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Diploma roadmap

AI Diploma

12 weeks · 5 stages

What you will learn

Master the foundations of Artificial Intelligence

Build an elegant portfolio of projects throughout the diploma

Think and develop AI LLMs like a real professional developer

Skills covered

  • Python Programming
  • Data Structures
  • Core Machine Learning
  • Supervised & Unsupervised Learning
  • Neural Networks
  • Deep Learning
  • Computer Vision
  • Natural Language Processing
  • Big Data & LLM training

Achieve expertise and proficiency as a professional AI developer.

01

Core Foundations

  • Python
    • NumPy
    • Pandas
    • Matplotlib
  • Basic algorithms
  • Data structures
  • Object-oriented programming (OOP)
02

Machine Learning Fundamentals

  • Supervised vs Unsupervised Learning
  • Regression, Classification, Clustering
  • Model evaluation
    • Accuracy
    • Precision / Recall
    • F1-score
    • ROC
  • Scikit-learn, Jupyter Notebooks
  • Project: train a spam email classifier
  • Project: cluster customers based on shopping behaviour
03

Deep Learning (Neural Networks)

  • Neural networks
    • Perceptron
    • Backpropagation
  • CNNs → images
  • RNNs, LSTMs → sequences
  • Transformers & Attention → modern NLP
  • TensorFlow basics
  • PyTorch basics
  • Project: build a digit recognizer (MNIST)
  • Project: image classifier for cats vs dogs
  • Project: sentiment analysis on tweets
04

AI Specializations

  • Computer Vision
    • Object detection, segmentation, image generation
    • Tools: OpenCV, YOLO, Detectron
    • Project: face mask detector, self-driving car lane detection
  • Natural Language Processing
    • Text classification, chatbots, summarization, translation
    • Tools: Hugging Face Transformers
    • Project: build a GPT-powered chatbot
  • AI for Data Science
    • Data cleaning, predictive analytics, recommender systems
    • Tools: Pandas, Scikit-learn, PyTorch / TensorFlow
05

Training Big Data & LLMs

  • Big Data fundamentals
    • Data pipelines
    • Distributed storage
    • Spark
    • Hadoop
  • Preprocessing & managing large-scale datasets
  • Training strategies for LLMs
  • Fine-tuning & adapting pretrained LLMs
  • Project: build a pipeline to preprocess a big dataset and fine-tune a medium-sized LLM for text classification

Graduation Party

The end of the road: demo your project, collect your certificate, and leave with a portfolio — alongside your cohort and mentors.

Reserve your seat