Machine Learning with Python - Session 2

Unsupervised learning and model evaluation

Session 2

1. Unsupervised Learning

  • Explanation of unsupervised learning
  • Overview of clustering algorithms:
  • K-means clustering
  • Hierarchical clustering
  • Overview of dimensionality reduction techniques:
  • Principal Component Analysis (PCA)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)
  • Demonstration of implementing unsupervised learning algorithms

2. Model Evaluation

  • Techniques for evaluating machine learning models
  • Overview of performance metrics:
  • Accuracy
  • Precision, Recall, F1-score
  • ROC curve
  • Cross-validation and model selection

3. Hands-on Exercise

  • Participants work on coding exercises and small projects to evaluate and fine-tune machine learning models using the techniques covered in the session

Instructor
Muniba Talha is a data scientist, educator and Python enthusiast.

The webinar will not be recorded. Language and materials are in English.

Dato

Start21. okt 2024 17:00
Slut21. okt 2024 20:00

Sted

Online

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