IT · Artificial Intelligence
Artificial Intelligence and Machine Learning Internship
Prepare data, train and evaluate machine-learning models, and explain results responsibly.
A foundations-to-capstone experience in applied machine learning, covering the complete workflow from problem framing and data quality to deployment considerations.
Program overview
Eligible branches: Computer Science and Engineering, Information Technology, Data-related engineering, Other branches with Python and quantitative interests
Target learner level: Learners comfortable with basic algebra who are beginning applied data science or machine learning.
Prerequisites: Basic algebra and comfort using a computer · Introductory Python is helpful; guided refreshers are included
Technologies and tools
Python · NumPy · pandas · Jupyter notebooks · scikit-learn · statistical evaluation · model interpretability
Practical assignments
- Clean and explore a dataset while recording quality decisions
- Train and compare baseline and tree-based models using appropriate metrics
- Review errors, fairness considerations and model limitations
Capstone project
Develop and evaluate a domain-specific machine-learning model with an evidence-based explanation of its limitations.
Expected deliverables
- Reproducible notebook or project repository
- Data preparation and experiment notes
- Model comparison with evaluation metrics
- Responsible-use and limitation report
- Technical presentation
Learning outcomes
- Prepare and explore a dataset using Python
- Compare suitable supervised or unsupervised models
- Evaluate model quality, limitations and responsible-use risks
Week-by-week curriculum
The program follows a 13-week core pathway. Weeks 14–15 are optional advanced work when the project scope calls for it.
Weeks 1–2
Orientation, fundamentals and setup
- Orientation, problem framing, Python notebooks and environment setup
- Python, NumPy, pandas, data types, statistics refresher and responsible AI basics
Weeks 3–5
Core concepts and guided learning
- Data cleaning, exploratory analysis, visualization and leakage prevention
- Train/validation/test splits, baselines and regression/classification metrics
- Linear models, decision trees and feature engineering with scikit-learn
Weeks 6–8
Intermediate concepts and practical exercises
- Ensembles, clustering, dimensionality reduction and model selection
- Cross-validation, imbalance handling, calibration and error analysis
- Reproducible experiments, pipelines and interpretable-model exercises
Weeks 9–11
Advanced topics, specialist tools and project development
- Neural-network foundations, embeddings and introductory deep learning
- Explainability, fairness checks, privacy, drift and deployment patterns
- Plan and implement a domain-specific ML capstone with measurable criteria
Weeks 12–13
Capstone completion, testing, documentation and evaluation
- Train and compare models; record data decisions and experiment results
- Test robustness, document limitations and prepare a reproducible handoff
- Present findings, evaluation evidence and responsible-use assessment
Weeks 14–15 · Optional
Advanced extension and final presentation
- Explore a specialised model family or a small model-serving workflow
- Improve monitoring/interpretability and present a refined technical demo
Frequently asked questions
Is prior experience required?
The prerequisites listed on this page describe helpful preparation. The opening weeks include a guided refresher; learners should be ready to practise consistently and complete project work.
Do I need advanced mathematics?
Basic algebra and introductory statistics are helpful. The internship introduces the concepts needed for its guided model exercises.