Introduction to Machine Learning




On demand


5 days

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  • Argentina  (Buenos Aires): 2020-05-18 till 2020-05-22
  • Estonia (Tallinn): 2020-06-15 till 2020-06-19
  • Poland (Warsaw): 2020-06-22 till 2020-06-26
  • Austria (Wiener Neustadt): 2020-06-29 till 2020-07-03
  • South Africa (Cape Town): 2020-07-06 till 2020-07-10
  • South Africa (Johannesburg): 2020-07-13 till 2020-07-17

Course description

This course is intended for technical analysts or mid-level managers who are willing to do their first steps in Machine Learning. 

The dynamic of this course contemplates both theoretical content explanation and practical activities, with a special focus on the last part (60-65% of the course). The main goal is that participants face “real” problems, experience consequential challenges and learn to come up with a solution – guided by the instructor at all stages of this process.



Analysts/technical managers with at least 1 year of programming experience (Ideally, also experience in Python)

Course outline

Day 1:

Machine Learning: Introduction and explanation of main concepts.

About Python/Jupyter

Overview of main Python libraries to be used

Exploratory Data Analysis: in theory

Exploratory Data Analysis: in practice


Day 2:

Supervised Learning: Introduction and explanation of main concepts.

  • Explanation of Training Process: Training/Validation/Testing, Cross-validation
  • Recap: Regression vs Classification
  • Cost/Loss functions
  • What is done in training? Minimization of Loss Function. Example Algorithms
  • Performance Evaluation
  • Steps for successful Model building



Day 3:


  • Classification Algorithms
  • Ensemble Algorithms
  • Performance Evaluation


Day 4:

Unsupervised Learning

  • Unsupervised Algorithms
  • Ensemble Algorithms
  • Performance Evaluation

From lab to production: challenges and common problems

The importance of distributed computing in Machine Learning


Day 5:

Introduction to neural networks

  • Definition
  • Main Concepts
  • Tensorflow Playground demo
  • Activation Functions
  • NN training process
  • Multi-class Problems and Softmax
  • Convolutional NN
  • Keras Demo and Explanation of Keras Library
  • Transfer Learning
  • Hyperparameter Tuning

Deep Learning: What is it and where can it be applied?

Machine Learning in my organization: How can I implement ML considering the current problems we face?

Final summary, questions, suggestions, etc.

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