Python

• The working environment

• Data types

• Data structures (list, dictionary, etc.)

• Flow control (if-else, for-in, etc.)

• Textual interface

• Functions (inc. lambda)

• Working with files

• Object-Oriented Programming (OOP) basics

• Python API's

  • Python Standard Library

  • Modules and packages

  • datetime

  • Regular expressions

EDA

• Pre-processing with pandas

  • Basic concepts

  • Indexation and filtering

  • Aggregations and advanced manipulations

• Mathematical packages (scipy, numpy)

• Visualization packages (matplotlib, seaborn)

• Working with data resources (JSON files, databases & web)

Machine Learning

• Concepts

  • Supervised & unsupervised learning

  • Pipelines – Transformers & Estimators

  • Feature engineering

  • Dimensionality reduction

  • Model selection – Cross-validation & grid search

  • Overfitting & regularization

  • Ensemble methods – Voting, bagging & boosting

  • Imbalanced data

  • Anomaly detection

  • Clustering

  • Metrics and similarities

  • Scoring

• Models

  • Linear regression

  • Logistic regression

  • Decision trees (inc. random forest)

  • K-nearest neighbors (k-NN)

  • K-means

  • Agglomerative clustering

Deep Learning & Generative AI

• Deep Learning

  • Introduction to Neural Networks

  • Deep Learning with Pytorch

  • Deep Learning Architectures

• Generative AI Fundamentals

  • Transformers + Diffusion architectures + Embeddings

  • HuggingFace

  • Behind the Scenes of an LLM

• Practical LLMs

  • LLM-based Applications Overview

  • Prompt engineering

  • LangeChain + LlamaIndex frameworks

  • Use Case: RAG

  • Agentic AI

  • Fine tuning (why, approaches, tools)

  • Workflow automation tools

  • Evaluation

Project

• In this section, participants will work on a final capstone project, with most of the progress carried out independently outside of class. Classroom sessions will be dedicated to mentoring, guidance, consultation, and peer idea exchange. During these sessions, we will also provide introductory workshops on NLP, Big Data (Spark), and working in a development environment (PyCharm).