Introduction to Machine Learning

Level:

Beginner

Duration:

5 days

Dates & Duration

See available dates for this course!
If there is no date and time available for a certain training you are interested in, please contact us.

See dates  >>

 

This course takes 5 days with 8 hours each (on site) or 4 hours each (online).
The only difference is that there will be less practical exercises in an online course. However, we will hand them over to you and you can still do them on your own and ask our consultants for feedback or help if needed.

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.

Audience

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

Available Languages

This course can be held in English, German and Spanish.

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

Regression

 

Day 3:

Classification

  • 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.

 

Contact us to sign up for this course >>