Tell me about ML, machine learning. All about it.
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. 

Machine learning is an important component of the growing field of data science. The basic concept of machine learning in data science involves using statistical learning and optimization methods that let computers analyze datasets and identify patterns through the use of statistical methods. Algorithms are trained to make classifications or predictions, and to uncover insights in data mining. These insights subsequently drive decision making within applications and businesses. 

Machine learning algorithms are typically created using frameworks that accelerate solution development, such as TensorFlow and PyTorch.

The typical supervised machine learning algorithm consists of roughly three components:
1. A decision process: A recipe of calculations or other steps that takes in the data and “guesses” what kind of pattern your algorithm is looking to find.
2. An error function: A method of measuring how good the guess was by comparing it to known examples (when they are available). Did the decision process get it right? If not, how do you quantify “how bad” the miss was?
3. An updating or optimization process: A method in which the algorithm looks at the miss and then updates how the decision process comes to the final decision, so next time the miss won’t be as great.

I hope this answers your question.
What does supervised in the context of Machine Learning mean?
Supervised learning is a type of machine learning algorithm where the model is trained on a labeled dataset, which means that the input data is accompanied by the correct output. In other words, the model is given a set of inputs and corresponding outputs, and it learns to make predictions based on that input-output mapping.

During the training process, the algorithm uses this labeled data to build a model that can accurately predict the correct output for new, unseen input data. The algorithm tries to find patterns in the data that will enable it to make accurate predictions when given new data.

Some common examples of supervised learning include image classification, language translation, and speech recognition.
What are some popular machine learning algorithms used in supervised learning?