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.

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

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.

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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

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.

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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

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.

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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

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.

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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

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.

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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

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.

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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

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.

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