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Course Outline
1. Grasping Classification via Nearest Neighbors
- The kNN algorithm
- Computing distance
- Selecting an optimal k
- Preparing data for kNN
- Why the kNN algorithm is considered lazy
2. Exploring Naive Bayes
- Core concepts of Bayesian methods
- Probability
- Joint probability
- Conditional probability and Bayes' theorem
- The Naive Bayes algorithm
- Naive Bayes classification
- The Laplace estimator
- Applying numeric features with Naive Bayes
3. Analyzing Decision Trees
- Divide and conquer approach
- The C5.0 decision tree algorithm
- Selecting the best split
- Pruning decision trees
4. Investigating Classification Rules
- Separate and conquer strategy
- 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. Understanding Regression and Model Trees
- Incorporating regression into trees
7. Comprehending Neural Networks
- From biological to artificial neurons
- Activation functions
- Network topology
- Number of layers
- Direction of information flow
- Node count per layer
- Training neural networks using backpropagation
8. Mastering Support Vector Machines
- Classification using hyperplanes
- Identifying the maximum margin
- Scenarios with linearly separable data
- Scenarios with non-linearly separable data
- Utilizing kernels for non-linear spaces
9. Delving into Association Rules
- The Apriori algorithm for association rule learning
- Evaluating rule interest through support and confidence
- Constructing a rule set using the Apriori principle
10. Examining Clustering
- Clustering as a machine learning task
- The k-means clustering algorithm
- Using distance for cluster assignment and update
- Determining the appropriate number of clusters
11. Assessing Classification Performance
- Handling classification prediction data
- In-depth analysis of confusion matrices
- Evaluating performance via confusion matrices
- Beyond accuracy – alternative performance metrics
- The kappa statistic
- Sensitivity and specificity
- Precision and recall
- The F-measure
- Visualizing performance trade-offs
- ROC curves
- Estimating future performance
- The holdout method
- Cross-validation
- Bootstrap sampling
12. Optimizing Models for Enhanced Performance
- Leveraging caret for automated parameter tuning
- Developing a basic tuned model
- Customizing the tuning workflow
- Boosting 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. Discussion of Specific Application Areas
21 Hours
Testimonials (1)
Very flexible.
