clusterify.ai
© 2025 All Rights Reserved, Clusterify.AI
NFT stands for Non-Fungible Token, which is a type of digital asset that represents ownership of a unique item or piece of content, such as a digital artwork, music, video, or collectible. NFTs are built on top of blockchain technology, which enables them to be verified as unique and to be traded and transferred securely […]
Ethereum ERC-721 is a standard for Non-Fungible Tokens (NFTs) on the Ethereum blockchain. It defines a set of rules and requirements for creating and managing unique digital assets that can be owned, traded, and transferred just like physical assets. The ERC-721 standard defines a specific set of smart contract functions that must be implemented in […]
Ethereum ERC-1155 is a standard for creating and managing both fungible and non-fungible tokens (NFTs) on the Ethereum blockchain. It was proposed to improve upon the ERC-20 and ERC-721 standards for creating and managing fungible and non-fungible tokens, respectively. The ERC-1155 standard allows for the creation of a single smart contract that can manage both […]
Data analysis is the process of systematically examining data to extract useful information and insights. This process involves a wide range of techniques, tools and approaches to make sense of data and it can be used for various purposes and different kind of data. It is a critical step in the process of understanding and making […]
Q-learning is a type of reinforcement learning algorithm that is used to train agents to make decisions in an environment. The goal of Q-learning is to find the optimal policy, which is a mapping from states to actions that maximizes the expected long-term reward. In Q-learning, an agent interacts with an environment by taking actions and […]
Here is an example code in Python that demonstrates a simple machine learning pipeline using the scikit-learn library: In this example, we start by importing the necessary libraries from scikit-learn. Then we load the iris dataset using the datasets module and split the data into training and test sets using the train_test_split function. We then train a logistic regression […]