Machine Learning


Duration: 2 days
Price: On request
Description:

This Python course is for people who are already familiar with Python and want to learn more about Data Science and Machine Learning.

The course

In this two day course you will get an extensive overview of the machine learning landscape. It will give you a head start so you can start to apply machine learning yourself. You'll learn about supervised and unsupervised machine learning, about regression and classification algorithms and a whole lot more. The course is hands-on, so be prepared to work on some exercises. You'll be using the open source and battle-tested scikit-learn framework and Jupyter Notebooks.

Who should take this course?

Professionals who working with data and are interested in learning more about data science and machine learning. The course offers both a theoretical framework as practical hands-on coding exercises.

Learning objectives

In this course, you will learn:

  • The machine learning landscape
  • Popular algorithms, their applicability & limitations
  • To choose between various algorithms
  • How to create and train models
  • Practical use cases
Topics
  • Supervised learning
  • Unsupervised learning
  • Regression
  • Classification
  • Correlations
  • Errors/Residuals
  • Overfitting/Underfitting
  • Model validation
  • ROC curve and Area Under Curve (AUC)
  • Feature Engineering
  • Hyperparameter tuning
  • Stacking, Bagging & Boosting
Machine learning models:
  • Linear regression
  • Logistic Regression
  • Naive Bayes
  • K-Nearest Neighbors
  • Random Forest
  • K-Means
Location

This is a virtual class and will take place via Zoom.

Language

Depending on the nationalities of the participants, the course will be in either English or Dutch.

Prerequisites
  • Basic Python skills. It is no problem if you are not fluent, the focus will be mostly on the Machine Learning concepts.
  • No installations needed. The course takes place on an online virtual environment.



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