Introduction to Machine Learning for someone completely new. We don’t get hands on in this lesson but we do get into the details of what machine learning practitioners really care about. Most of the lessons require python, but this lesson has no requirements.

Topic Covered

- The relationship between AI, machine learning, and deep learning
- Linear and multivariate regression
- Training data
- Overfitting
- Loss functions
- The machine learning API

## Statistics

## Loss Functions

## The Machine Learning API

## Choosing an Algorithm

Another question that machine learning engineers grapple with, is what algorithm to use for their particular problem. Linear regression is one algorithm that can be used, but there are many others – such as Naive Bayes, logistic regression, decision trees, neural networks, and many, many more. We will learn about some of these in the upcoming videos, so stay tuned if you’re curious.

So with this knowledge we can now revisit our original question: what counts as a machine learning problem? Anything that can be transformed into the restrictive API of number inputs and number outputs, and has training data. There are many ways we can pre format our data and interpret our output that helps us expand this use case, but if your problem still cannot fit into this definition, then it is probably not suitable for Machine Learning.

In the next tutorial, you will learn to build a Convolutional Neural Network (CNN).