📊 TECHNICAL BOOTCAMP
📅 16–22 November 2026 (Bootcamp)
Data Science & Machine Learning
Go from raw datasets to trained and deployed machine learning models in 10 intensive days. A rigorous, applied technical bootcamp for future data scientists and ML engineers.
Industry-Standard Data Science & ML Stack
🐍 Python
🔢 NumPy
🐼 Pandas
📈 Matplotlib
📊 Seaborn
🧠 Scikit-Learn
⚡ Streamlit
🐙 GitHub
Technical Roadmap
10 Days to Production ML Models
No toy tutorials. Every single day bridges foundational math and statistics with production Python code and interactive model deployment.
Day 1 — Python for Data Science
DAY 01- Python data structures, vectorization, list comprehensions, and functional programming.
- NumPy multidimensional arrays, broadcasting operations, indexing, and linear algebra routines.
- Setting up clean VS Code, Jupyter, and GitHub workflows.
Day 2 — Data Cleaning & Preprocessing
DAY 02- Pandas DataFrames, Series manipulation, multi-indexing, and missing value imputation strategies.
- Detecting anomalies, parsing inconsistent dates/strings, and handling duplicate noisy records.
- Data transformation pipelines and feature encoding (One-Hot, Ordinal, Label).
Day 3 — Exploratory Data Analysis & Visualization
DAY 03- Visual storytelling with Matplotlib and statistical plotting with Seaborn.
- Distribution curves, correlation heatmaps, box plots, and multivariate scatter relationships.
- Extracting high-value commercial patterns and domain hypotheses from raw tabular data.
Day 4 — Statistics for Machine Learning
DAY 04- Descriptive statistics: variance, standard deviation, covariance, and skewness.
- Probability distributions (Normal, Binomial) and the Central Limit Theorem.
- Hypothesis testing (p-values, z-scores, t-tests) and statistical validation of data patterns.
Day 5 — Machine Learning Fundamentals
DAY 05- Supervised vs. Unsupervised paradigms and the ML engineering lifecycle.
- Train-test splits, cross-validation, data leakage prevention, and baseline benchmarking.
- The Bias-Variance tradeoff, overfitting diagnostics, and regularization concepts.
Day 6 — Regression Modeling
DAY 06- Simple and Multiple Linear Regression mathematical intuition and Scikit-learn implementation.
- Cost functions, Gradient Descent mechanics, and residual error analysis.
- Evaluation metrics: MSE, RMSE, MAE, R² score, and Ridge/Lasso regularization.
Day 7 — Classification Modeling
DAY 07- Logistic Regression, Decision Trees, and Random Forest ensemble classifiers.
- Handling class imbalance: SMOTE, downsampling, and class weighting.
- Confusion matrices, Precision, Recall, F1-score, and ROC-AUC curves.
Day 8 — Real-World ML Project
DAY 08- End-to-end implementation of an industry dataset (financial fraud, customer churn, or housing valuation).
- Building an automated Scikit-learn Pipeline for transformation, feature scaling, and inference.
- Hyperparameter tuning with GridSearchCV and model serialization via Pickle/Joblib.
Day 9 — Streamlit & Deployment
DAY 09- Building an interactive web UI using Streamlit to accept live user parameters.
- Connecting the saved machine learning pipeline to generate instant predictions.
- Deploying the interactive application live on Streamlit Community Cloud or Vercel.
Day 10 — Portfolio & Final Project
DAY 10- Packaging the final ML codebase with structured requirements.txt, modular functions, and README.
- Creating a high-impact GitHub portfolio repository and LinkedIn project walkthrough.
- Reviewing next steps in Deep Learning, Neural Networks, and AI engineering career paths.
Technical Capstone Deliverable
Build 2+ ML Projects & Live Deployed App
Leave with a verified GitHub portfolio demonstrating real data pipeline mastery, model evaluation rigor, and a publicly accessible web application that recruiters and clients can test live.
PKR 12,000
Early Bird: PKR 10,000 | Premium: PKR 15,000
Cohort Admissions
Register for Data Science & Machine Learning
Dates: Mid-November 2026 (10-Day Bootcamp). Limited cohort capacity for hands-on code reviews.
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