You are not alone. Both concepts explained simply...
📚 Machine Learning (ML) is a subset of artificial intelligence focused on training on data and improving performance over time ("gradient descent"). Instead of programming explicit rules, ML algorithms identify patterns in data and make predictions or decisions based on those patterns.
🧠 Deep Learning (DL), on the other hand, is a specialised form of Machine Learning that uses artificial neural networks to model complex patterns in data. These networks are designed to mimic the structure and function of the human brain, making them incredibly powerful at learning from large amounts of data.
🌳 You can compare Machine Learning to planting a tree: you start with a seed (data) and as it grows (learns), branches (rules) form to help the tree make decisions ("decision tree").
Deep Learning, on the other hand, is like planting an entire forest: an enormous network of interconnected trees, each contributing to the overall decision-making process.
🖼️ Suppose we want to train a model that can determine whether a photo contains a dog or a cat. Both ML and DL learn from labelled data, but there is a difference in how they identify features. ML depends more on "feature engineering", where more human adjustment is needed to achieve a good result. DL is largely capable of recognising features automatically, down to small details such as fur or the shape of a nose or ears.
Deep Learning is a more advanced technique that resembles human learning, but Machine Learning can be easier to interpret and can converge with smaller datasets, so both certainly have their own unique applications and advantages.
