AI and Machine Learning fundamentals using Python

During this three-day training at ABIS, you learn to analyse data and build data models by using ML tools like scikit-learn and PyTorch.

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 AI and 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 a training?

Delivered as a live, interactive training – available in-person or online, or in a hybrid format. Training can be implemented in English, Dutch, or French.

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Public training calendar

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 let us know.

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). Knowledge of Pandas (see course Python for data analytics) is an advantage.

Main topics

  • Basic concepts & techniques in AI and Machine Learning:
    • Supervised learning versus unsupervised learning
    • Training & testing data; cross-validation
    • Accuracy; confusion matrix
    • Under-fitting versus over-fitting; the bias–variance trade-off; validation curves
    • Regularisation
    • Automating the model selection
  • 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.

Certificate

At the end of the session, the participant receives a "Certificate of Completion".

Duration

3 days.

Course leader

Peter Vanroose (ABIS).

Reviews

4.0/5 (based on 18 evaluations; the most recent ones are shown below)

It was a good course for anyone who does want to start machine learning.

 
  (, )

I enjoyed the course it provided a very good introduction into the different aspects of ML

 
  (, )

Excellent trainer with in-depth knowledge and excellent interpersonal skills. Course did holistic coverage of ML life cycle, that was great.

 
  (, )

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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