Launch your machine learning career with MIT
By Katherine Ouellette
Machine learning is one of the top three fastest-growing career fields with 86% of global organizations turning to this technology to transform businesses, according to the World Economic Forum. After building core machine learning skills, equip yourself with the knowledge for these five jobs with online courses and resources on MIT Learn, home to thousands of educational materials from MIT Open Learning.
Machine learning engineer
Machine learning engineers design, train, and deploy models that learn from data to solve real-world problems and integrate intelligent decision-making into products.
Fields
- E-commerce
- Finance
- Healthcare
- Technology
Courses
- Computational Methods of Scientific Programming
- Data Analysis: Statistical Modeling and Computations in Applications
- Introduction to Machine Learning
- Machine Learning with Python: From Linear Models to Deep Learning
- Matrix Calculus for Machine Learning and Beyond
- Mathematics of Machine Learning
- Machine Learning for Healthcare
- Machine Learning, Modeling, and Simulation Principles
- Applying Machine Learning to Engineering and Science
- Designing and Building AI Products and Services
- FinTech: Shaping the Financial World
Reinforcement learning engineer
Reinforcement learning engineers build and train agents that learn to make decisions through trial and error to maximize long-term rewards in dynamic environments.
Fields
- Autonomous vehicles
- Gaming
- Logistics
- Robotics
Courses
- Computational Methods of Scientific Programming
- Mathematics of Big Data and Machine Learning
- Principles of Autonomy and Decision Making
- Machine Learning, Modeling, and Simulation Principles
- Machine Learning with Python: From Linear Models to Deep Learning
- Learning Time Series with Intervention
- Applying Machine Learning to Engineering and Science
- Deploying AI for Strategic Impact
- Machine Learning for Healthcare
- Machine Vision
- Advances in Computer Vision
- Robotic Manipulation
- Cognitive Robotics
- Underactuated Robotics
- Visual Navigation for Autonomous Vehicles
- Supply Chain Management: Leading with AI and Digital Transformation
AI model optimization engineer
AI model optimization engineers improve model efficiency by reducing size, latency, and power consumption for deployment on various hardware platforms.
Fields
- Autonomous vehicles
- Consumer electronics
- Gaming
- Logistics
- Robotics
Courses
- Computational Methods of Scientific Programming
- Principles of Autonomy and Decision Making
- Optimization Methods in Business Analytics
- Risk Aware and Robust Nonlinear Planning
- Machine Learning, Modeling, and Simulation Principles
- Applying Machine Learning to Engineering and Science
- Deploying AI for Strategic Impact
- Designing and Building AI Products and Services
- Machine Vision
- Advances in Computer Vision
- Robotic Manipulation
- Cognitive Robotics
- Visual Navigation for Autonomous Vehicles
Machine learning infrastructure engineer
Machine learning infrastructure engineers develop and maintain scalable systems, pipelines, and tools that support data processing, model training, and deployment at scale.
Fields
- AI platform development
- Cloud computing
- Enterprise software
- Telecommunications
Courses
- Introduction to Algorithms
- Computer Systems Engineering
- Operating System Engineering
- Computational Methods of Scientific Programming
- Mathematics of Big Data and Machine Learning
- Models in Engineering
- Model-Based Systems Engineering: Documentation and Analysis
- Quantitative Methods in Systems Engineering
- Risk Aware and Robust Nonlinear Planning
- Mechatronics
- FinTech: Shaping the Financial World
Deep learning researcher
Deep learning researchers explore novel neural network architectures and leverage modern computing hardware while investigating further improvements to these networks and the principles underlying their performance.
Fields
- Academia
- Research institutions
- Technology
Courses
- Brains, Minds, and Machines
- Minds and Machines
- Introduction to Deep Learning
- Machine Learning with Python: From Linear Models to Deep Learning
- Deep Learning: Mastering Neural Networks
- Natural Language Processing (Part 1)
- Natural Language Processing (Part 2)
- Advances in Computer Vision
These courses and materials are available through MIT OpenCourseWare, MITx, MITx MicroMasters, and MIT xPROⓇ, which are part of MIT Open Learning. OpenCourseWare offers free, online, open educational resources from more than 2,500 courses that span the MIT undergraduate and graduate curriculum. MITx offers high-quality massive open online courses adapted from the MIT classroom for learners worldwide. MicroMasters Programs are graduate-level digital credentials from MIT that help learners accelerate their careers and Master’s degrees. MIT xPROⓇ provides professional development opportunities to a global audience via online courses and blended programs.
Launch your machine learning career with MIT was originally published in MIT Open Learning on Medium, where people are continuing the conversation by highlighting and responding to this story.