Thread by @Jeande_d

Machine learning is the science of teaching the computer to do certain tasks, where instead of hardcoding it, we give it the data that contains what we want to achieve, and its job is to learn from such data to find the patterns that map what we want to achieve and provided data.

These patterns or (learned) rules can be used to make predictions on unseen data.

A machine learning model is nothing other than a mathematical function whose coefficient and intercept hold the best (or learned) values representing the provided data & what we want to achieve.

In ML terms, coefficients are weights, intercepts are biases.

Take an example for a simple straight line equation:
y = aX + b

y is the label
X is the input data
a is weight/coefficient
b is bias/intercept

So basically,

If you have a set of values X and y, and you are asked to use those values to find `a` and `b`,

you would crunch numbers until you find them as long as you know you are dealing with a line equation.

Let's say that the below table is X and y.

Would you find the relationship between X and y?

If you can be able to find such a relationship, you can get the corresponding value of 9 easily.

Say you are able to find that y = 2X + 1 from the above table. You can use this equation to find any value of y given X.

Finding the value of 9 would be the very straight thing.

y = 9*2 + 1 = 19

That is fairly easy to calculate and you would not need machine learning.

But let's forget that for a minute, and use machine learning to find a model that we can use to predict y given 9.

About tooling, for problem like that, we can use Scikit-Learn, a classical ML framework that is well designed.

We can also use NumPy to represent our data as an array of values.

And Matplotlib for plotting the line. It's always good to plot the data that you are working with.

The thing called Linear regression above (imported from sklearn) is a type of machine learning model that is used for linear datasets (ideally),

but primarily, it is used when we want to predict a single continuous value.

A linear model is the best fit for our data because our data seems to be linear.

A meaning of linearity: Change in the input data(X) is directly proportional to change in output (y).

P.S there are more complex models suited for non-linear datasets like support vector machines.

So, now it's time to build our model and train or teach it on our input data X and output label y.

The whole goal of training a machine learning model is to find the best values of weight (a) and bias (b).

It's simple as we are not reinventing the wheel.

The nice thing about having good data (technically, good data has a predictive power) and a relevant model, is that you do not need to tweak a lot of things.

In addition to that, Linear Regression doesn't have things to tweaks (hyperparameters)

To remove the confusion of a newly introduced term called hyperparameters, we got to understand parameters first.

Parameters are model values (weight and biases), and hyperparameters are set of values that engineer has to set or change to get good results.

We can change the hyperparameters, but we do not change parameters. As we train, the model learns the best values of parameters (weight & bias).

More about hyperparameters and parameters. By @svpino and @AlejandroPiad replies

https://twitter.com/svpino/status/1433800148526764034?s=20

So, now having a model, the next thing would be to predict the value of y given 9. That's the initial question.

Perfect, you see it's 100% correct. y(9.0) = 19.0

If you wanted to access the model parameters, or weight and bias, you can get them too.

And remember, in equation y = aX + b, a is the weight or coefficient of the equation, and b is the bias or intercept.

Looking at the below image, these values of weight and bias are ☑☑

This was a simple thing to do, but I find it good to start simple when explaining a hard topic.

Plus it can motivate you to do more. I was personally motivated by these simple things in my early days of doing machine learning.

This is the end of the thread.

Initially, I wasn't planning a thread but then thought that extending it would perhaps be helpful to anyone who still wonders what machine learning really is.

Thank you for reading.

You can follow @Jeande_d for more simple posts like this.

P.S. One of my 2021's goals was to do something tangible for the ML community. I found that it would be cool to make well-structured content/curriculum.

It's close and I will share detail soon