Machine learning with scikit-learn


Use python to build machine learning models for regression and classification using scikit-learn. Learn how to create, train and evaluate models. Be familiar with the common machine learning algorithms available from scikit-learn.

Duration: 1 day

Who is it for: Anyone who has an interest in machine learning using scikit-learn

Layout: This is an intense, fast paced course containing a lot of theory and modelling content, pdfs and coding exercises.


Introduction to Machine Learning, Deep Learning, and Generative AI.

  • Overview of machine learning concepts.
  • Introduction to supervised, unsupervised, and reinforcement learning.

Data Pre-processing.

  • Techniques for cleaning and preparing data for machine learning.
  • Missing Values, Imputers, Formatting
  • One Hot encoding
  • Feature Scaling
  • The scikit-learn object model.

Regression Analysis.

  • Understanding regression analysis and its applications.
  • Building regression models using Python
  • Simple Linear
  • Multiple Linear
  • Polynomial
  • Model Optimization


  • Introduction to classification models.
  • Logistic Regression
  • KNN Classification
  • Multi Class Classification


  • Overview of clustering techniques.
  • KNN Clustering
  • Divisive and Agglomerative Clustering
  • Clustering to perform image compression

Rule Association Learning.

  • Implementing rule association learning for pattern discovery.
  • Apriori rule association
  • Eclat rule association

Dimensionality Reduction.

  • Know how to use Dimensionality Reduction as a tool for feature engineering
  • Principal Component Analysis
  • Latent Discriminative Analysis
  • Kernel Principal Component Analysis

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