3-Month AI, ML & Data Science Internship Roadmap
This 12-week internship roadmap is designed to take a beginner to a confident project-builder in AI, Machine Learning and Data Science. It focuses on practical skills, weekly deliverables, and an end-to-end final project.
🎯 Internship Goals
- Understand core AI/ML & Data Science concepts
- Build 3–4 real projects and deploy one end-to-end app
- Learn data cleaning, EDA, model building, tuning and deployment
🗓️ Duration & Format
12 weeks (3 months). Each week contains theory, guided code notebooks, and a mini project. Weekly deliverables: GitHub notebook, short demo video (2 min), and a one-page report.
📘 Weekly Breakdown
Phase 1 — Data Science Foundations (Weeks 1–4)
Week 1: Python for Data Science
Libraries: NumPy, Pandas, Matplotlib. Mini project: Analyze IPL / Netflix dataset.
Week 2: Data Cleaning & Visualization
Handle missing values, encode categorical data. Mini project: COVID-19 dashboard.
Week 3: Statistics & Probability
Distributions, hypothesis testing. Mini project: Student marks analysis.
Week 4: Exploratory Data Analysis (EDA)
EDA reports & dashboards. Mini project: EDA on Zomato or Flipkart dataset.
Phase 2 — Machine Learning (Weeks 5–9)
Week 5: Intro to ML
Supervised vs Unsupervised, metrics. Mini project: House Price Prediction.
Week 6: Regression Models
Linear, Polynomial, Regularization. Mini project: Salary Prediction.
Week 7: Classification Models
Logistic Regression, Decision Trees, Random Forests. Mini: Spam Detection.
Week 8: Unsupervised Learning
K-Means, PCA. Mini: Customer Segmentation.
Week 9: Model Optimization
Cross-validation, GridSearchCV. Mini: Loan Approval Prediction (tuned).
Phase 3 — AI & Deep Learning (Weeks 10–12)
Week 10: Neural Networks
ANN basics with TensorFlow/Keras. Mini: MNIST digit recognition.
Week 11: Choose CV or NLP
Computer Vision — CNNs (Face Mask Detector) OR NLP — Sentiment Analysis.
Week 12: Deployment & Final Presentation
Deploy with Flask / Streamlit, prepare final report and demo video. Final project: End-to-end ML app.
🧩 Tools & Tech
| Category | Tools |
|---|---|
| Language | Python |
| ML Libraries | scikit-learn, TensorFlow, Keras |
| Data | Pandas, NumPy |
| Visualization | Matplotlib, Seaborn, Power BI |
| Deployment | Flask, Streamlit |
📦 Weekly Deliverables
- Jupyter / Colab notebook on GitHub
- One page report (Markdown / PDF)
- 2 minute demo video
- Weekly reflection (short)
🏁 Final Project Ideas
- Movie Recommender System (content + collaborative)
- Diabetes Prediction Web App (deploy with Streamlit)
- Resume Screener AI (NLP)
✅ Completion Certificate
To earn the certificate: submit all weekly deliverables, present a final project, and pass the final assessment quiz.
🔗 Resources & Starter Notebooks
Included starter notebooks: week1_python_basics.ipynb, week5_house_prices.ipynb, week10_mnist_keras.ipynb.
📢 Call to Action
Ready to run this as a Learning Sutras internship? Use the WhatsApp button above to contact me or click Apply for Internship.
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