Answer 1

I may be wrong with some things I say here...

Essentially Data Science is analyzing large datasets. The goal is to pull useful conclusions out of data or to create a way to predict a certain variable. This is where Machine Learning comes into play. It's really used to predict variables. Some of it uses Neural Networks like a lot of AI, and some doesn't. In general, the idea is to create a model where you can supply some explanatory variable values and it will output what it thinks the response variable value will be. A good example of this is regression.

Regression is used to find trends within data. Let's look at global warming per year. It's growing, right? So each year the planet's average temperature increases. What a regression would do is tell us by how much it is expected to change per year. We can have a regression function that will give us that information and we can then use it to predict the future changes. Let's make up an equation now: y = 0.004x + 62. Where x is the year, 72 is the intercept, 0.04 is the amount of degrees that the world can expect to increase by per year, and y is the expected global temperature of the year you've supplied (of course, this is not actually representative of the global climate). So with this equation, we can say that the average global temperature in 1500 was 68 degrees. We can also say that this year, the average global temperature is expected to be 70.08 degrees. Then we can extrapolate and say that the average global temperature in the year 2552 is 72.208 degrees. That is the purpose of regression and machine learning overall, but how does it work?

Well, there's a lot of different machine learning algorithms. What's probably most common is the least-squares regression. You may have learned about this. It's the same as a line of best fit! As stated earlier, the regression will help you to find trends in the data. There are many algorithms that go about classifying data in a number of ways. If you're interested, look into it more! Data Science is a blast.