Data Science & AI
Learn machine learning, data analysis, and AI fundamentals. Work with Python, TensorFlow, and real datasets.
Track Curriculum
15 Courses in This Track
Python Programming Fundamentals
Start your data science journey with Python. Master variables, data structures, functions, OOP, and file handling — everything you need before diving into data analysis.
Data Analysis with Pandas & NumPy
Master the two core Python libraries for data analysis. Load, clean, transform, aggregate, and explore real-world datasets with Pandas DataFrames and NumPy arrays.
Data Visualization with Matplotlib & Seaborn
Turn raw data into compelling, publication-quality charts. Master Matplotlib for fine-grained control and Seaborn for beautiful statistical plots — then build interactive dashboards with Plotly.
SQL for Data Scientists
Query any database like a pro. Learn SQL specifically for data analysis — from basic SELECTs to window functions, CTEs, and connecting SQL directly to your Python data pipeline.
Statistics & Probability for Machine Learning
Build the mathematical intuition behind ML algorithms. Master descriptive statistics, probability distributions, hypothesis testing, and Bayesian thinking — with Python throughout.
Machine Learning Fundamentals
Your complete introduction to machine learning. Understand the ML workflow, build and evaluate models with scikit-learn, and master the techniques behind every algorithm category.
Supervised Learning: Classification & Regression
Go deep on supervised learning. Master linear & logistic regression, decision trees, SVMs, neural networks, and advanced ensembles — with real datasets and production-grade code.
Unsupervised Learning & Clustering
Discover hidden patterns in unlabelled data. Master K-Means, DBSCAN, hierarchical clustering, dimensionality reduction with PCA & t-SNE, and anomaly detection.
Deep Learning Fundamentals
Unlock the power of neural networks. Build, train, and evaluate deep learning models with PyTorch — covering forward/backprop, CNNs, RNNs, and regularisation techniques.
Neural Networks with TensorFlow & Keras
Build production-grade neural networks with TensorFlow 2 and the Keras API. Cover the full pipeline from data ingestion to model serving with TensorFlow Serving and TF Lite.
Natural Language Processing (NLP)
Teach machines to understand human language. Master text preprocessing, classical NLP, word embeddings, transformers, and fine-tune BERT for real-world NLP tasks.
Computer Vision with OpenCV & PyTorch
Build systems that see. Master image processing with OpenCV, train deep CNNs with PyTorch, implement object detection with YOLO, and deploy vision models to production.
MLOps & Model Deployment
Bridge the gap between data science and production engineering. Master experiment tracking, model versioning, CI/CD for ML, monitoring, and end-to-end ML pipelines.
AI Ethics & Responsible AI
Build AI you can be proud of. Understand bias, fairness, transparency, privacy, and accountability — and learn the frameworks, tools, and regulations shaping responsible AI development.
Data Science Capstone Project
Apply everything you've learned in one complete, portfolio-ready project. Collect, clean, analyse, model, and deploy a machine learning solution to a real business problem.