Machine Learning 401 : Zero to Mastery Machine Learning
Learn Machine Learning Master Level, Deep Learning, Reinforcement Learning, Application of Machine Learning
What you’ll learn
- Introduction to machine learning.
- Linear prediction
- Maximum likelihood and linear prediction
- Ridge, nonlinear regression with basis functions and Cross-validation
- Bayesian learning
- Gaussian processes for nonlinear regression
- Bayesian optimization, Thompson sampling and bandits
- Decision trees
- Random forests
- Spring break
- Random forests applications
- Unconstrained optimization
- Gradient descent and Newton’s method
- Logistic regression, IRLS and importance sampling
- Neural networks
- Deep learning
- Importance sampling and MCMC
- Constrained optimization, Lagrangians and duality
- Application to penalized maximum likelihood and Lasso
- Deep Learning
- Reinforcement Learning