In this post, the information regarding new free course on machine learning launched by MIT OpenCourseware. In case, you are a beginner data scientist or ML Engineer, you will find this course to be very useful.
Here is the URL to the free course on machine learning: https://bit.ly/37iNNAA.
This course, titled as Introduction to Machine Learning, introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences.
Here are some of the key topics for which lectures can be found:
- Introduction to machine learning (ML)
- Features of ML models
- Linear models such as regression, logistic regression
- Decision trees & nearest neighbors algorithm
- Gradient descent algorithm
- Neural networks
- Convolutional neural networks (CNN)
- Recurrent neural networks (RNN)
- State machines & Markov decision processes
- Reinforcement learning
- Recommender systems
Each of the lectures consists of exercises, Lab work, homework etc which can prove to be useful for learning purpose.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
- Agentic Reasoning Design Patterns in AI: Examples - October 18, 2024
- LLMs for Adaptive Learning & Personalized Education - October 8, 2024
- Sparse Mixture of Experts (MoE) Models: Examples - October 6, 2024
I found it very helpful. However the differences are not too understandable for me