Python: Introduction to Machine Learning with Python

Duration: 3 days

Prerequisites: Basic knowledge of programming with Python


  • Understanding machine learning and its subfields
  • Getting used to common machine learning algorithms
  • Making the right choice about what algorithm to use depending on the case
  • Acquiring expertise in analyzing algorithms results and performance metrics


  1. Introduction
    • Supervised learning, unsupervised learning and reinforcement learning
    • Classification, regression, structural prediction
    • Model evaluation: metrics
    • Hyperparameter selection, model selection
    • Initiation to Scikit-learn
    • Data types and methods selection guide
  2. Classification: Introduction with Optical Character Recognition (OCR)
    • K nearest neighbors’ algorithm (KNN)
    • Decision trees
    • Ensemble methods
    • Support Vector Machines (SVM)
    • Results visualization
  3. Classification: Advanced concepts with sentiment analysis
    • Data preprocessing for learning algorithms
    • Dimensionality reduction
    • Batchwise training
    • Interpretability
  4. Regression
    • Linear regression
    • Non-linear regression with kernel methods
    • Outlier detection and handling
    • Time series: Challenges, decomposition and predictive methods
    • Time series: non-stationary regression and auto-regressive models
  5. Recommendation systems, case study
    • Collaborative filtering per user
    • Collaborative filtering per item
    • Advanced concepts and algorithms
  6. Unsupervised learning
    • Clustering: K-means, hierarchical clustering, density methods
    • Dimensionality reduction: PCA, t-SNE,
    • Generative models: Introduction to autoencoders and variational autoencoders
  7. Practical debugging guide
    • Overfitting test
    • Data pipelines test
  8. Exploration of alternative metrics
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