Machine Learning by Michael pyrcz from UT Austin
00 Machine Learning- Introduction.mp4
01 Machine Learning- Spatial, Subsurface.mp4
02 Machine Learning- Probability & Statistics.mp4
03 Machine Learning- Workflow Construction and Coding.mp4
04 Machine Learning- Data Preparation.mp4
05 Machine Learning- Multivariate Analysis.mp4
05b Machine Learning- Curse of Dimensionality.mp4
05c Machine Learning- Feature Selection.mp4
05d Machine Learning- Feature Transformations.mp4
06 Machine Learning- Intro to Machine Learning.mp4
07 Machine Learning- Clustering.mp4
08 Machine Learning- Dimensionality Reduction.mp4
08b Machine Learning- Principal Component Analysis.mp4
08c Machine Learning- Multidimensional Scaling.mp4
08d Machine Learning- Random Projection.mp4
09 Machine Learning- Norms.mp4
09b Machine Learning- Linear Regression.mp4
10 Machine Learning- Ridge Regression.mp4
10b Machine Learning- LASSO Regression.mp4
10c Machine Learning- Polynomial Regression.mp4
10d Machine Learning- Training and Testing.mp4
10e Machine Learning- Metrics for Tuning Hyperparameters.mp4
10g Machine Learning- Isotonic Regression.mp4
11 Machine Learning- k-Nearest Neighbors.mp4
11b Machine Learning- Computational Complexity.mp4
11c Machine Learning- k-Nearest Neighbors Considerations.mp4
11d Machine Learning- Bayesian Linear Regression.mp4
11e Machine Learning- Markov Chain Monte Carlo.mp4
11f Machine Learning- Bayesian Regression Example.mp4
12 Machine Learning- Naive Bayes.mp4
13 Machine Learning- Time Series Analysis.mp4
14 Machine Learning- Decision Tree.mp4
15 Machine Learning- Random Forest.mp4
15b Machine Learning- Gradient Boosting.mp4
16 Machine Learning- Support Vector Machines.mp4
17 Machine Learning- Artificial Neural Networks.mp4
