Machine Learning

Python Knowledge

This course will be a combination of theory and practical work with specific examples used throughout the event.

Introduction

This section provides a general introduction of when to use 'machine learning', what should be considered and what it all means including the pros and cons. Datatypes (structured/unstructured/static/streamed), data validity/volume, data driven vs user driven analytics, statistical models vs. machine learning models/ challenges of unsupervised learning, bias-variance trade off, iteration/evaluation, cross-validation approaches, supervised/unsupervised/reinforcement.

MAJOR TOPICS

1. Understanding naive Bayes

• Basic concepts of Bayesian methods
• Probability
• Joint probability
• Conditional probability with Bayes' theorem
• The naive Bayes algorithm
• The naive Bayes classification
• The Laplace estimator
• Using numeric features with naive Bayes

2. Understanding decision trees

• Divide and conquer
• The C5.0 decision tree algorithm
• Choosing the best split
• Pruning the decision tree

3. Understanding 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
• Deep Learning

4. Understanding Support Vector Machines

• Classification with hyperplanes
• Finding the maximum margin
• The case of linearly separable data
• The case of non-linearly separable data
• Using kernels for non-linear spaces

5. Understanding clustering

• Clustering as a machine learning task
• The k-means algorithm for clustering
• Using distance to assign and update clusters
• Choosing the appropriate number of clusters

6. Measuring 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

7. Tuning stock models for better performance

• Using caret for automated parameter tuning
• Creating 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

MINOR TOPICS

8. Understanding classification using the nearest neighbors

• The kNN algorithm
• Calculating distance
• Choosing an appropriate k
• Preparing data for use with kNN
• Why is the kNN algorithm lazy?

9. Understanding classification rules

• Separate and conquer
• The One Rule algorithm
• The RIPPER algorithm
• Rules from decision trees

10. Understanding regression

• Simple linear regression
• Ordinary least squares estimation
• Correlations
• Multiple linear regression

11. Understanding regression trees and model trees

• Adding regression to trees

12. Understanding association rules

• The Apriori algorithm for association rule learning
• Measuring rule interest – support and confidence
• Building a set of rules with the Apriori principle

Extras

• Spark/PySpark/MLlib and Multi-armed bandits