LearnCrunch | Applied Machine Learning by Dr. Kirk Borne

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Applied Machine Learning
The best way to master Machine Learning is to understand the core concepts, be capable of explaining them with simple everyday examples, and be able to apply those to solving business problems. This course focuses on building up your theoretical and practical knowledge so that you can understand how to identify ML problems, choose the right algorithms for solving the problem at hand, build robust models in Python, and explain your models and communicate their results in common business language for colleagues, clients, and executives. You’ll learn from real world examples and apply your learnings to a variety of business problems.
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Dr. Kirk Borne
Dr. Kirk Borne is the Chief Science Officer at AI startup DataPrime and the founder of Data Leadership Group LLC. He is a career data professional, data science leader, and research astrophysicist. From 2015 to 2021, he was Principal Data Scientist, Data Science Fellow, and Executive Advisor at Booz Allen Hamilton.
Previously, Kirk was professor of Astrophysics and Computational Science at George Mason University. Before that, he spent 20 years supporting data systems activities for NASA space science missions, including the Hubble Space Telescope.
He has been named by many media outlets as a top worldwide influencer on social media, promoting big data analytics, data science, machine learning, AI, data & AI strategy, and data literacy for all.
He has spoken at hundreds of events worldwide, for which he has been the conference keynote speaker at dozens of those, including TEDx, Global AI Summit (online), Marketing Analytics & Data Science (San Francisco), Big Data London, and more.
What you'll learn
Module 1: Modeling and Machine Learning Concepts
Focus on the basics, foundations, and concepts that are essential for progressing on the journey of expanding one’s expertise in ML techniques, algorithms, and applications, including supervised vs. unsupervised learning, feedback and optimization loops, accuracy vs. precision, benefits of high-variety data, bias and ethical modeling, types of analytics outcomes, and examples of successes and failures in the field.
Module 2: Common Business Applications of ML Algorithms
Focus on specific applications of ML algorithms to common business problems, including customer segmentation (personalization and recommender engines), outlier detection (anomaly, fraud, and surprise discovery), predictive analytics (forecasting, predictive maintenance), and association analysis (link discovery, knowledge graphs, marketing attribution).
Module 3: Uncommon (Atypical) Applications of Typical ML Algorithms
Focus on solving a business problem in an unexpected way with an ML algorithm or technique that is more commonly used for a very different problem or in a very different application domain. A common thread throughout this module is Forecasting 2.0 - going beyond traditional forecasting modeling techniques, into the realm of early warning detection within your business applications through precursor analytics.
Module 4: Analytics Mastery in Business and Beyond
Focus on the steps to analytics mastery, including matching the right algorithm and technique to the right business problem, data storytelling, decision science, the internet of things (IoT, which will produce massive quantities of real-time streaming data in the coming decade, generating a multi-trillion dollar market, requiring a data-literate analytics-equipped IoT-savvy workforce), and the soft skills that must accompany the hard skills for long-term career success and advancement.
Prerequisites:
A basic knowledge of modeling principles, understanding of the different types of machine learning algorithms (unsupervised: clustering; and supervised: classification), prior expertise in working with data, adept with simple algebraic manipulations of numbers and equations, and familiarity with basic business analytics concepts and their outcomes.
This course is for:
This course is for beginner to intermediate learners of machine learning and data analytics who wish to deepen their understanding of the concepts, techniques, algorithms, limitations, and applications of ML.
What you will be able to do after this course:
Participants will be able to demonstrate expertise in deploying both common and uncommon applications of ML concepts, techniques, and algorithms to a variety of business problems. Participants will also be able to identify the types of data needed for a particular ML problem, the choice of ML algorithm needed for the problem, and the type of analytics outcome that is desired (detection, recognition, prediction, or optimization). Participants will also develop strong competencies in data literacy, analytic thinking, and data storytelling.
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