Learn more about computerscience with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
Steps
- Formatting: The data is spread in different formats. Formatting will bring it together in one sheet. For example, customer data can come with different currencies, languages, etc. These need to be compiled under one format.
- Labeling: Labeling is done to ensure the data set works for your model. For example, a self-driving car will need data labeled as pictures of cars, pedestrians, street signs, footpaths etc.
- Data Cleaning: Unwanted characters are removed and missing values are dealt with.
- Feature extraction: A number of features are analyzed and optimized. Features that are important for prediction are selected for quicker computation and less memory consumption.
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MORE IDEAS ON THIS
Mistake - Don’t prioritize data curation
As AI integration across industries picks greater pace, ML engineers are confronted with a sad reality - once stakeholders identify a use case with proven ROI, they are eager to jump onto the AI ship, and dat...
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Testing Data
Testing data is used to test the validity of the training data set. Training data is not used for testing because it will produce the expected output. The testing data set comprises of 20 percent of the total data.
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How to start curating
The process of curating datasets for machine learning starts well before availing datasets. Here’s what we suggest:
- Identify the goal of AI
- Identify what dataset you will need to solve the problem
- Make a record of your assumptions while selecting the data
- Aim fo...
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Small Dataset = use pre-trained model
If you have a small dataset, using a model pre-trained on large datasets can be a good idea. You can use your small dataset to fine-tune it.
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Start with Datasets
Data is the new oil - and just as oil needs the right refining to come into perfect usage, data too needs curing. The power of your machine learning models will greatly depend on the quality of your data.
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Types of Datasets for Machine Learning
ML engineers depend on data during each step of their AI journey – from model selection, training, and tuning to testing. These datasets usually fall under three categories:
- Training sets
- Testing sets
- Validation sets
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Training Data
The training data set is used to train an algorithm, apply concepts, learn, and give results. Around 60 percent of data is training data.
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