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Course Outline
1. Mastering Classification via Nearest Neighbors
- The kNN algorithm
- Distance calculation techniques
- Selecting the optimal value for k
- Data preparation for kNN implementation
- Understanding the 'lazy' nature of the kNN algorithm
2. Mastering Naive Bayes
- Fundamental concepts of Bayesian methods
- Probability theory
- Joint probability
- Conditional probability using Bayes' theorem
- The Naive Bayes algorithm
- Naive Bayes classification techniques
- The Laplace estimator
- Handling numeric features with Naive Bayes
3. Mastering Decision Trees
- Divide and conquer strategy
- The C5.0 decision tree algorithm
- Selecting the optimal split
- Pruning techniques for decision trees
4. Mastering Classification Rules
- Separate and conquer approach
- The One Rule algorithm
- The RIPPER algorithm
- Deriving rules from decision trees
5. Mastering Regression
- Simple linear regression
- Ordinary least squares estimation
- Correlation analysis
- Multiple linear regression
6. Mastering Regression and Model Trees
- Integrating regression into tree structures
7. Mastering Neural Networks
- From biological to artificial neurons
- Activation functions
- Network topology
- Layer configuration
- Information flow direction
- Determining the number of nodes per layer
- Training neural networks using backpropagation
8. Mastering Support Vector Machines
- Classification using hyperplanes
- Identifying the maximum margin
- Handling linearly separable data
- Handling non-linearly separable data
- Utilizing kernels for non-linear spaces
9. Mastering Association Rules
- The Apriori algorithm for association rule learning
- Evaluating rule interest through support and confidence
- Constructing rule sets using the Apriori principle
10. Mastering Clustering
- Clustering as a machine learning task
- The k-means clustering algorithm
- Using distance metrics for cluster assignment and updates
- Selecting the appropriate number of clusters
11. Evaluating Performance for Classification
- Working with classification prediction data
- In-depth analysis of confusion matrices
- Utilizing confusion matrices for performance measurement
- 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 Model Performance
- Automated parameter tuning with caret
- Creating a basic tuned model
- Customizing the tuning process
- Enhancing model performance through meta-learning
- Understanding ensemble methods
- 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.
