This course is for you, who wants to learn how to use Python in your work with Machine Learning. Maybe you are working with data on a daily basis, and you would like to gain more value by using Machine Learning models.
You are expected to have a basic understanding of programming in Python or other programming languages. Concepts such as variables, loops, if-statements and functions should be familiar.
The course consists of a theoretical overview of models and relevant theory, and with a strong focus on hands-on assignments. This way you will quickly and easily be able to use your newly aquired skills in practice.
Module 1: Tools
- Data science requires a series of libraries that you shold be familiar with. We will go through pandas, scikit-learn and more.
Module 2: Data and plotting
- In real life a lot of time is spent on loading and cleaning data. We will go through procedures for loading data, performing imputation and one-hot encoding.
Module 3: Supervised learning models
- Modelling is essential to make good predictions. A subset of models from scikit-learn will be presented, one of which will be Random Forest.
Module 4: Model Evaluation
- To ensure consistent results on new data, it is important to evaluate a model with the proper techniques. We will use cross-validation to validate the model, and go into tuning of hyperparameters to avoid overfitting.
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Do you have any course related questions, please contact
- Charlotte Heimann
- +45 72203147