Setting up a proper development environment is the first step toward machine learning proficiency. This guide covers installing Python, setting up a virtual environment, and configuring essential libraries for machine learning on a MacBook Air M4. Additionally, it includes instructions for running Jupyter Notebook and a simple linear regression experiment… Read More

AI is improving healthcare by diagnosing diseases and recommending treatments. Deep learning models can analyze medical images to detect anomalies. Example: Using CNNs for Medical Image Classification This builds a simple Convolutional Neural Network (CNN) for image classification. Read More

MLOps streamlines the deployment and monitoring of ML models in production. Tools like Docker, Kubernetes, and TensorFlow Serving are commonly used. Example: Serving a Model with FastAPI This creates a simple API to serve ML model predictions. Read More

Large Language Models (LLMs) like ChatGPT process and generate human-like text. They can assist with tasks like coding, summarization, and customer support. Example: Summarizing Text with an LLM This script summarizes a long text input using an AI model. Read More

Reinforcement Learning (RL) enables AI to improve through trial and error. AI learns by interacting with an environment and receiving rewards for good actions. Example: Training an RL Agent in OpenAI Gym This script runs a random agent in the “CartPole” environment. Read More

Computer vision powers applications from autonomous vehicles to medical diagnostics. Modern models like YOLO and Vision Transformers (ViTs) enhance image recognition. Example: Object Detection with OpenCV and YOLO This code initializes a YOLO object detection model using OpenCV. Read More

Bias in AI models can lead to unfair treatment in areas like hiring, law enforcement, and finance. It’s crucial to implement bias mitigation strategies when training AI models. Example: Checking Data Bias in a Dataset This helps to identify imbalances in datasets that could lead to biased AI decisions. Read More

Transformers, introduced by Google in 2017, changed the way AI processes language. Instead of traditional RNNs, they use self-attention mechanisms to analyze entire sequences efficiently. Example: Implementing a Transformer with Hugging Face This code generates text using a pre-trained GPT-2 transformer model. Read More