Programs

Cohort-based courses in machine learning & data science

Every program is instructor-led, project-based, and taught in small cohorts so you get direct feedback — not a self-paced video library.

7 programs found

Beginner → Intermediate

Foundations of Machine Learning

A rigorous introduction to supervised and unsupervised learning — the same foundations our instructors use in production research.

Duration: 8 weeks, cohort-based
Format: Online, live sessions + recordings

Explore the curriculum

What you'll learn

Python & NumPy/Pandas for ML workflows
Linear & logistic regression, regularization
Decision trees, ensembles, and model evaluation
Unsupervised learning: clustering & dimensionality reduction
Capstone project on a real dataset

Prerequisites: Basic Python and high-school-level statistics.

Apply for this program

Intermediate

Applied Data Science Bootcamp

From messy raw data to decision-ready insight: data wrangling, SQL, visualization, and communicating results to stakeholders.

Duration: 10 weeks, cohort-based
Format: Online, live sessions + recordings

Explore the curriculum

What you'll learn

Data cleaning, wrangling & feature engineering
SQL for analytics
Exploratory data analysis & visualization
Experiment design and statistical inference
Portfolio project with a real-world dataset

Prerequisites: Completion of Foundations of Machine Learning, or equivalent experience.

Apply for this program

Intermediate → Advanced

Deep Learning & Neural Networks

Build and train neural networks from first principles, then work with modern architectures in PyTorch.

Duration: 8 weeks, cohort-based
Format: Online, live sessions + recordings

Explore the curriculum

What you'll learn

Backpropagation and optimization from scratch
Convolutional neural networks for vision
Recurrent networks and an introduction to transformers
Model calibration and uncertainty — not just accuracy
Final project: train and evaluate a deep model end-to-end

Prerequisites: Foundations of Machine Learning or equivalent, plus working Python.

Apply for this program

Advanced

Time-Series & Biosignal Machine Learning

A specialist track drawn directly from our parent lab's research on ECG, EDA and PPG signals — rare hands-on coverage of biosignal ML.

Duration: 6 weeks, cohort-based
Format: Online, live sessions + recordings

Explore the curriculum

What you'll learn

Signal preprocessing & noise handling for physiological data
Feature extraction for time-series and biosignals
Sequence models for classification & forecasting
Uncertainty quantification and calibration for health data
Case study drawn from real biosignal research pipelines

Prerequisites: Deep Learning & Neural Networks or equivalent experience.

Apply for this program

Beginner

Python for Data Science

A gentle, practical on-ramp for complete beginners who want to start working with data in Python.

Duration: 4 weeks, cohort-based
Format: Online, live sessions + recordings

Explore the curriculum

What you'll learn

Python syntax, control flow, and functions
Working with NumPy and Pandas
Reading, cleaning, and plotting real datasets
Introduction to Jupyter notebooks and reproducible workflows

Prerequisites: None — open to complete beginners.

Apply for this program

Advanced

Nepali NLP & Devanagari Text Processing

A niche, high-value track on natural language processing for Nepali and Devanagari script — an area our parent lab actively researches.

Duration: 6 weeks, cohort-based
Format: Online, live sessions + recordings

Explore the curriculum

What you'll learn

Challenges of low-resource language NLP
Tokenization and text normalization for Devanagari script
OCR fundamentals for Devanagari documents
Fine-tuning language models for Nepali text tasks
Project: build a small Nepali NLP tool end-to-end

Prerequisites: Deep Learning & Neural Networks or equivalent experience.

Apply for this program

Advanced

Sentiment Analysis & Emotion Recognition

Text-based sentiment analysis and multimodal emotion recognition — facial expression, speech, and physiological signals — grounded in our parent lab's biosignal research.

Duration: 6 weeks, cohort-based
Format: Online, live sessions + recordings

Explore the curriculum

What you'll learn

Classical and transformer-based sentiment classification
Aspect-based sentiment analysis
Facial expression recognition (computer vision)
Speech emotion recognition
Multimodal fusion for emotion recognition
Evaluation and bias considerations for emotion-labeled data

Prerequisites: Deep Learning & Neural Networks or equivalent experience.

Apply for this program