Applied Machine Learning & NLP
The critical foundation of all AI. Before jumping to LLMs, you must understand data processing, feature engineering, and training algorithms that constitute 90% of enterprise AI use cases today.
Mastered Technologies
You Will Build
Build an end-to-end automated ML pipeline that ingests raw customer data, trains an XGBoost model, evaluates metrics, and deploys via FastAPI.
The 5-Week Syllabus
An intense, week-by-week breakdown designed to push your limits.
Data Wrangling & Feature Engineering
Data is the new oil, but it needs refining.
Core Topics
- Pandas & NumPy Deep Dive
- Handling Missing Data
- Dimensionality Reduction
Hands-on Lab
Clean and engineer a messy 10GB dataset for training.
Supervised & Unsupervised Learning
Classification, regression, and clustering algorithms.
Core Topics
- Random Forests
- Gradient Boosting (XGBoost)
- K-Means
Hands-on Lab
Build a high-accuracy churn prediction model for a SaaS dataset.
Foundations of NLP
Teaching machines to read before the Transformer era.
Core Topics
- Tokenization & Stemming
- TF-IDF
- Word Embeddings (Word2Vec)
Hands-on Lab
Create a fast intent-classification system for customer support tickets.
Model Evaluation & Bias Mitigation
Knowing when your model is lying to you.
Core Topics
- Cross-Validation
- Confusion Matrices
- Algorithmic Fairness
Hands-on Lab
Audit a hiring algorithm for gender and demographic bias.
API Deployment & Model Serving
Getting your model out of the Jupyter notebook.
Core Topics
- FastAPI routing
- Pickle/ONNX serialization
- Dockerizing ML apps
Hands-on Lab
Deploy your churn model as a scalable REST API.
Expert Facilitator
Sarah has led ML teams in quantitative finance and health tech, specializing in predictive modeling and structured data.
Student Perks
- Cloud GPU Grants
- Kaggle Grandmaster mentorship session
- Enterprise Dataset Access