# What is Machine Learning?
Machine learning (ML) powers some of the most important technologies we use,
from translation apps to autonomous vehicles. This course explains the core
concepts behind ML.
ML offers a new way to solve problems, answer complex questions, and create new
content. ML can predict the weather, estimate travel times, recommend
songs, auto-complete sentences, summarize articles, and generate
never-seen-before images.
In basic terms, ML is the process of
[training](https://developers.google.com/machine-learning/glossary#training) a piece of software, called a
[model](https://developers.google.com/machine-learning/glossary#model), to make useful
[predictions](https://developers.google.com/machine-learning/glossary#prediction) or generate content
(like text, images, audio, or video) from
data.
For example, suppose we wanted to create an app to predict rainfall. We could
use either a traditional approach or an ML approach. Using a traditional
approach, we'd create a physics-based representation of the Earth's atmosphere
and surface, computing massive amounts of fluid dynamics equations. This is
incredibly difficult.
Using an ML approach, we would give an ML model enormous amounts of weather data
until the ML model eventually *learned* the mathematical relationship between
weather patterns that produce differing amounts of rain. We would then give the
model the current weather data, and it would predict the amount of rain.
[Video](https://www.youtube.com/watch?v=RdC7KZ6OFPA)
### Check Your Understanding
What is a "model" in machine learning? A model is a mathematical relationship derived from data that an ML system uses to make predictions A model is a piece of computer hardware A model is a smaller representation of the thing you're studying.
## Types of ML Systems
ML systems fall into one or more of the following categories based on how they
learn to make predictions or generate content:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Generative AI
## Supervised learning
[Supervised learning](https://developers.google.com/machine-learning/glossary#supervised-machine-learning)
models can make predictions after seeing lots of data with the correct answers
and then discovering the connections between the elements in the data that
produce the correct answers. This is like a student learning new material by
studying old exams that contain both questions and answers. Once the student has
trained on enough old exams, the student is well prepared to take a new exam.
These ML systems are "supervised" in the sense that a human gives the ML system
data with the known correct results.
Two of the most common use cases for supervised learning are regression and
classification.
### Regression
A [regression model](https://developers.google.com/machine-learning/glossary#regression-model) predicts a
numeric value. For example, a weather model that predicts the amount of rain, in
inches or millimeters, is a regression model.
See the table below for more examples of regression models:
| Scenario | Possible input data | Numeric prediction |
|---|---|---|
| Future house price | Square footage, zip code, number of bedrooms and bathrooms, lot size, mortgage interest rate, property tax rate, construction costs, and number of homes for sale in the area. | The price of the home. |
| Future ride time | Historical traffic conditions (gathered from smartphones, traffic sensors, ride-hailing and other navigation applications), distance from destination, and weather conditions. | The time in minutes and seconds to arrive at a destination. |
### Classification
[Classification models](https://developers.google.com/machine-learning/glossary#classification-model) predict
the likelihood that something belongs to a category. Unlike regression models,
whose output is a number, classification models output a value that states
whether or not something belongs to a particular category. For example,
classification models are used to predict if an email is spam or if a photo
contains a cat.
Classification models are divided into two groups: binary classification and
multiclass classification. Binary classification models output a value from a
class that contains only two values, for example, a model that outputs either
`rain` or `no rain`. Multiclass classification models output a value from a
class that contains more than two values, for example, a model that can output
either `rain`, `hail`, `snow`, or `sleet`.
[Video](https://www.youtube.com/watch?v=NNue6rkDrws)
### Check Your Understanding
If you wanted to use an ML model to predict energy usage for commercial buildings, what type of model would you use? Regression Energy usage is measured in kilowatt-hours (kWh), which is a number, so you'd want to use a regression model. Classification Classification models predict whether or not something belongs to a category, while regression models predict a number. Because energy usage is measured in kilowatt-hours (kWh), which is a number, you'd want to use a regression model.
## Unsupervised learning
An [unsupervised learning](https://developers.google.com/machine-learning/glossary#unsupervised-machine-learning)
model aims to identify meaningful patterns in a dataset.
For example, many unsupervised learning models rely on a technique called
[clustering](https://developers.google.com/machine-learning/glossary#clustering) to organize similar data
into groups ("clusters").
**Figure 1**. An ML model clustering similar data points.
**Figure 2**. Groups of clusters with natural demarcations.
Clustering differs from classification because the categories aren't defined by
you. For example, an unsupervised model might cluster a weather dataset based on
temperature, revealing segmentations that define the seasons. You might then
attempt to name those clusters based on your understanding of the dataset.
**Figure 3**. An ML model clustering similar weather patterns.
**Figure 4**. Clusters of weather patterns labeled as snow, sleet,
rain, and no rain.
### Check Your Understanding
What distinguishes a supervised approach from an unsupervised approach? A supervised approach is given data that contains the correct answer. A supervised approach is given data that contains the correct answer. The model's job is to find connections in the data that produce the correct answer. An unsupervised approach is given data without the correct answer. Its job is to find groupings in the data. A supervised approach typically uses clustering. An unsupervised approach uses clustering. An unsupervised approach knows how to label clusters of data. An unsupervised approach doesn't know what the clusters of data mean. Based on your understanding of the data, it's up to you to define them.
## Reinforcement learning
[Reinforcement learning](https://developers.google.com/machine-learning/glossary#reinforcement-learning-rl)
models make predictions by getting [rewards](https://developers.google.com/machine-learning/glossary#reward)
or penalties based on actions performed within an environment. A reinforcement
learning system generates a [policy](https://developers.google.com/machine-learning/glossary#policy) that
defines the best strategy for getting the most rewards.
Reinforcement learning is used to train robots to perform tasks, like walking
around a room, and software programs like
[AlphaGo](https://deepmind.com/research/case-studies/alphago-the-story-so-far) to play the game of Go.
## Generative AI
[Generative AI](https://developers.google.com/machine-learning/glossary#generative-ai) is a class of models
that creates content from user input. For example, generative AI can create
unique images, music compositions, and jokes; it can summarize articles,
explain how to perform a task, or edit a photo.
Generative AI can take a variety of inputs and create a variety of outputs, like
text, images, audio, and video. It can also take and create combinations of
these. For example, a model can take an image as input and create an image and
text as output, or take an image and text as input and create a video as output.
We can discuss generative models by their inputs and outputs, typically written
as "type of input"-to-"type of output." For example, the following is a partial
list of some inputs and outputs for generative models:
- Text-to-text
- Text-to-image
- Text-to-video
- Text-to-code
- Text-to-speech
- Image and text-to-image
The following table contains examples of generative models, their input, and
their possible output:
| Model | Input | Example output |
|---|---|---|
| Text-to-text | Who invented the Le Mans race? | The 24 Hours of Le Mans was founded by the Automobile Club de l'Ouest (ACO), a motoring organization founded in 1906. The first race was held on 26 and 27 May 1923 on the Sarthe Circuit, a 13.426-kilometre (8.345-mile) road circuit located near the town of Le Mans in France. The race was originally conceived as a way to promote the ACO and the Sarthe region. Source: [Bard](https://bard.google.com) |
| Text-to-image | An alien octopus floats through a portal while reading a newspaper. | :octopus: Source: [Imagen](https://imagen.research.google/) |
| Text-to-video | A photorealistic teddy bear is swimming in the ocean at San Francisco. The teddy bear goes under water. The teddy bear keeps swimming under the water with colorful fishes. A panda bear is swimming under water. |  Source: [Phenaki](https://phenaki.video/) |
| Text-to-code | Write a Python loop that loops over a list of numbers and prints the prime numbers. | ```python for number in numbers: # Check if the number is prime. is_prime = True for i in range(2, number): if number % i == 0: is_prime = False break # If the number is prime, print it. if is_prime: print(number) ``` Source: [Bard](https://bard.google.com) |
| Image-to-text |  | This is a flamingo. They are found in the Caribbean. Source: [Google DeepMind](https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model) |
How does generative AI work? At a high-level, generative models learn
patterns in data with the goal to produce new but similar data. Generative
models are like the following:
- Comedians who learn to imitate others by observing people's behaviors and style of speaking
- Artists who learn to paint in a particular style by studying lots of paintings in that style
- Cover bands that learn to sound like a specific music group by listening to lots of music by that group
To produce unique and creative outputs, generative models are initially trained
using an unsupervised approach, where the model learns to mimic the data it's
trained on. The model is sometimes trained further using supervised or
reinforcement learning on specific data related to tasks the model might be
asked to perform, for example, summarize an article or edit a photo.
Generative AI is a quickly evolving technology with new use cases constantly
being discovered. For example, generative models are helping businesses refine
their ecommerce product images by automatically removing distracting backgrounds
or improving the quality of low-resolution images.
> [!IMPORTANT]
> **Key Terms:**
>
>
>
> - [classification model](https://developers.google.com/machine-learning/glossary#classification-model)
> - [clustering](https://developers.google.com/machine-learning/glossary#clustering)
> - [model](https://developers.google.com/machine-learning/glossary#model)
> - [policy](https://developers.google.com/machine-learning/glossary#policy)
> - [prediction](https://developers.google.com/machine-learning/glossary#prediction)
> - [regression model](https://developers.google.com/machine-learning/glossary#regression-model)
> - [reinforcement learning](https://developers.google.com/machine-learning/glossary#reinforcement-learning-rl)
> - [reward](https://developers.google.com/machine-learning/glossary#reward)
> - [supervised learning](https://developers.google.com/machine-learning/glossary#supervised-machine-learning)
> - [training](https://developers.google.com/machine-learning/glossary#training)
> - [unsupervised learning](https://developers.google.com/machine-learning/glossary#unsupervised-machine-learning)
>
>
>