Training and Improving Classifiers with Sklearn (Practicum) Introduction to Machine Learning (Lecture) Program OutlineĬlass runs from 9:00am until 5:00pm each day with one-hour lunch and two 15-minute breaks. Tablets will not be sufficient for the computing activities performed in this course. A high-level understanding of programming (thinking in terms of programs) is also beneficial.įor professionals whose work involves data hands-on, the course aims to provide a deeper understanding and sharper intuitions about translating their problems into the deep learning terms and writing programs to implement these formulations and which methods to consider in what contexts. The course assumes an undergraduate degree in computer science or another technical area such as statistics, physics, electrical engineering, etc., with exposure to vectors and matrices, basic concepts of probability. This course is designed for people with basic analytic skills and familiarity with supervised learning. Understand current machine learning trends and opportunities that they bring.Learn to discuss scaling issues (amount of data, dimensionality, storage, and computation).Obtain hands-on experience in implementing, debugging and tuning neural models in pytorch.Outline key aspects of practical problems that are likely to impact performance.Learn alternative approaches for feature representation and modeling for graphs, text and images.
#Machine learning bootcamp how to
Understand how to formulate new problems into machine learning terms.Explore modern natural language processing and computer vision tools, formulations, and problems.Understand broad opportunities for automation with machine learning.This is a hands-on course where lectures will be supplemented by the guided practical tutorials and in class-programming labs where participants will learn how to implement, train and improve supervised models using PyTorch package.Įnrollment for this course is limited to 30 participants to allow for more personalized instruction. We will discuss issues that impact classification performance, foresee likely hurdles and explore possible remedies for improving model accuracy. In this course, you will learn how to formulate your problems in machine learning terms and how to effectively utilize existing deep learning packages to solve them. The goal of this bootcamp is to teach participants how to use deep learning (DL) tools to process data in different modalities, ranging from text, images, and graphs. The use of machine learning models in industry continues to grow.
#Machine learning bootcamp professional
THIS COURSE MAY BE TAKEN INDIVIDUALLY OR AS PART OF THE PROFESSIONAL CERTIFICATE PROGRAM IN MACHINE LEARNING & ARTIFICIAL INTELLIGENCE. Over the course of two intensive days, you’ll explore actionable strategies for anticipating and addressing critical issues that can impact classification performance and other hurdles, and master cutting-edge machine learning tools that process data in different modalities, including text, images, and graphs. Learn to leverage the latest deep learning advancements to create innovative solutions and solve your organization’s pressing challenges.
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