Training a Machine Learning Model

Learn the fundamentals of the machine learning training cycle.

In this lesson, we'll cover the process of training an ML model.

Training process

Regardless of the chosen task, the ML process consists of the following fundamental steps:

  1. Data collection: The first step is to collect and preprocess the relevant data required for the model. This includes data cleaning, normalization, feature extraction, and data labeling.

  2. Model selection: The second step is to select an appropriate ML algorithm that can learn from the data and make predictions.

  3. Model training: The third step is to train the selected ML algorithm on the preprocessed data. During training, the algorithm learns from the data and adjusts its parameters to minimize the error between the predicted output and the actual output.

  4. Model evaluation: The fourth step is to evaluate the performance of the trained model using a separate set of data called the validation set. This step helps to determine if the model is overfitting or underfitting the data.

  5. Model tuning: Based on the evaluation results, the model can be further fine-tuned by adjusting the hyperparameters to improve its performance.

  6. Model deployment: Once the model is trained and tuned to a satisfactory level, it can be deployed for making predictions on new data.

  7. Model monitoring: Finally, the model needs to be continuously monitored and updated to ensure that it remains accurate and relevant to the changing data patterns.

Before the model can be useful, it needs to be trained. Training is a cycle that can be repeated as many times as needed until we're satisfied with the quality of the output. It can be summarized by the following diagram:

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