RAG Systems Course
A Crash Course on Building RAG Systems – Part 8 (With Implementation)
A deep dive into ColBERT and ColBERTv2 for improving RAG systems (with implementation).
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RAG Systems Course
A Crash Course on Building RAG Systems – Part 7 (With Implementation)
A deep dive into Graph RAG and how it improves traditional RAG systems (with implementation).
RAG Systems Course
A Crash Course on Building RAG Systems – Part 6 (With Implementation)
A deep dive into building multimodal RAG systems on real-world data (with implementation).
RAG Systems Course
A Crash Course on Building RAG Systems – Part 5 (With Implementation)
A deep dive into key components of multimodal systems—CLIP embeddings, multimodal prompting, and tool calling.
RAG Systems Course
A Crash Course on Building RAG Systems – Part 4 (With Implementation)
A deep dive into handling multiple data types in RAG systems (with implementations).
RAG Systems Course
A Crash Course on Building RAG Systems – Part 3 (With Implementation)
A deep dive into making RAG systems faster (with implementations).
Causal inference
Causality (Part 2)
A guide to building robust decision-making systems in businesses with causal inference.
RAG Systems Course
A Crash Course on Building RAG Systems – Part 2 (With Implementation)
A deep dive into evaluating RAG systems (with implementations).
RAG Systems Course
Foundations of RAG systems
A practical and beginner-friendly crash course on building RAG apps (with implementations).
Classical ML and Deep Learning
Causality (Part 1)
A guide to building robust decision-making systems in businesses with causal inference.
LLM and Fine-tuning
AugSBERT: Bi-encoders + Cross-encoders for Sentence Pair Similarity Scoring – Part 2
A deep dive into extensions of cross-encoders and bi-encoders for sentence pair similarity.
LLM and Fine-tuning
Bi-encoders and Cross-encoders for Sentence Pair Similarity Scoring (Part 1)
A deep dive into why BERT isn't effective for sentence similarity and advancements that shaped this task forever.
Classical ML and Deep Learning
Model Interpretability (Part 3)
A deep dive into interpretability methods, why they matter, along with their intuition, considerations, how to avoid being misled, and code.
Engineering Best Practices
5 Must-Know Ways to Test ML Models in Production (Implementation Included)
A beginner-friendly guide to model testing.
Classical ML and Deep Learning
Model Interpretability (Part 2)
A deep dive into interpretability methods, why they matter, along with their intuition, considerations, how to avoid being misled, and code.
Classical ML and Deep Learning
Model Interpretability (Part 1)
A deep dive into PDPs and ICE plots, along with their intuition, considerations, how to avoid being misled, and code.
Engineering Best Practices
Spark DataFrames and Big Data ML using PySpark on Databricks
A completely hands-on and beginner-friendly deep dive on PySpark using Databricks.
Graph ML Course
Graph Neural Networks Part 3 (Implementation Included)
A practical and beginner-friendly guide to building neural networks on graph data.
Graph ML Course
Graph Neural Networks Part 2 (Implementation Included)
A practical and beginner-friendly guide to building neural networks on graph data.
Graph ML Course
Graph Neural Networks Part 1 (Implementation Included)
A practical and beginner-friendly guide to building neural networks on graph data.
Classical ML and Deep Learning
Model Calibration (Part 2)
How to make ML models reflect true probabilities in their predictions?
Classical ML and Deep Learning
Federated Learning: A Critical Step Towards Privacy-Preserving ML
Learn real-world ML model development with a primary focus on data privacy – A practical guide.
Classical ML and Deep Learning
Model Calibration (Part 1)
How to make ML models reflect true probabilities in their predictions?
Engineering Best Practices
A Practical Guide to Scaling ML Model Training
GPUs - GPU Clusters - Distributed Training.
Classical ML and Deep Learning
Conformal Predictions: Build Confidence in Your ML Model's Predictions
A critical step towards building and using ML models reliably.
Engineering Best Practices
Quantization: Optimize ML Models to Run Them on Tiny Hardware
A must-know skill for ML engineers to reduce model footprint and inference time.
Engineering Best Practices
How to Structure Your Code for Machine Learning Development
A highly overlooked yet critical skill for data scientists.
Engineering Best Practices
Develop an Elegant Testing Framework For Data Science Projects Using Pytest
A Comprehensive Guide to Pytest for data scientists.
Engineering Best Practices
A Beginner-friendly Guide to Multi-GPU Model Training
Models are becoming bigger and bigger. Learn how to scale models using distributed training.
Engineering Best Practices
Object-Oriented Programming with Python for Data Scientists
A beginner to advanced guide for Python OOP.
