Machine Learning fundamentals using Python
In this course we will learn the most important aspects of 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.
Anyone that wants to use machine learning to solve real-life problems.
Good knowledge of the Python programming language is a prerequisite (see Python fundamentals).
- 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
- 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
- Building, validating and using Machine Learning models with Scikit-learn
Classroom teaching, focused on practical examples and supported by in-depth exercises and individual practice.
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