7 Phases of Machine Learning

How does the magic occur?

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Machine Learning has so many applications in daily life, and it's going to be crucial for humanity in the next few decades. But how does it work exactly?

Steps of Machine Learning

  1. Import the data. Generally, you will need .csv files with data enough to treat the problem you're trying to solve.
  2. Clean the data. Now, before using this data, you need the review it and get rid of repetitive items or irrelevant information.
  3. Split the data. You'll need to separate the data you have. The first group will be used for training your model, and the second group will be used for test sets.
  4. Create a model. Actually, you don't have to create an algorithm. There are libraries that provide useful models for your machine learning project (such as Numpy). These models are based on prediction techniques, and all of them have pros and cons. The final decision on which model is better for you depends on the problem you are working with.
  5. Train the model. This is going to take some time, depending on how complex is your project.
  6. Make predictions (test sets). Cool errors happen here.
  7. Evaluate and improve your model. Repeat 🙂