We are excited to announce the launch of our AI & Machine Learning Bootcamp. This intensive, hands-on program is designed to help technical professionals break into the world of applied AI.
The first course in this series is titled Applied AI: From Deep Learning Fundamentals to Agentic Systems, an 8-week journey into building real-world AI capabilities.
Machine Learning Course Outline — “From Intuition to Deployment”
12–14 weeks (can be adapted to 8 or 16 weeks)
Prerequisites:
- Basic Python
- Linear algebra (vectors, matrices)
- Probability & statistics fundamentals
- Optional: basic data analysis with pandas/numpy
Module 1: Foundations of Machine Learning
Concepts
- What is ML? Types (Supervised, Unsupervised, Reinforcement)
- Real-world ML pipeline overview: data → model → evaluation → deployment
- Bias-variance tradeoff
- Overfitting & underfitting
Math
- Mean, variance, covariance
- Correlation vs causation
- Linear algebra recap (dot product, matrix multiplication)
Hands-on
- Predict housing prices using Linear Regression (sklearn)
- Visualizing model fit with Matplotlib/Seaborn
Mini-project: “Predicting Airbnb Prices”
Module 2: Supervised Learning — Regression & Classification
Concepts
- Linear Regression (simple, multiple)
- Polynomial Regression & regularization (Ridge, Lasso)
- Logistic Regression
- Decision Trees, Random Forests, Gradient Boosting (XGBoost/LightGBM)
Math
- Cost function (MSE, Cross-Entropy)
- Gradient Descent intuition
Hands-on
- Classify tumors using the Breast Cancer dataset
- Compare performance metrics (accuracy, precision, recall, F1, ROC)
Mini-project: “Predicting Heart Disease Risk
Module 3: Unsupervised Learning
Concepts
- Clustering: K-Means, Hierarchical, DBSCAN
- Dimensionality Reduction: PCA, t-SNE, UMAP
- Anomaly detection & feature engineering
Math
- Eigenvectors, Eigenvalues, variance explained
- Distance metrics (Euclidean, Manhattan, Cosine)
Hands-on
- Customer segmentation with K-Means
- Visualizing clusters in 2D PCA space
Mini-project: “Customer Segmentation for an E-commerce Platform”
Module 4: Neural Networks & Deep Learning
Concepts
- Perceptron and MLPs
- Backpropagation
- Activation functions
- Intro to CNNs (image data)
- Intro to RNNs (sequence data)
Math
- Chain rule for backpropagation
- Loss gradients
Hands-on
- Build neural nets from scratch (NumPy)
- Train MNIST classifier using PyTorch or TensorFlow
Mini-project: “Digit Recognition App”
Module 5: NLP & Modern ML Pipelines
Concepts
- Text preprocessing (tokenization, stemming, TF-IDF)
- Word embeddings (Word2Vec, GloVe)
- Transformers and BERT intro
- Prompt engineering & LLM APIs (optional advanced)
Hands-on
- Sentiment analysis on tweets (TF-IDF + Logistic Regression)
- Compare performance with BERT fine-tuning (Hugging Face)
Mini-project: “Movie Review Sentiment Classifier”
Module 6: Model Evaluation, Explainability & Ethics
Concepts
- Cross-validation, confusion matrix, ROC-AUC
- Model interpretability: SHAP, LIME
- Data bias, fairness, privacy
- ML model reproducibility
Hands-on
- Use SHAP to explain a credit scoring model
- Detect data leakage
Mini-project: “Interpretable Credit Risk Model”
Module 7: ML in Production
Concepts
- Model deployment (REST APIs, Flask/FastAPI)
- CI/CD in ML (MLOps)
- Monitoring drift and retraining
- Using MLflow for experiment tracking
Hands-on
- Deploy model to Render, Hugging Face, or AWS Lambda
- Integrate with a React or Streamlit frontend
Capstone Project:
“End-to-End ML App” — student’s choice (e.g., price predictor, recommender system, sentimentanalyzer)
Module 8: AI Ethics and Course Wrap-Up
This final week addresses ethical considerations and consolidates learning.
Session 22: AI Ethics—bias, fairness, transparency, responsible practices, case studies.
Hands-On:Â Discuss ethical scenarios and mitigation strategies.
Session 23: Course Wrap-Up—recap key concepts, future trends (e.g., multimodal models), Q&A.
Hands-On:Â None, focus on consolidation.
Session 24: Open Discussion—applying AI in industry, sharing insights, optional guest speaker.
Hands-On:Â None, group discussion or expert talk.
This ensures participants reflect on AI’s societal impact and connect learning to their work.
Hands-On and Theoretical Balance.
Each session is split into approximately 45 minutes of theory and 45 minutes of hands-on practice, ensuring a balanced approach. Hands-on activities include coding exercises like building neural networks, fine-tuning models, and creating agents, with materials provided (slides, notebooks, datasets). This structure supports the course’s theoretical + hands-on nature, catering to the participants’ need for practical skills.
Tools and Prerequisites.
The course uses Python, PyTorch for deep learning, and Hugging Face for NLP and generative tasks, with additional libraries for Agentic AI (e.g., LangChain for agent frameworks). Prerequisites include familiarity with Python and basic tech concepts, but no prior AI experience, aligning with the target audience’s profile.
Additional Considerations
- Projects: Hands-on sessions build toward a portfolio of mini-projects, such as a fine-tuned NLP model, a CV classifier, generated images, andan LLM-based agent, enhancing practical application.
- Support: Instructors are available for questions, with optional pre reading for deeper dives, ensuring accessibility.
- Ethics and Future Trends: Week 8’s focus on ethics and future trends, like multimodal models, prepares participants for responsible AI use and industry evolution.
This detailed design ensures a comprehensive, engaging, and practical AI learning experience, ready for implementation in professional settings.