Machine Learning with Microsoft Azure ML Studio and Python

Trainer: Vlad Iliescu
Duration: 2 days

“AI is the new electricity” says Andrew Ng, one of the pioneers of AI and online learning. His remark may be an overstatement, bit there’s no denying the impact AI has had, is having, and will continue to have on our lives, at both personal and professional levels.

One of the core instruments of AI is Machine Learning, and by attending this workshop you will get the chance to learn about ML and understand how you can use it effectively. During this workshop, you will learn about the types of problems solved by Machine Learning, the most used algorithms, the metrics used to compare their performance, and how to improve said performance.

After attending this workshop, you will be able to assess a Machine Learning problem, decide how to approach it using ML, train and evaluate a predictive model, and fine-tune its performance to match your accuracy criteria.

Agenda

  1. Who’s who
    Introduces the speaker and the audience, sets the goals for the training.

  2. Introduction to Machine Learning

    1. Lecture
      1. What is Machine Learning and how it relates to Artificial Intelligence.
      2. The types of Machine Learning and the problems they’re designed to solve
    2. Lab
      1. Setup & Hello World
      2. Working with data in Python
      3. Working with data in Azure ML Studio
  3. Classification

    1. Lecture
      1. What is classification, use cases
      2. Most used algorithms, how they work, advantages and disadvantages
      3. Metrics used for determining performance
    2. Lab
      1. Classifying data in Python
      2. Classifying data in Azure ML Studio
      3. Comparing algorithm performance using metrics
  4. Regression

    1. Lecture
      1. What is regression, use cases
      2. Most used algorithms, how they work, advantages and disadvantages
      3. Metrics used for determining performance
    2. Lab
      1. Regression in Python
      2. Regression in Azure ML Studio
      3. Comparing algorithm performance using metrics
  5. Ensemble Learning

    1. Lecture
      1. What are ensemble algorithms, use cases
      2. Most used algorithms for classification and regression
    2. Lab
      1. Ensembles in Python
      2. Ensembles in Azure ML Studio
      3. Measuring performance boost obtained from using ensembles
  6. Optimizing models

    1. Lecture
      1. Overfitting and underfitting
      2. Feature engineering
      3. Hyperparameter optimization
    2. Lab
      1. Compete with the other attendees in analysing an unknown dataset and using it to train a predictive model
      2. Once all models are trained, we will score their performance over an unknown test dataset, and reveal the best-performing model.
  7. Conclusion

    1. Where to next
    2. Course summary

Prerequisites

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