Machine Learning — EdX
This course ranks 6th in the top best online Machine Learning courses. This is the most advanced course on the list, with the greatest math prerequisite. Linear algebra, calculus, probability, and programming are all skills you'll need. The course includes fascinating programming assignments in Python or Octave, although neither language is taught.
The treatment of the probabilistic approach to machine learning is one of the most notable distinctions in this course. This course would be an excellent companion to reading a textbook like Machine Learning: A Probabilistic Perspective, which is one of the most recommended data science books in Master's degrees.
Course structure:
- Maximum Likelihood Estimation, Linear Regression, Least Squares
- Ridge Regression, Bias-Variance, Bayes Rule, Maximum a Posteriori Inference
- Nearest Neighbor Classification, Bayes Classifiers, Linear Classifiers, Perceptron
- Logistic Regression, Laplace Approximation, Kernel Methods, Gaussian Processes
- Clustering, K-Means, EM Algorithm, Missing Data
- Maximum Margin, Support Vector Machines (SVM), Trees, Random Forests, Boosting
- Mixtures of Gaussians, Matrix Factorization
- Non-Negative Matrix Factorization, Latent Factor Models, PCA and Variations
- Markov Models, Hidden Markov Models
- Continuous State-space Models, Association Analysis
- Model Selection, Next Steps
Many of the concepts listed are addressed in other beginner-level courses, but the math isn't simplified here. If you've previously acquired these concepts and want to study more about the mathematics underpinning machine learning, as well as work on programming projects that derive some of the algorithms, this course is for you.
Provider: Columbia
Cost: Free to audit, $300 for Certificate
Rate:N/A
Enroll here:https://tinyurl.com/3rz7rr9p
