ML Data Preparation

The process of converting raw data into dataset of features and labels for training, testing and implementations of algorithm.

Data Preparation for Machine Learning

Data Preparation is a supervised task in Machine Learning, as it requires inputs like feature maps, labels, input constraints, etc. The main work in Data Preparation is to construct and design a data type, which can be used in various algorithms like Data mining, Data classification, and Data cleansing. These data types are the critical building blocks of diverse Machine Learning Algorithms like principal component analysis, neural networks, supervised learning, artificial intelligence, decision trees, etc. Data can also be used in different stages of an algorithm, like training, pre-training, post-trained, and benchmarks.

In machine learning, data preparation is basically the process of preparing data for an algorithm, the actual execution of that algorithm, and the testing. It is actually a multiple-step procedure that entails data cleansing, processing, feature extraction, and label identification. The steps are usually performed sequentially in order to ensure that the whole sequence will yield the expected results, i.e., the best classification result. Foiwe manages all critical steps to provide high quality dataset for machine learning models to perform efficiently. Both dataset and model complement each other to ensure a model performs to expectations.

Benefits of Data Preparation

Technology is evolving everyday and technocrats must  stay up to date with changing trends in Big Data and AI. And Foiwe’s Data Preparation solutions can be helpful in gaining that edge, at a fraction of the cost. 

End-to-end Preparation
Foiwe provides assistance in all steps involved in preparing unsorted data to train your neural engine for any project.
Curated by Experts
Unstructured data are categorized keeping human sentiments and actions in mind -resulting in a natural learning.
Fully Accounted Logging
Every action in each step is logged and signed digitally to ensure data safety.

Case Studies and Reports

Applications and Capabilities

Data Tagging also helps in machine learning, and in particular, it is helpful for large machine learning tasks, which in turn helps in providing better quality solutions to problems faced by users. 

Applications

  • Processing Big Data

Capabilities

  • A Cleaner and structured data stream results in refined machine learning. Foiwe is at the root of the insights that drive businesses forward.

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