Pure ML vs Applied ML

⚡𝐏𝐮𝐫𝐞 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 is a subfield of artificial intelligence that focuses on the development and study of algorithms that can learn from and make predictions or decisions based on data. It involves the study of how these algorithms can be designed to learn from data, identify patterns, and make decisions with minimal human intervention. It is about understanding the underlying principles and theories of machine learning algorithms.

Consider a scenario where you have a dataset of images labeled as either cats or dogs. In pure machine learning, you would use this dataset to train an algorithm to recognize the features that distinguish cats from dogs. The algorithm would analyze the images, learn the common characteristics of each category, and then be able to classify new images as either a cat or a dog based on what it has learned.

⚡𝐀𝐩𝐩𝐥𝐢𝐞𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 refers to the practical application of machine learning techniques to solve real-world problems. It involves using algorithms and statistical models to analyze and interpret complex data, make predictions, or automate decision-making processes. Unlike pure machine learning research, which focuses on developing new algorithms or improving existing ones, applied machine learning is about using these tools to deliver tangible results in various fields such as finance, healthcare, marketing, and more.

For example, in healthcare, applied machine learning might be used to predict patient outcomes based on historical data, while in finance, it could be used to detect fraudulent transactions. The key aspect of applied machine learning is its focus on practical implementation and the ability to create value from data by making informed decisions or predictions.

Both pure and applied machine learning are crucial for the field's progress. Pure ML advancements provide the foundation for applied ML applications, while applied ML success stories motivate further pure ML research.

𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬: https://lnkd.in/gVDDBUim