PyCaret makes time series forecasting easy, automating predictions for sales, stocks, or ETAs in minutes. In this post, I will show simple code for sequential data—no complex ARIMA or stationarity tests needed. Time series forecasting uses past patterns to predict future values. It captures trends (steady rise/fall), seasonality (weekly/monthly repeats),…
PyCaret is a low-code Python library that automates the full ML pipeline. This guide uses the classic Iris dataset for classification, showing how to preprocess, compare 15+ models, tune the best one, and deploy—all in under 10 lines of code. Start by installing: pip install pycaret[full] PyCaret’s default pip install pycaret is…
Imagine wanting to build a smart prediction system for your business, but lacking months to learn complex coding and math. AutoML solves this by acting as autopilot for machine learning—it automates the entire process from raw data to a working model, allowing beginners to create powerful AI in hours instead…
Waiting for a ride or food delivery can be frustrating when the app suddenly changes the estimated arrival time from 20 minutes to 45 minutes. Behind the scenes, AI systems use large models and data from traffic, weather, and past trips to make these predictions. However, unexpected events and imperfect…
This post explores how LLM-driven systems transform simple travel predictions into something intelligent, accurate, and responsive. Classic ETA systems rely on basic formulas: distance, average speed, maybe a historical table showing usual delays. These approaches are serviceable in predictable conditions but often fall short. Unexpected traffic jams, road closures, unusual…
In today’s world, knowing exactly when a delivery or a bus will arrive is very important for both companies and customers. This is where ETA, or Estimated Time of Arrival, comes in. Traditional methods for predicting ETAs are often simple and don’t always account for real-world factors such as traffic…
Prompt injection attacks are a growing problem in AI tools like chatbots and language models. They happen when someone adds or “injects” extra instructions or harmful content into a prompt to manipulate the AI. Learning how to protect AI systems from these attacks is important for anyone who builds or…
A vector database is a specialized system for storing and searching high-dimensional data represented as vectors. In simple terms: It acts as a storage space for embeddings (numeric representations), which might come from texts, images, or audio. The main job of a vector database is to quickly find which stored vectors are most similar…
Modern natural language processing allows developers to choose between small fine-tuned language models and large general-purpose LLMs like GPT-4 or LLaMA. Both solutions have their strengths and trade-offs. Small Fine-Tuned Models Small fine-tuned models, sometimes called SLMs (Small Language Models), have fewer parameters—from several million up to a few billion. They are first trained…
Retrieval-Augmented Generation (RAG) chatbots use large language models (LLMs) plus a search system that pulls information from external sources to answer questions more accurately and reliably. While powerful, RAG chatbots can hit snags—from missing answers to confusing responses. Here’s a beginner-friendly, step-by-step guide for debugging these chatbots to help make…