Machine learning's ultimate goal is to create algorithms that automatically assist a system in gathering data and using that data to learn more. Systems are expected to search for patterns in collected data and use them to make critical decisions for themselves.

A typical machine-learning workflow consists of the following steps:

  1. Data collection: The first step in machine learning is to collect data that will be used to train and test the machine learning algorithm. Use data from a reliable source because it will have a direct impact on the outcome of your model. Good data is relevant, has few missing or repeated values, and accurately represents the various subcategories/classes present.
  2. Data preparation: The data collected needs to be cleaned and pre-processed, which involves tasks such as removing missing values, dealing with outliers, and scaling the data.
    • This step can be divided further into two processes:
      • Exploration of data:
        • It is used to comprehend the nature of the data with which we must contend. We must comprehend data characteristics, format, and quality.
        • A better understanding of data results in more effective results. We find correlations, general trends, and outliers in this.
      • Data pre-processing:
        • The next step is to prepare the data for analysis.
  3. Feature engineering: This step involves selecting the relevant features from the dataset that will be used to train the machine learning algorithm. Feature engineering can also involve transforming the data into a more suitable format for the algorithm. It is not necessary that data we have collected is always of our use as some of the data may not be useful. In real-world applications, collected data may have various issues, including:
    • Missing Values
    • Duplicate data
    • Invalid data
    • Noise.  So, we use various filtering techniques to clean the data.
  4. Model selection: The next step is to select the appropriate machine learning algorithm for the problem at hand. This depends on factors such as the type of problem, the type of data, and the performance metrics required.
  5. Training the model: The selected machine learning algorithm is trained on the training dataset. The algorithm learns from the data and adjusts its internal parameters to minimize the error.
  6. Model evaluation: After the model has been trained, it needs to be evaluated on a separate test dataset to determine its performance. This step helps to determine how well the model generalizes to new, unseen data.
  7. Model tuning: Based on the results of the evaluation, the model may need to be tuned by adjusting its parameters or changing the feature set.
  8. Deployment: Once the model has been trained and tested, it can be deployed in a production environment to make predictions on new data.
  9. Monitoring: The performance of the deployed model needs to be monitored over time to ensure that it continues to perform well and that any issues are addressed.

These steps are frequently iterative, and they may need to be repeated several times before a satisfactory result is obtained.

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