Introduction
Description will be added soon.
Schedule
Day 1:
- Machine learning fundamentals
- Features and labels
- Training and testing
- Types of machine learning
- Scikit-learn API
- Overview of modules and classes
- Common process
- Unsupervised machine learning
- Outlier detection
- Clustering
- Feature engineering
- Principal Components Analysis
- Feature selection
- One-hot encoding
- Bag-of-words representation
- TF-IDF
Day 2:
- Classification
- K-Nearest Neighbour
- Decision Tree Classifier
- Random Forest
- Neural Network
- Regression
- Linear Regression
- Polynomial Regression
- Support Vector Regression
- Model evaluation
- Measuring performance
- Overfitting and underfitting
- Cross validation
- Model selection
- Pipelines and grid search
- Where to go from here?
About your instructor
Jeroen is an RStudio Certified Instructor who enjoys visualizing data, building machine learning models, and automating things using either Python, R, or Bash. Previously, he was an assistant professor at Jheronimus Academy of Data Science and a data scientist at Elsevier in Amsterdam and various startups in New York City. He is the author of Data Science at the Command Line. Jeroen holds a PhD in machine learning from Tilburg University and an MSc in artificial intelligence from Maastricht University.
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