Here, you’ll find everything on REST APIs whether it’s tutorials on how to access specific APIs, or conceptual articles explaining how APIs work.
REST APIs (Representational State Transfer APIs) play a key role in machine learning by providing a way for different systems to communicate and share data and functionality. In the context of machine learning, REST APIs are used for various purposes, including sharing data, sharing system functionality, and connecting different systems with one another and enabling them to work together and exchange data. Overall, REST APIs are an important part of the machine learning ecosystem, and they provide a way for different systems to interoperate and share data and functionality.
You haven’t had enough of the recent hype about OpenAI? Fantastic! Let’s explore the business use cases for ChatGPT and other Large Language Models (LLMs). ChatGPT is often considered the beginning of a new era of AI adoption. A growing number of ChatGPT users are now using AI in their personal workflows. However, adoption in … Read more
In this article, we will explore how to automate the creation of AI-generated art by integrating DALL-E with ChatGPT using the respective APIs in Python. ChatGPT, the state-of-the-art language model developed by OpenAI, has recently made waves in the tech community for its exceptional language abilities, such as code generation, prompt answering, and text completion. … Read more
ChatGPT, developed by San Francisco-based OpenAI, is a revolutionary AI chatbot that uses artificial intelligence to generate coherent responses to user inquiries. Unlike traditional search engines, ChatGPT uses artificial intelligence to generate coherent responses to user inquiries rather than providing standard answers. The results are surprisingly good and have created a massive worldwide sensation. Many … Read more
Predictive maintenance is a game-changer for the modern industry. Still, it is based on a simple idea: By using machine learning algorithms, businesses can predict equipment failures before they happen. This approach can help businesses improve their operations by reducing the need for reactive, unplanned maintenance and by enabling them to schedule maintenance activities during … Read more
Have you ever wondered how Facebook predicts the future? Meet Facebook Prophet, the open-source time series forecasting tool developed by Facebook’s Core Data Science team. Built on top of the PyStan library, Facebook Prophet offers a simple and intuitive interface for creating forecasts using historical data. What sets Facebook Prophet apart is its highly modular … Read more
Cryptocurrencies like Bitcoin or Ethereum are built on public blockchains, meaning anyone can see the transactions and trades happening on these networks. This transparency makes on-chain data an excellent resource for data science and machine learning. By examining transaction activity and the holdings of Bitcoin addresses, analysts can better understand a cryptocurrency network’s health and … Read more
Pandas DataReader is a library that allows data scientists to easily read data from a variety of sources into a Pandas DataFrame. This is especially useful for accessing data that resides outside of their local development environment and needs to be accessed via APIs. The Pandas DataReader provides functions for loading data from various online … Read more
Affinity propagation is a powerful unsupervised clustering technique that can identify hidden patterns in large datasets. In the cryptocurrency world, where new coins are constantly emerging and prices can be highly volatile, affinity propagation can help investors simplify the chaos. By analyzing historical price data, affinity propagation groups coins into clusters based on their past … Read more
Multi-output time series regression can forecast several steps of a time series at once. The number of neurons in the final output layer determines how many steps the model can predict. Models with one output return single-step forecasts. Models with various outputs can return entire series of time steps and thus deliver a more detailed … Read more
Embark on a journey into the world of unsupervised machine learning with this beginner-friendly Python tutorial focusing on K-Means clustering, a powerful technique used to group similar data points into distinct clusters. This invaluable tool helps us make sense of complex datasets, finding hidden patterns and associations without the need for a predetermined target variable. … Read more
This tutorial develops a Twitter bot in Python that will generate automated trading signals. The bot will pull real-time price data on various cryptocurrencies (Bitcoin, Ethereum, Doge, etc.) from the crypto exchange Gate.io and analyze it using predefined rules. Whenever the bot detects a relevant price change, it automatically posts a tweet via Twitter. Simple … Read more
