segmentation - Approaching text classification/regression - Approaching ensembling and stacking - Approaching reproducible code & model serving There are no sub-headings. Important terms are written in bold. I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, visit this link: https://bit.ly/aamlquestions And Subscribe to my youtube channel: https://bit.ly/abhitubesubView
R for Data Science - Import, Tidy, Transform, Visualize, and Model Data
Hadley Wickham, Garrett Grolemund
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and resultsView
Jediný oficiální a dlouho očekávaný životopis vizionáře a jedné z nejvýraznějších osobností moderní doby. Už v předobjednávkách byla kniha na Amazonu nejprodávanější knihou všech dob. Na podkladě více než čtyřiceti rozhovorů se Stevem Jobsem provedených v průběhu dvou let, a na základě více než sta rozhovorů se členy jeho rodiny, s přáteli, konkurenty, soupeři i kolegy - pojednává tato kniha o životě plném vrcholů i propastí a o pronikavě intenzivní osobnosti tvořivého podnikatele, jehož vášeň pro dokonalost a železné odhodlání zcela převrátily šest odvětví lidské činnosti: osobní počítače, kreslené filmy, hudbu, telefony, počítačové tablety a digitální tisk.View