Multivariate statistics and Machine learning - a full course
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39 learners
What you'll learn
This course includes
- 8.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 33 lessons • 8.5 hours of video
Multivariate statistics and Machine learning - a full course
33 lessons
• 8.5 hours
Multivariate statistics and Machine learning - a full course
33 lessons
• 8.5 hours
- Matrices and matrix operations for beginners 1/2 14:07
- Matrices and matrix operations for beginners 2/2 09:48
- Eigenvectors and eigenvalues - simply explained 11:40
- Eigenvectors and eigenvalues - the math step-by-step 10:59
- Euclidean distance and the Mahalanobis distance (and the error ellipse) 11:01
- Covariance and the correlation matrix - with simple examples 22:21
- PCA : the basics - explained super simple 22:11
- PCA : the math - step-by-step with a simple example 20:22
- PCA : standardization and how to extract components 16:27
- PCA : how to interpret the weights/loadings and Varimax rotation 13:40
- Linear discriminant analysis (LDA) - simply explained 24:26
- MANOVA - explained with a simple example 15:01
- Hotelling's T-square - explained with a simple example 12:02
- Sensitivity and specificity - clearly explained 12:12
- The positive and negative predictive values (PPV and NPV) - simply explained 12:46
- ROC curve - explained 16:23
- Validation techniques - explained with simple examples (Hold-out, cross-validation, LOOCV) 18:56
- How to calculate the likelihood ratio 06:59
- Linear discriminant analysis (LDA) - how to use it as a classifier 23:46
- Logistic regression : the basics - simply explained 20:25
- Logistic regression : how to use it as a classifier 23:52
- K nearest neighbors (KNN) - explained | validation 15:38
- Mahalanobis distance for classification | Machine Learning 10:56
- Decision trees for classification - explained 14:08
- Random forest classification - simply explained 14:01
- Support Vector Machines (SVM) - the basics | simply explained 28:44
- Hierarchical clustering - explained 14:16
- Understanding cluster heat maps 11:48
- k-means clustering - explained 10:54
- Principal component regression (PCR) - explained 14:48
- Partial least squares regression (PLSR) - explained 14:59
- PLS-DA 06:54
- Canonical correlation analysis - explained 16:58
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