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Course Outline

Supervised learning: classification and regression

  • Introduction to Machine Learning in Python: exploring the scikit-learn API
    • Linear and logistic regression
    • Support vector machines
    • Neural networks
    • Random forests
  • Constructing end-to-end supervised learning pipelines with scikit-learn
    • Processing data files
    • Imputing missing values
    • Managing categorical variables
    • Data visualisation

Python frameworks for AI applications:

  • TensorFlow, Theano, Caffe, and Keras
  • Scaling AI with Apache Spark: Mlib

Advanced neural network architectures

  • Convolutional neural networks for image analysis
  • Recurrent neural networks for time-structured data
  • Long short-term memory (LSTM) cells

Unsupervised learning: clustering and anomaly detection

  • Implementing principal component analysis (PCA) with scikit-learn
  • Building autoencoders in Keras

Practical examples of AI-solvable problems (hands-on exercises using Jupyter notebooks), including:  

  • Image analysis
  • Forecasting complex financial series, such as stock prices
  • Complex pattern recognition
  • Natural language processing
  • Recommender systems

Understanding the limitations of AI methods: failure modes, costs, and common challenges

  • Overfitting
  • Bias-variance trade-off
  • Biases in observational data
  • Neural network poisoning

Applied project work (optional)

Requirements

There are no specific prerequisites or prior requirements necessary to participate in this course.

 28 Hours

Custom Corporate Training

Training solutions designed exclusively for businesses.

  • Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
  • Flexible Schedule: Dates and times adapted to your team's agenda.
  • Format: Online (live), In-company (at your offices), or Hybrid.
Investment

Price per private group, online live training, starting from 5200 € + VAT*

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