PG Certification in ML & AI

The mathematical and programming base every ML model rests on — not generic Python 101, built specifically for the workflows you'll use later.

• Python for ML: NumPy, Pandas, and scripting for data workflows
• Probability, linear algebra, and calculus — the parts ML actually uses, like gradient descent and loss functions
• SQL for pulling and shaping training data

Before you can model data, you have to actually understand what's in it.

• Exploratory data analysis and data visualization
• Inferential statistics and hypothesis testing
• Data cleaning and preprocessing pipelines

Where you stop reading about machine learning and start actually building it.

• Linear and logistic regression, KNN, decision trees, and ensembles
• Clustering and unsupervised learning
• Model evaluation, regularization, and hyperparameter tuning

Neural networks, and the language and vision systems built on top of them.

• Neural network fundamentals, CNNs, and RNNs
• NLP: text processing, sentiment analysis, and transformer-based models like BERT
• Computer vision fundamentals

The unglamorous, high-value work that most ML engineering jobs actually are.

• Recommendation and ranking systems
• Fraud and anomaly detection on real-world, imbalanced data
• Model deployment basics and monitoring

Optional, and worth taking — this is the fastest-growing corner of the field, once the fundamentals underneath it are solid.

• Prompt engineering and working with LLM APIs
• RAG (Retrieval-Augmented Generation): embeddings, vector databases, grounded generation
• Building single- and multi-agent AI systems