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**Machine learning is not just about importing an algorithm**

Machine learning is a part of data science. There are several machine learning algorithms that we use to learn from data.

In case of supervised learning, we train an algorithm with known data and expect it to make predictions on new observations. Unsupervised learning algorithms provide insight into the underlying structure within the data or the relationships among the observations.

In both cases, the processing of raw data is extremely important to get reliable and accurate results. We cannot just dump the raw data into a ready-to-use algorithm and expect outstanding results.

The raw data might contain outliers that negatively affect the performance of a model. There might also be some missing values in the data. They need to be carefully handled to preserve the integrity of features.

How we perform these operations has a large impact on the model performance. In order to handle them appropriately, we need to have a strong statistical knowledge. For instance, we use statistical techniques to mark the outliers. Similarly, the appropriate replacement for a missing value is determined with the help of statistics.

Evaluating the results of a model is just as important as creating it. We cannot just look at a metric and complete the evaluation process. In fact, it should be dynamic and iterative.

We evaluate the results to provide feedback for improving the model. For instance, it is of crucial importance to detect high bias or high variance in the results. The model is tuned or updated differently based on the patterns of errors. Statistics help us to create a valuable and informative evaluation process.

Machine learning is not just about importing an algorithm and use it. We need to prepare and process the data appropriately. Similarly, the output of a model needs to be evaluated carefully. Both tasks require statistical knowledge so it is a must-have skill for data scientists.

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