Data Science and Machine Learning are buzzwords these days, but do you know what they mean and what the difference between them is? Let's find out in this article.
Data Science is a discipline that deals with making sense of the large amounts of data available these days. On the other hand, Machine Learning involves machines being given data to learn from and then making decisions based on that data.
Still confused? Let's clarify with an illustration: Take, for example, an online e-marketplace company that has a large amount of customer data. A data scientist would analyze this data to infer useful information. For instance, they might hypothesize targeted advertising strategies based on individual customer preferences. In contrast, Machine Learning involves using the data to predict customer behavior, such as whether a customer will click on an ad.
How does a Machine Learning model do this? It learns from the preferences of a large number of customers and predicts ad clicks based on this learning. This process happens thousands or even millions of times, and with each iteration, the model improves.
Another example of Machine Learning is a model trained on a large amount of text data. After learning from this data, it can predict the next word in a sequence, similar to how Google suggests search terms as you type.
In conclusion, Data Science is about humans making sense of data, while Machine Learning is about machines learning from data and predicting future outcomes.
I hope you have learned something from this. Thank you so much for your time.
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AI