SumanKA_StandardPythonLanguage_TipsAndTricksCommandsInfo.csv

ISO Standardised Stages/Processes of AIML DEVELOPMENT / Data Analysis

https://www.iso.org/sectors/it-technologies/ai
https://www.iso.org/artificial-intelligence
https://www.iso.org/artificial-intelligence/machine-learning
https://www.iso.org/standard/74438.html (ISO/IEC 23053:2022, Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML))


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How machine learning works
The first step in machine learning is collecting relevant data which may come from sources such as databases, sensors or the Internet.

1) Preprocessing data: Once the data is collected, it needs to be preprocessed to ensure its quality and suitability for analysis.
2) Training the model: The next step is to train a machine learning model – an algorithm or mathematical representation that learns to make predictions or decisions from input data.
3) Feature selection and engineering: That machine learning model then selects the most relevant features from the input data that will have a significant impact on the model’s performance.
4) Evaluating and optimizing the model: Once a model is trained, it needs to be evaluated to assess its performance and determine whether it meets the desired criteria.
5) Deployment and monitoring: After successful training and evaluation, the model can be deployed in real-world applications of machine learning.
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Data Registration
Data Preperation
Data Cleaning

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