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Designing a Learning System in Machine Learning

Machine learning systems have made analysis much easier since it can process tons of data in seconds. The down side is that , although its speed and laser sharp accuracy in identifying dangerous risks or profitable results still needs time to train. Combing artificial intelligence and machine learning technologies is what makes it more efficient.

The increase in the use of machine learning is due to the collaborations of artificial intelligence, machine learning and machine learning platforms. Fortunately enough, you don’t need to be a data scientist to design a machine learning model.

Here are steps to briefly explain how it is developed:

  1. Define the Goal – Know the objective of your business. Understand well what you want to achieve with that data. You can use regression to specify the numbers of particular subjects
  2. Prepare Data for ML – Assure consistent data, usable and clean. This is what makes the largest part. It involves merging and mixing data, analyzing and exploring, feature selection and feature engineering and handling.
  3. Build the Model – AutoML simplifies the process by automating the whole process. It involved building the baseline, designing the model, training the model and deciding the algorithm and hyperparameters.
  4. Tune the Model – Tracking and doing a comparison of the model performance. It involved evaluating metrics and checking overfitting and regularization.
  5. Model Interpretation – The degree where a human can understand the model is checked. It involves partial dependence plots, subpopulation analysis, individual and prediction explanations.

Topics in Machine Learning

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