Course Outline


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.


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


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


  • Spark/PySpark/MLlib and Multi-armed bandits
  21 Hours


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