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Course Outline
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
21 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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Public Training
Please see our public courses
Testimonials (5)
Keeping it short and simple. Creating intuition and visual models around the concepts (decision tree graph, linear equations, calculating y_pred manually to prove how the model works).
Nicolae - DB Global Technology
Course - Machine Learning
It helped me achieve my goal of understanding ML. Much respect for Pablo for giving a proper introduction in this topic, since it becomes obvious after 3 days of training how vast this topic is. I have also enjoyed A LOT the idea of virtual machines you have provided, which had very good latency! It allowed every coursant to do experiments at their own pace.
Silviu - DB Global Technology
Course - Machine Learning
The way practical part, seeing the theory materializing into something practical is great.
Lisa Fekade - Vodacom
Course - Machine Learning
It's just great that all material including the exercises is on the same page and then it gets updated on the fly. The solution is revealed at the end. Cool! Also, I do appreciate that Krzysztof took extra effort to understand our problems and suggested us possible techniques.
Attila Nagy - L M ERICSSON LIMITED
Course - Machine Learning
I liked the lab exercises.
