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Course Outline
1. Grasping classification via nearest neighbors
- The kNN algorithm
- Distance calculation
- Selecting an optimal k
- Data preparation for kNN application
- Why is the kNN algorithm considered lazy?
2. Exploring naive Bayes
- Core concepts of Bayesian methods
- Probability fundamentals
- Joint probability
- Conditional probability through Bayes' theorem
- The naive Bayes algorithm
- Naive Bayes classification
- The Laplace estimator
- Incorporating numeric features with naive Bayes
3. Exploring decision trees
- Divide and conquer strategies
- The C5.0 decision tree algorithm
- Selecting the optimal split
- Pruning the decision tree
4. Exploring classification rules
- Separate and conquer approach
- The One Rule algorithm
- The RIPPER algorithm
- Deriving rules from decision trees
5. Exploring regression
- Simple linear regression
- Ordinary least squares estimation
- Correlations
- Multiple linear regression
6. Exploring regression trees and model trees
- Integrating regression into trees
7. Exploring neural networks
- Transitioning from biological to artificial neurons
- Activation functions
- Network topology
- Layer count
- Information flow direction
- Node count per layer
- Training neural networks using backpropagation
8. Exploring Support Vector Machines
- Classification using hyperplanes
- Identifying the maximum margin
- Scenarios involving linearly separable data
- Scenarios involving non-linearly separable data
- Utilizing kernels for non-linear spaces
9. Exploring association rules
- The Apriori algorithm for association rule learning
- Evaluating rule interest – support and confidence
- Constructing a rule set based on the Apriori principle
10. Exploring clustering
- Clustering as a machine learning task
- The k-means algorithm for clustering
- Employing distance for cluster assignment and updates
- Selecting the appropriate number of clusters
11. Assessing performance for classification
- Handling classification prediction data
- Examining confusion matrices in detail
- Utilizing confusion matrices to gauge performance
- Beyond accuracy – additional performance metrics
- The kappa statistic
- Sensitivity and specificity
- Precision and recall
- The F-measure
- Visualizing performance tradeoffs
- ROC curves
- Forecasting future performance
- The holdout method
- Cross-validation
- Bootstrap sampling
12. Optimizing stock models for enhanced performance
- Utilizing caret for automated parameter tuning
- Developing a simple tuned model
- Customizing the tuning process
- Enhancing model performance through meta-learning
- Comprehending ensembles
- Bagging
- Boosting
- Random forests
- Training random forests
- Evaluating random forest performance
13. Deep Learning
- Three Categories of Deep Learning
- Deep Autoencoders
- Pre-trained Deep Neural Networks
- Deep Stacking Networks
14. Review of Specific Application Areas
21 Hours
Testimonials (1)
Very flexible.
