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Training Data

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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  1. 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.
  2. Labeling: ...

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...

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...

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.

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:

  1. Training sets
  2. Testing sets
  3. Validation sets

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.

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.

Validation tests are used to identify and tune the ML model.

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Machine learning (ML) is a subfield of artificial intelligence (AI). The goal of ML is to make computers learn from the data that you give them. Instead of writing code that describes the action the computer should take, your code provides an algorithm that adapts based on examples of intended be...

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The biggest problem, thoug h, is that models like this one are performed only on a single task. Future tasks require a new set of data points as well as equal or more amount of resources.

Transfer learning is an approach in deep learning (and machine learning) where knowledge is ...

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A Guide to Artificial Intelligence Model Development

Machine Learning (ML) is an integral part of Data Science (DS), that helps computers learn from data. 

This process of learning from data through machine learning techniques contributes to Artificial Intelligence (AI).

Here is a quick...

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published 11 ideas

The terminologies might change, the technologies might evolve, but this is the future. And that Future is already here!

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