Tag: AI/ML Techniques

In this article, you’ll learn to: • Turn unstructured, raw image data into structured, informative features.

If you’re reading this, it’s likely that you are already aware that the performance of a machine learning model is not just a function of the chosen algorithm.

These days, it is not uncommon to come across datasets that are too large to fit into random access memory (RAM), especially when working on advanced data analysis projects at scale, managing streaming data generated at high velocity, or building large machine learning models.

You’ve built a machine learning model that performs perfectly on training data but fails on new examples.

In classification models , failure occurs when the model assigns the wrong class to a new data observation; that is, when its classification accuracy is not high enough over a certain number of predictions.

NumPy is one of the most popular Python libraries for working with numbers and data.

Visualizing model performance is an essential piece of the machine learning workflow puzzle.

In this article, you will learn: • Build a decision tree classifier for spam email detection that analyzes text data.

One of the most widespread machine learning techniques is XGBoost (Extreme Gradient Boosting).

The foundational instructions that govern the operation and user/model interaction of language models (also known as system prompts) are able to offer insights into how we — as users, AI practitioners, and developers — can optimize our interactions, approach future model advancements, and develop useful language model-driven applications.