Here are some common machine learning terms that you should know ✨🧵

Let's say we want to teach a computer to recognize pictures of cats and dogs. We start with a "dataset" of pictures of cats and dogs, and we give each image a label - either cat 🐱 or dog 🐶.

1/ Dataset: A data collection used to train a machine learning model. It usually consists of input data (features) and output data (labels).

2/ Feature: A measurable property of an object or phenomenon used as input to a machine-learning model. In this case, the features of each picture include things like the shape of the ears, the color of the fur, or the animal's size.

3/ Label: The output of a machine learning model. It represents the target variable that the model is trying to predict. Here, it's either 🐱 or 🐶.

4/ Model: A mathematical representation of the relationship between the input data (features) and the output data (labels). A machine learning model aims to learn this relationship and use it to make predictions on new data.

5/ Training: Using a dataset to train a machine learning model. During training, we show the model many pictures of cats and dogs and their labels. The model looks at the features of each image and tries to learn what features are associated with cats and dogs.

6/ Validation: Once the model has been trained, we use a separate dataset to validate its performance. We show the model pictures it hasn't seen before and ask it to predict whether each image is a cat or a dog.

7/ Overfitting: When a machine learning model becomes too complex and starts to fit the training data too closely. It will do well on the training data but not the new one.

8/ Underfitting: When a machine learning model is too simple and cannot capture the complexity of the data. In this case, the model will not do well on either training or new data.

There are many other machine learning terms and concepts to explore, but understanding these basics is a great starting point. Keep learning and experimenting with building your machine-learning skills!