11 key concepts of Machine Learning.

— Supervised Learning Edition —



Before starting, remember that, if you follow me, one of your enemies will be immediately destroyed (and you'll get to read more of these threads, of course.)

And if you don't follow me, well, you just hurt my feelings.

1. Labels

(Also referred to as "y")

The label is the piece of information that we are predicting.

For example:

- the animal that's shown in a picture
- the price of a house
- whether a message is spam or not

2. Features

(Also referred to as "x")

These are the input variables to our problem. We use these features to predict the "label."

For example:

- pixels of a picture
- number of bedrooms of a house
- square footage of a house

3. Samples

(This is also known as "examples.")

A sample is a particular instance of data (features or "x.") It could be "labeled" or "unlabeled."

4. Labeled sample

Labeled samples are used to train and validate the model. These are usually represented as (x, y), where "x" is a vector containing all the features, and "y" is the corresponding label.

For example, a labeled sample could be:

([3, 2, 1500], 350000)
5. Unlabeled sample

Unlabeled samples contain features, but they don't contain the label: (x, ?)

We usually use a model to predict the labels of unlabeled samples.

6. Model

A model defines the relationship between features and the label.

You can think of a model as a set of rules that, given certain features, determines the corresponding label.

For example, given the # of bedrooms, bathrooms, and square footage, we get the price.

7. Training

Training is a process that builds a model.

We show the model labeled samples during training and allow the model to gradually learn the relationships between features and the label.

8. Validation

Validation is the process that lets us know whether a model is any good.

Usually, we run a set of (unseen) labeled samples through a model to ensure that it can predict the labels.

9. Inference

Inference is the process of applying a trained model to unlabeled samples to obtain the corresponding labels.

In other words, "inference" is the process of making predictions using a model.

10. Regression

A regression model predicts continuous values, for example:

- the value of a house
- the price of a stock
- tomorrow's temperature

11. Classification

A classification model predicts discrete values, for example:

- the picture is showing a dog or a cat
- the message is spam or not
- the forecast is sunny or overcast

More from Santiago

Free machine learning education.

Many top universities are making their Machine Learning and Deep Learning programs publicly available. All of this information is now online and free for everyone!

Here are 6 of these programs. Pick one and get started!

Introduction to Deep Learning
MIT Course 6.S191
Alexander Amini and Ava Soleimany

Introductory course on deep learning methods and practical experience using TensorFlow. Covers applications to computer vision, natural language processing, and more.


Deep Learning
NYU DS-GA 1008
Yann LeCun and Alfredo Canziani

This course covers the latest techniques in deep learning and representation learning with applications to computer vision, natural language understanding, and speech recognition.


Designing, Visualizing, and Understanding Deep Neural Networks
UC Berkeley CS L182
John Canny

A theoretical course focusing on design principles and best practices to design deep neural networks.


Applied Machine Learning
Cornell Tech CS 5787
Volodymyr Kuleshov

A machine learning introductory course that starts from the very basics, covering all of the most important machine learning algorithms and how to apply them in practice.


More from Machine learning

Starting a new project using #Angular? Here is a list of all the stuff i use to launch my projects the fastest i can.


Have you heard about Monorepo? I created one with all my Angular (and Nest) projects using

I can share A LOT of code with it. Ex: Everytime i start a new project, i just need to import an Auth lib, that i created, and all Auth related stuff is set up.

Everyone in the Angular community knows about https://t.co/kDnunQZnxE. It's not the most beautiful component library out there, but it's good and easy to work with.

There's a bunch of state management solutions for Angular, but https://t.co/RJwpn74Qev is by far my favorite.

There's a lot of boilerplate, but you can solve this with the built-in schematics and/or with your own schematics

Are you not using custom schematics yet? Take a look at this:


You can automate all the boilerplate with hundreds of files associates with creating a new feature.
Happy 2⃣0⃣2⃣1⃣ to all.🎇

For any Learning machines out there, here are a list of my fav online investing resources. Feel free to add yours.

Let's dive in.

Investing Services

✔️ @themotleyfool - @TMFStockAdvisor & @TMFRuleBreakers services

✔️ @7investing

✔️ @investing_city

✔️ @MorningstarInc Premium

✔️ @SeekingAlpha Marketplaces (Check your area of interest, Free trials, Quality, track record...)

General Finance/Investing

✔️ @morganhousel

✔️ @dollarsanddata

✔️ @awealthofcs

✔️ @iancassel

✔️ @InvestorAmnesia


Tech focused

✔️ @stratechery

✔️ @bgurley

✔️ @CBinsights

✔️ @benedictevans


Tech Deep dives

✔️ @StackInvesting

✔️ @hhhypergrowth

✔️ @Beth_Kindig

✔️ @SeifelCapital

✔️ @borrowed_ideas

You May Also Like

1/ Here’s a list of conversational frameworks I’ve picked up that have been helpful.

Please add your own.

2/ The Magic Question: "What would need to be true for you

3/ On evaluating where someone’s head is at regarding a topic they are being wishy-washy about or delaying.

“Gun to the head—what would you decide now?”

“Fast forward 6 months after your sabbatical--how would you decide: what criteria is most important to you?”

4/ Other Q’s re: decisions:

“Putting aside a list of pros/cons, what’s the *one* reason you’re doing this?” “Why is that the most important reason?”

“What’s end-game here?”

“What does success look like in a world where you pick that path?”

5/ When listening, after empathizing, and wanting to help them make their own decisions without imposing your world view:

“What would the best version of yourself do”?