Course Code: pymlbspk
Duration: 21 hours
Prerequisites:

Basic knowledge of statistical concepts is desirable though not compulsary.

A prior knowledge of programming (preferably Python) is highly recommended.

Overview:

In this training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. Python will be used as the programming language.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.

The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
Course Outline:

Quick Introduction to Python

Quick go through with Python

Getting Started with Python Libraries for Machine Learning

Introduction to Machine Learning

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 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