Read my bestselling 📖 The Hundred-Page Machine Learning Book: http://themlbook.com.

Read my new book 📖 Machine Learning Engineering: http://mlebook.com.

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About me:

Ph.D. in artificial intelligence, passionate about data, speaking English, French, and Russian. Solid scientific programming and team leadership skills. More than 20 years of experience working on various computing projects, including several own startups.

More than 15 years of practical hands-on experience in automated data analysis, machine learning, natural language processing, Linux hacking, database, and search engine setup and querying.

Expert in Python and Java with several years of day-to-day design and development in the big data context.

Specialties: machine learning, natural language processing, conversational interfaces (chatbots), information retrieval.

National scholarship

Distinction scholarship

Coursera

Computing for Data Analysis — Johns Hopkins University (with distinction)

Data Analysis — Johns Hopkins University

Computational Graph Theory

Computing for Data Analysis

Data Analysis

Machine Learning

Multiagent Systems

Optimization

Université Laval

Sevastopol State Technical University

English

French

Java

Python

Russian

TalentNeuron

Leading the team of Machine Learning developers digging through the gigabytes of talent marketplace data.

Machine Learning Lead

True Positive Inc.

“If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book.”

—Cassie Kozyrkov, Chief Decision Scientist at Google

“Foundational work about the reality of building machine learning models in production. Comes at the right time when companies start to see through the AI hype, realizing it takes a conscious engineering effort and best practices to make machine learning work. Another great book from Andriy!”

—Karolis Urbonas, Head of Machine Learning and Science at Amazon

Released on Amazon, B&N, and Leanpub. Drafts are available on http://www.mlebook.com.

Author of the Machine Learning Engineering 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."

CEO and Author of The Hundred-Page Machine Learning Book

Gartner

Leading a team working on "hacking" text and other unstructured or semi-structured data to extract useful information. Problems successfully solved by me and my team include:

— conversational user interfaces (chatbots);
— parsing of free-form résumé to detect and extract candidate name, employers, skills, certifications, employment history;
— parsing of job descriptions to detect and extract job title, salary, employer name, assign a standard occupation code;
— automated noisy and multi-language data normalization, cleanup, deduplication;
— novel data detection;
— language detection / text segmentation by language;
— sequence labeling (queries, addresses, salaries);
— machine translation;
— Web-data analysis.

Tools and techniques used: machine learning (deep/shallow, classification, clustering, topic modeling, structured output prediction), regular expressions, Java, Python, MySQL, scikit-learn, Keras.

Director of Data Science — Machine Learning Team Leader

Wanted Technologies

Turning terabytes of job announcements from online job boards into structured knowledge: extraction and classification of skills, standardization of job titles, classification of job announcements by an occupational classification, extraction/cleanup of structured data (company names, proper names, salaries, skills, etc), extraction and standardization of postal addresses, fuzzy duplicate identification (announcements, skills).

Tools: linux shell scripting, MySQL, Sphinx, Python (scikit-learn, NLTK), Java.

Data Analyst

Fujitsu Canada

Computer scientist with the focus on applying machine learning and natural language processing in the context of big data; technology intelligence lead.

Technologies: R, Python, Hadoop, HBase.

Applied Scientist — Fujitsu Innovation Center

Working on research contracts for the Defence Research and Development Canada (DRDC).

Research projects mostly turned around the problems of fusion of sensor data and intelligent software assistants.

Technologies: Scilab.

Scientific Consultant

Research in multiagent systems resulted in multiple publications in top international conferences and journals (AAMAS, AAAI, Journal of Algorithms).

Taplio

The 100 Best Linkedin Influencers about coding

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The 100 Best Linkedin Influencers about coding

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