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65 hands-on Python tutorials on machine learning, data science, generative AI, RAG, and AI agents. New here? Follow a learning path, jump to a topic, or browse everything below.
Explore 3,000+ real-world AI use cases, save favorites, set company and industry alerts, and build your own impact-effort matrix for free.
Discover how AIUseCaseHub.com uses agentic web scraping with Azure AI Foundry Agent Service, multi-agent orchestration and tool calling.
It’s been a while since my last post in February 2024. A lot has happened since then—not just in the world of Large Language Models (LLM), but also in my…
This article explores the limitations of current LLMs and highlights six key areas where AGI is expected to excel beyond today's AI models.
Learn how to build a conversational voice bot in Python with the latest models from Azure OpenAI and Azure AI Speech Services.
Explore the future of database interaction with our comprehensive guide on Text-to-SQL technology using Large Language Models (LLMs).
Explore the new era of digital interaction with LLM-powered virtual AI assistants. This article offers rare insights into the architecture
Explore prompt flows for AI copilots and how LLMs like GPT can be adapted to your application to enhance user interaction through natural language.
A step-by-step guide to building your own Copilot — how AI can unlock productivity in software applications by turning untapped features into powerful tools.
Use OpenAI to enable users to chat with your business data by learning how to build a custom ChatGPT using Cosmos Mongo DB vCore and Python.
How vector databases are transforming AI applications and LLMs by enabling efficient handling of unstructured data and fast similarity search.
This tutorial guides you through creating an intelligent bot fetching and sharing relevant news updates on Twitter.
A ChatGPT Python script that simulates a conversation between a pirate and a nobleman on life's meaning. Customize the characters; code on GitHub.
How the Swiss economy is adopting OpenAI — from companies yet to explore its potential to those with a proven track record of implementing it.
Discover typical challenges and learn how to engineer prompts for the successful use of ChatGPT in a business context.
Learn about digital friction and how OpenAI's GPT technology can help reduce it, improving user experience across products and services.
This ChatGPT style guide presents voice and tone options and explains how you can trigger them with simple language.
Move towards responsible AI by using FairLearn, an open-source Python package for assessing and mitigating unfairness in machine learning.
Discover the unique value proposition of OpenAI's GPT models, including ChatGPT, and why they're causing such a buzz in the world of AI.
Learn about the top 9 use cases for implementing OpenAI's GPT models such as ChatGPT, and Davinci in a business context.
Just a year ago, ChatGPT was launched, and it has since catalyzed a seismic shift in the AI landscape. Given the astonishing capabilities of generative AI…
In this step-by-step tutorial, we show how to generate detailed OpenAI DALL-E prompts using ChatGPT in Python.
Unleash the power of AI with OpenAI's models: A comprehensive guide on how to interact with ChatGPT, and other language models via the OpenAI API in Python.
By using machine learning and Python, businesses can predict equipment failures before they happen and optimize their maintenance cycles.
In this tutorial, we will use Python and the scikit-learn library to apply hierarchical clustering to a dataset of customer data.
This tutorial gives an overview of Facebook Prophet and shows how to use the framework in Python to create a univariate time series forecast.
The basic idea behind naive Bayes is that we can use the probabilities of each feature belonging to a particular class to predict the likelihood that a…
Machine learning is transforming the insurance industry by providing new and powerful ways to analyze and manage risk.
This article combines blockchain data and historic crypto prices from CryptoCompare in a comprehensive on-chain analysis using Python.
The Python library Pandas is a useful package that makes it easy to access a variety of popular data sources on the Internet.
This guide provides tips on feature exploration, engineering, and selection for machine learning using Python and Scikit-Learn
This article shows how to employ a bag of words model and cosine similarities to create a content-based movie recommender with Python.
This tutorial shows how to use affinity propagation to analyze asset clusters in the crypto market using Python.
Learn how to use Random Search to tune the model hyperparameters of a Random Forest with Python that predicts house sale prices.
This tutorial teaches the basics of Big Data analytics with Pyspark, based on the ingestion, processing, and analysis of Zurich weather data.
This tutorial explains the concepts and architecture behind Apache Spark, a mighty big data processing and analytics framework.
Using confusion matrix and error metrics for measuring classification performance in machine learning with Python.
This tutorial develops and evaluates a Keras multi-output regression model that forecasts ten future Apple trading sessions in one pass.
This tutorial presents k-mean clustering and how to perform a cluster analysis on synthetic data with Python and Scikit-Learn.
This article describes multivariate anomaly detection in the example of credit card fraud using Random Isolation Forests in Python
This article shows how to build a recommender system for movies with python. The approach used is based on collaborative filtering.
In this article, we will create a Twitter signal bot in Python that analyzes crypto prices for notable events and notifies users on Twitter.
The Gate.io API provides access to market prices of a variety of cryptocurrencies. Learn to retrieve price data via the API using Python.
This article lays the foundation for building a Twitter bot by showing how to submit tweets via the Twitter API using Python and Tweepy.
Learn how to request crypto price data from the Coinmarketcap REST API and store it in a local SQLite DB using Python and Pewee.
This article predicts crime types in San Francisco with the XGboost classifier in Python and displays them on a crime map of SF
This Python tutorial shows how to use Auto-ARIMA for time series forecasting using the example of forecasting beer sales.
Create color-coded cryptocurrency price charts with Python to visualize the lag between price points and the bitcoin halving.
Discover how you can use the twitter API to access tweets and images from twitter with Python and use them in your data science project.
Learn about image classification with deep learning and develop a convolutional neural network that distinguishes between cats and dogs!
This tutorial shows how to build a customer churn prediction model in telecommunications. We will use Python and measure feature importance.
Learn how to tune the model hyperparameters of a Random Forest that predicts the survival of Titanic passengers using grid search in Python.
Test causal technical indicators in a leakage-free NASDAQ forecasting experiment with Python, Keras, and chronological evaluation.
This article deals with sentiment analysis and shows how to build a sentiment classifier using logistic regression and naive Bayes in Python.
This tutorial builds and evaluates a Keras multivariate LSTM that forecasts the next-session NASDAQ Composite return.
Learn to use logistic regression to solve two-class prediction problems in Python by classifying online shoppers' purchase intentions.
Learn six regression error metrics, their limitations, and how to evaluate a time series model against a baseline with Python and scikit-learn.
This tutorial shows how to create and evaluate a recursive multi-step forecast for a rising sine curve using Keras LSTM layers in Python.
Geographic heat maps are powerful to visualize spatial data. Learn to use heat maps with Python and GeoPandas to visualize COVID-19 data
COVID-19 has had a strong impact on the global stock market. We can measure this influence with a stock market correlation matrix in Python.
This tutorial shows how to build a direct five-trading-session forecast using a Keras recurrent neural network and Python.
This article shows how to train a univariate neural network model for stock market forecasting with Python and Scikit-learn.
This article shows how to access remote data sources via REST APIs in Python. Two examples are given: Using Pandas Webreader and Requests.
Get started with Python Machine Learning and set up the Anaconda Python Environment and Python packages, incl. Jupyter Notebooks.
Want to learn about flight delay prediction? This tutorial develops a classifier in Azure Machine Learning that predicts flight delays.