Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
1. Grasping Classification via Nearest Neighbors
- The kNN algorithm
- Computing distance
- Selecting an optimal k
- Preparing data for kNN application
- Why is the kNN algorithm considered lazy?
2. Grasping Naive Bayes
- Core concepts of Bayesian methods
- Probability
- Joint probability
- Conditional probability with Bayes' theorem
- The Naive Bayes algorithm
- Naive Bayes classification
- The Laplace estimator
- Utilizing numeric features with Naive Bayes
3. Grasping Decision Trees
- Divide and conquer
- The C5.0 decision tree algorithm
- Selecting the best split
- Pruning the decision tree
4. Grasping Classification Rules
- Separate and conquer
- The One Rule algorithm
- The RIPPER algorithm
- Deriving rules from decision trees
5. Grasping Regression
- Simple linear regression
- Ordinary least squares estimation
- Correlations
- Multiple linear regression
6. Grasping Regression Trees and Model Trees
- Incorporating regression into trees
7. Grasping Neural Networks
- From biological to artificial neurons
- Activation functions
- Network topology
- The number of layers
- The direction of information travel
- The number of nodes in each layer
- Training neural networks with backpropagation
8. Grasping Support Vector Machines
- Classification with hyperplanes
- Finding the maximum margin
- The case of linearly separable data
- The case of non-linearly separable data
- Utilizing kernels for non-linear spaces
9. Grasping Association Rules
- The Apriori algorithm for association rule learning
- Measuring rule interest – support and confidence
- Constructing a set of rules with the Apriori principle
10. Grasping Clustering
- Clustering as a machine learning task
- The k-means algorithm for clustering
- Utilizing distance to assign and update clusters
- Selecting the appropriate number of clusters
11. Evaluating Performance for Classification
- Working with classification prediction data
- A closer look at confusion matrices
- Using confusion matrices to measure performance
- Beyond accuracy – other measures of performance
- The kappa statistic
- Sensitivity and specificity
- Precision and recall
- The F-measure
- Visualizing performance tradeoffs
- ROC curves
- Estimating future performance
- The holdout method
- Cross-validation
- Bootstrap sampling
12. Tuning Stock Models for Enhanced Performance
- Utilizing caret for automated parameter tuning
- Constructing a simple tuned model
- Customizing the tuning process
- Improving model performance with meta-learning
- Understanding ensembles
- Bagging
- Boosting
- Random forests
- Training random forests
- Evaluating random forest performance
13. Deep Learning
- Three Classes 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.
