Introduction to Machine Learning

Level:

Beginner

Date:

On demand

Duration:

5 days

Dates

Data Science trainings and workshops can be held online, at your location – and anywhere else in the world. Please contact us for details. Contact >>

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.

 

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Audience

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

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.

 

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