AI — Scratch to Industry
From Your First Line of Code to Building Real AI Models
A hands-on AI bootcamp where you learn by building. Across 6 focused days, you will construct three fully working AI models spanning Natural Language Processing, Deep Learning, and Applied AI — all portfolio-ready and industry-relevant.
Three Real AI Models. Six Days.
Every day is structured around a concrete output — you won't just study theory, you will write, train, evaluate, and ship working models.
NLP Model
Sentiment Analysis or Spam Detection — a complete NLP pipeline from raw text to trained classifier.
Deep Learning Model
Image Classification or Neural Network Prediction — train a neural network from scratch using real datasets.
Applied AI Project
An AI Assistant or Prediction Application — a fully integrated, deployable AI application.
6 Days to Industry-Ready AI Skills
Structured from zero-to-production: each day builds directly on the last, culminating in a deployed, presentable AI portfolio.
Day 1 — AI Foundations & Setup
DAY 01- AI landscape overview: ML, Deep Learning, NLP, and Applied AI — how they differ and connect.
- Python for AI: NumPy, Pandas, Matplotlib — essential data manipulation and visualization foundations.
- Datasets: sourcing, understanding, and loading real AI datasets (CSV, JSON, image folders).
- Development environment: Jupyter Notebook, VS Code, Google Colab, GitHub setup and workflow.
Day 2 — Build an NLP Model
DAY 02- Text preprocessing: tokenization, stopword removal, stemming, and lemmatization.
- Feature extraction: TF-IDF, Bag of Words, and word embeddings introduction.
- Train a Sentiment Analysis or Spam Detection classifier using Scikit-Learn / NLTK.
- Deliverable: Working NLP model with evaluation metrics (accuracy, F1-score, confusion matrix).
Day 3 — Build a Deep Learning Model
DAY 03- Neural network fundamentals: layers, activations, forward/back-propagation — intuitively explained.
- Build and train with TensorFlow/Keras: Image Classification or Neural Network Prediction pipeline.
- Training loop: epochs, batch size, loss functions, and optimizer selection.
- Deliverable: Trained Deep Learning model with accuracy tracking and visualization plots.
Day 4 — Build an Applied AI Project
DAY 04- Integrating trained models into a functional AI application interface.
- Build an AI Assistant (question-answering, text generation) or a Prediction Application.
- Basic API integration and using pre-trained models effectively.
- Deliverable: Interactive AI application prototype running locally.
Day 5 — Evaluation, Debugging & Improvement
DAY 05- Model evaluation deep-dive: precision, recall, F1, ROC-AUC, and confusion matrix interpretation.
- Debugging underfitting and overfitting: regularization, dropout, and data augmentation techniques.
- Hyperparameter tuning strategies and cross-validation for reliable performance estimates.
- Testing edge cases and improving model robustness across diverse input scenarios.
Day 6 — Showcase, Deploy & Portfolio
DAY 06- Build a polished application interface using Streamlit or Gradio for your best model.
- Deployment basics: hosting your model demo on Streamlit Cloud or Hugging Face Spaces.
- AI Project Showcase: present all three models to the cohort with technical walk-through.
- Portfolio packaging: GitHub README, LinkedIn project post, and next-steps roadmap.
Leave with Three Working AI Models & a Portfolio
Graduate with a GitHub repository containing three trained, evaluated, and documented AI models — a deployable application, and demonstrated skills across NLP, Deep Learning, and Applied AI that you can show to employers and clients.
Enroll: AI — Scratch to Industry
6-Day hands-on AI bootcamp. Batch 1: 2–8 Nov · Batch 2: 9–15 Nov 2026 — secure your seat now.