Practical Workshops on Machine Learning are coming soon in Bishkek! The event is part of Data Science Month Program organized by High Tech Park of the Kyrgyz Republic. Day 1 ( September 22, 10:00-14:00 ): Demystifying Data Science and Machine Learning, and Demo Session. This meetup will have two parts. In part one, you will hear a presentation that provides broad awareness and understanding of Data Science, Machine Learning, and AI. This talk will discuss the uses, misuses, possibilities, and limitations of Data Science, Machine Learning, and AI to a general audience. In part two, we will put Machine Learning into action with a brief, hands-on demo session using Python to highlight examples of building three predictive models using spreadsheet data, images, and text. (Completed notebooks will be provided for participants to follow along.) Day 2 ( September 29, 10:00-14:00): Machine Learning Key Concepts Using Python. In this meetup, we will go through an example project in Python from beginning to end. You'll practice key Machine Learning concepts like data visualization, data cleaning, data splitting, and model building. Emphasis will be on understanding the concepts, not the code. (Completed notebooks will be provided for participants to follow along.) Trainer background: Alex Gutman, PhD, is a Data Scientist, Corporate Trainer, and Accredited Professional Statistician® with expertise in statistical & machine learning and experience working as a Data Scientist for two Fortune 50 companies. At Procter & Gamble, Alex supported the Data Science and Artificial Intelligence (AI) team where he taught courses on open-source software and machine learning. He also implemented several machine learning and Natural Language Processing (NLP) techniques to improve the safety of P&G products. In his current role at 84.51° - a data science company owned by Kroger - Alex enables, empowers, and encourages other data scientists to use machine learning by developing & delivering training on open-source & state-of-the-art proprietary AI tools and by consulting on supervised, unsupervised, NLP, & time-series projects. He’s especially interested in communicating the uses, misuses, possibilities, and limitations of AI and machine learning to non-technical business partners and executives. He received his BS and MS in Mathematics from Wright State University and his PhD in Applied Mathematics from the Air Force Institute of Technology (AFIT) at Wright-Patterson Air Force Base in Ohio. He’s a member of the American Statistical Association and serves as an Adjunct Professor in the Department of Operational Sciences at AFIT.