Machine Learning fundamentals using Python

In this course we will learn the most important aspects of Artificial Intelligence (AI) and more specifically Machine Learning (ML). We will begin by looking into the different problems that can be solved with ML and when and why to use ML. Then we dive into the inner workings of ML.

This course will get you started with the fundamentals of ML. We will put this knowledge into practice by building (and using) our own machine learning models in Python with Scikit-learn, a popular Python library for ML.

Schedule

No public sessions are currently scheduled. We will be pleased to set up an on-site course or to schedule an extra public session (in case of a sufficient number of candidates). Interested? Please contact ABIS.

Intended for

Anyone that wants to use machine learning to solve real-life problems.

Background

Good knowledge of the Python programming language is a prerequisite (see Python fundamentals).

Main topics

  • Basic concepts & techniques in Machine Learning:
  • Supervised learning versus unsupervised learning
  • Training & testing data; cross-validation
  • Accuracy; confusion matrix
  • Underfitting versus overfitting; the bias-variance tradeoff; validation curves
  • Regularization
  • The different models to be trained with Machine Learning:
  • Linear & polynomial regression; Ridge regression
  • Logistic regression & Classification
  • Clustering: K-means & hierarchical clustering
  • Anomaly detection
  • Dimensionality reduction: PCA & LDA
  • Decision trees & random forests
  • A short introduction into neural networks and deep learning
  • Building, validating and using Machine Learning models with Scikit-learn

Training method

Classroom teaching, focused on practical examples and supported by in-depth exercises and individual practice.

Duration

3 days.

Course leader

Peter Vanroose.

Reviews

the explanation of the course is clear and exercise is good and handable

 
  (, )

Peter is an amazing instructor. Course is awesome.

 
  (, )

Coherent, focused, hands on

 
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