12 Weeks | 180 Hours Intensity Timeline

Data Science & Machine Learning

Master the complete Data Science lifecycle—from collecting and analyzing raw data to building, deploying, and monitoring production-ready Machine Learning models. Students will work on real-world datasets, build AI-powered applications, create REST APIs, deploy ML models to the cloud, and develop professional dashboards to showcase their work.

Total Duration

12 Weeks | 180 Hours

Category

Data Science & Artificial Intelligence

Credential

Certified Data Science & Machine Learning Specialist

Modular Protocol Breakdown
MODULE 01 35 Hours Intensity

Data Foundations with Python & Statistics

Master Python programming for Data Science while building a strong foundation in statistics, data preprocessing, and exploratory data analysis. Learn to clean, transform, and visualize real-world datasets for machine learning applications.

Python Pandas NumPy Statistics EDA Data Cleaning Feature Engineering Matplotlib Seaborn
MODULE 02 45 Hours Intensity

Supervised Machine Learning

Develop predictive machine learning models using industry-standard algorithms. Learn regression, classification, model evaluation, feature selection, cross-validation, and hyperparameter tuning for production-quality solutions.

Scikit-Learn Regression Classification Decision Trees Random Forest XGBoost SVM Grid Search
MODULE 03 35 Hours Intensity

Unsupervised Learning & Feature Engineering

Discover hidden patterns in data using clustering and dimensionality reduction techniques. Learn feature engineering strategies to improve model performance and create scalable machine learning pipelines.

K-Means DBSCAN PCA Feature Engineering Clustering Dimensionality Reduction Pipeline
MODULE 04 35 Hours Intensity

Deep Learning & Explainable AI

Build intelligent neural network models using TensorFlow and PyTorch. Understand model interpretability with SHAP and LIME while exploring computer vision and natural language processing fundamentals.

TensorFlow PyTorch Neural Networks CNN Deep Learning SHAP LIME Explainable AI
MODULE 05 35 Hours Intensity

Model Deployment, APIs & Full-Stack ML Project

Convert machine learning models into real-world applications by developing REST APIs, interactive dashboards, and cloud-deployed AI systems. Learn deployment best practices and model packaging for production environments.

FastAPI Flask REST API Docker Streamlit GitHub MLflow Deployment