TES110 - Machine Learning & AI Micro Boot Camp

Course Overview

The Machine Learning and AI MicroBootCamp™ is an online, self-paced course designed for experienced tech professionals to complete in 8 to 10 weeks. Learners will emerge with the context, confidence, and technical skills needed to catapult themself  — and their organization — into the future. By the end of this premium course, learners will be able to:

• Understand the AI landscape: summarize what AI is and the ways it is impacting our lives, discover recent innovations in AI, and hypothesize how AI could be applied in the future.
• Apply machine learning techniques: gain knowledge and solve problems using machine learning models.
• Use unsupervised and supervised machine learning models: categorize unlabeled data, and use labeled data to make predictions.
• Optimize machine learning models: evaluate machine learning models by using test data and metrics, and improve the performance of machine learning models by using optimization techniques.
• Make predictions about data: explain neural networks and deep learning models, and apply them to make predictions about data.
• Use natural language processing (NLP) and transformers: interpret and predict human language text, solve NLP tasks with pre-trained transformer models, and build AI applications.

This course is offered through an educational partnership between edX and UNC Charlotte.

Benefits of a MicroBootCamp course include:

• A self-paced format: Learners are in control over how quickly they work through the course, based on personal and professional obligations.
• Innovative instruction: Built to help move at the pace of AI with an in-depth, comprehensive machine learning and AI curriculum designed and led by experts.
• A hands-on environment: While learning on your own time, learners still have 10 weeks of access to instructor support, office hours, Q&A sessions, and peer discussions.
• Real-world projects: Practice new skills with hands-on, human-graded projects that can be added to your professional portfolio.

Course Outline

Course overview:

Module 1 - Introduction to AI and Machine Learning

Module 2 - Unsupervised Learning

Module 3 - Supervised Learning - Linear Regression

Module 4 - Supervised Learning - Classification

Module 5 - Machine Learning Optimization

Module 6 - Neural Networks and Deep Learning

Module 7 - Natural Language Processing

Module 8 - Emerging Topics in AI