Data science & AI masterclass

hands-on setting up a data science project.

2 or 3 days

What does the training include?

In this two- or three-day training, we'll take you hands-on with setting up a data science project, and what's involved. You will learn everything about machine learning algorithms, preparing a dataset, training and validating the model and bringing and keeping a model live. Python or prior programming knowledge is useful but not required.

Part 1: What is data science and how to set up a project

· What do we mean by data science?

· Why is data science so popular?

· What is involved in a data science project?

o Intake phase

o Construction phase

o Production phase

· What types of algorithms are there?

· Key concepts:

o Development kit & validation kit

o Overfit & underfit

· How do the best-known algorithms work?

· How do I build my first model?

Part 2: How do I ensure appropriate data?

· How do I clean my data?

· How do I structure my data?

· How do I visualize my data?

· How do I create my first features?

· How do I know if a feature is good?

· How do I select the best feature?

Part 3: How do I create a robust model?

· How do I validate my model?

· How do I define a baseline?

· How do I create a good validation set?

· What validation methods are available?

· What are the validation metrics?

· How do I visualize the results?

· How do I use scikit-learn for validation?

Part 4: Neural Networks and More

· Neural networks:

o What are the basic concepts?

o When is it better to opt for a neural network?

o What activation features are available?

o What types of neural networks are there?

o What other important terms are there?

o How to make your network robust

o What Python libraries are available?

· Which tooling is useful to take a look at?

· Wrap-up

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For whom?

This masterclass is intended for professionals who want a practical introduction to data science and AI. It is suitable for both beginners with no programming experience and people with some Python knowledge who want to learn how to set up and execute a data science project, including training and implementing machine learning models.

What will you learn?

Setting up a data science project: You understand the phases of intake, construction and production.

Preparing datasets: You can clean, structure, visualize and create and select features.

Building machine learning models: You can train, validate, and improve simple models.

Evaluate and validate models: You know how to define a baseline, build validation sets, and apply appropriate validation methods and metrics.

Understanding and applying neural networks: You know the basic principles of neural networks and know when and how to use them effectively.

Use useful tools: You are familiar with tools such as scikit-learn and relevant Python libraries to automate and optimize data science tasks.

Interested in this training?

Feel free to contact us, we'll be happy to tell you more about the options.

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