7 Feature Engineering Tricks for Text Data
An increasing number of AI and machine learning-based systems feed on text data — language models are a notable example today.
10 Python One-Liners for Calling LLMs from Your Code
You don’t always need a heavy wrapper, a big client class, or dozens of lines of boilerplate to call a large language model.
7 Pandas Tricks to Handle Large Datasets
Large dataset handling in Python is not exempt from challenges like memory constraints and slow processing workflows.
The Machine Learning Practitioner’s Guide to Agentic AI Systems
Agentic artificial intelligence (AI) represents the most significant shift in machine learning since deep learning transformed the field.
Building Transformer Models from Scratch with PyTorch (10-day Mini-Course)
Before we begin, let’s make sure you’re in the right place.
Build an Inference Cache to Save Costs in High-Traffic LLM Apps
Large language models (LLMs) are widely used in applications like chatbots, customer support, code assistants, and more.
7 NumPy Tricks to Vectorize Your Code
You’ve written Python that processes data in a loop.
Is ChatGPT-5 Able to Provide Proofs for Advanced Mathematics?
One of the claims made by OpenAI regarding its latest model, GPT-5 , is a breakthrough in reasoning for math and logic, with the ability to “think” more deeply when a prompt benefits from careful analysis.
A Decision Matrix for Time Series Forecasting Models
Time series data have the added complexity of temporal dependencies, seasonality, and possible non-stationarity.
Algorithm Showdown: Logistic Regression vs. Random Forest vs. XGBoost on Imbalanced Data
Imbalanced datasets are a common challenge in machine learning.