Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | 2021 | Simplilearn

Summary

This script introduces the concept of machine learning, explaining how it enables machines to learn from past data and make predictions. It distinguishes between supervised, unsupervised, and reinforcement learning, using relatable examples like coin identification, cricket data analysis, and movie recommendations. The script also touches on the importance of data availability, computational power, and memory handling in the modern era, highlighting applications in healthcare, sentiment analysis, fraud detection, and dynamic pricing models like Uber's surge pricing.

Takeaways

• 🤖 Machine learning enables machines to learn from past data and perform tasks at a speed unattainable by humans.
• 🧠 It involves not just learning, but also understanding and reasoning, similar to human cognitive processes.
• 🎵 The example of Paul's music preferences illustrates how machine learning can classify data based on past experiences.
• 📊 Machine learning algorithms, like k-nearest neighbors, can predict outcomes based on majority votes from similar data points.
• 📈 Supervised learning uses labeled data to train models, allowing them to predict outcomes based on known features and labels.
• 🔍 Unsupervised learning deals with unlabeled data, identifying patterns and clusters within the data on its own.
• 🎲 Reinforcement learning is a feedback-based learning method where the system learns from rewards or penalties.
• 📊 The flowchart of a machine learning model shows input data being processed to produce an output, with feedback loops for continuous learning.
• 🌐 The availability of vast amounts of data, increased computational power, and improved memory handling capabilities have made machine learning feasible.
• 🏥 Machine learning applications span various sectors, including healthcare, sentiment analysis, fraud detection, and e-commerce.
• 🚗 Real-world examples include surge pricing models used by companies like Uber for demand-based pricing and predictive modeling for efficient resource allocation.

Q & A

What is the fundamental concept of machine learning?

Machine learning is the process by which machines are trained to learn from past data and make predictions or decisions based on that data, similar to how humans learn from their experiences.

How does Paul decide whether he likes a song or not?

Paul decides based on the song's tempo and intensity, with a preference for fast tempo and soaring intensity.

What is the k-nearest neighbors algorithm mentioned in the script?

The k-nearest neighbors algorithm is a basic machine learning algorithm used for classification and regression. It predicts the likelihood of a target variable by considering its k closest data points in the feature space.

What are the three main types of machine learning?

The three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning.

How does supervised learning differ from unsupervised learning?

Supervised learning uses labeled data, where the machine learning model is trained on features and their associated labels. Unsupervised learning, on the other hand, deals with unlabeled data, where the model identifies patterns or clusters on its own.

What is an example of supervised learning mentioned in the script?

An example of supervised learning is predicting the currency of a coin based on its weight, where the weight is the feature and the currency is the label.

How does reinforcement learning work?

Reinforcement learning is a reward-based learning method where a machine learning model learns to make decisions by receiving feedback on its actions, improving its performance over time.

What technological advancements have made machine learning possible today?

The availability of massive amounts of data, increased memory handling capabilities of computers, and enhanced computational powers have made machine learning possible in the current era.

What are some applications of machine learning mentioned in the script?

Some applications include healthcare diagnostics, sentiment analysis on social media, fraud detection in finance, and predictive modeling for demand in services like Uber's surge pricing.

How does Uber use machine learning for surge pricing?

Uber uses machine learning to predict demand and adjust prices in real-time based on factors like the number of available cars, weather conditions, and rush hour, ensuring that those who need a cab can get one.

What is an everyday example of machine learning mentioned in the script?

An everyday example is Siri, an AI assistant that can set reminders based on user input, utilizing machine learning to understand and respond to natural language commands.

Outlines

00:00

🤖 Introduction to Machine Learning

This paragraph introduces the concept of machine learning, explaining how it allows machines to learn from past data and perform tasks similar to humans, but at a much faster pace. It uses the example of Paul, who likes or dislikes songs based on their tempo and intensity, to illustrate how machine learning can classify and predict preferences. The paragraph also touches on the different types of machine learning: supervised, unsupervised, and reinforcement learning, and provides a basic explanation of each.

05:02

📊 Understanding Supervised and Unsupervised Learning

This paragraph delves deeper into supervised and unsupervised learning. It explains supervised learning with the analogy of a coin weight classification task, where the machine learning model learns to associate weights with specific currencies. Unsupervised learning is introduced through the example of analyzing cricket player performance data to identify patterns and clusters, such as distinguishing between batsmen and bowlers. The paragraph also mentions reinforcement learning, which is based on feedback and reward, using the example of a system learning to correctly identify images of dogs after receiving feedback.

Keywords

💡Machine Learning

Machine learning is a subset of artificial intelligence that enables computers to learn from and make predictions or decisions based on data. In the video, it's described as a process where machines can learn from past data, similar to how humans learn from their experiences, but at a much faster pace. The example of Paul liking or disliking songs based on tempo and intensity illustrates how machine learning can classify and predict preferences.

💡Supervised Learning

Supervised learning is a type of machine learning where the model is trained on labeled data, meaning the input data is associated with the correct output. In the video, it's exemplified by predicting the currency of a coin based on its weight. The machine learning model learns the relationship between the feature (weight) and the label (currency), allowing it to make accurate predictions for new, unseen data.

💡Unsupervised Learning

Unsupervised learning involves analyzing data without any pre-existing labels. The goal is to find patterns or groupings in the data. In the context of the video, it's used to differentiate between batsmen and bowlers in a cricket dataset by identifying clusters of performance data. The machine learning model discovers the underlying structure of the data without explicit guidance on what the clusters represent.

💡Reinforcement Learning

Reinforcement learning is a learning paradigm where an agent learns to behave in an environment by performing actions and receiving rewards or penalties. The video uses the example of a system learning to identify images of dogs or cats based on feedback. If the system incorrectly identifies a dog as a cat, it receives negative feedback, which it uses to improve its future predictions.

💡K-Nearest Neighbors

K-Nearest Neighbors (KNN) is a simple, yet powerful, supervised learning algorithm used for classification and regression. In the video, KNN is used to predict whether Paul will like a song by considering the majority vote of similar songs' outcomes. It's a non-parametric method that does not make any assumptions about the underlying data distribution, making it versatile for various applications.

💡Data Analysis

Data analysis is the process of inspecting, cleaning, transforming, and modeling data to extract useful information, answer business questions, and support decision-making. The video emphasizes the importance of data analysis in machine learning, as it's the foundation for training models and making predictions. For instance, analyzing Paul's song preferences or the weight of coins in supervised learning scenarios.

💡Prediction Model

A prediction model is a mathematical model that is trained on historical data and used to forecast future outcomes. In the video, prediction models are created based on the data provided, such as the weight of coins or the features of songs, to predict unknown outcomes like the currency of a new coin or whether Paul will like a new song.

💡Accuracy

Accuracy in machine learning refers to the correctness of a model's predictions. It's a measure of how well the model performs on new, unseen data. The video mentions that the more data available, the better the model can learn, and thus, the higher the accuracy of its predictions. For example, a machine learning model predicting song preferences would become more accurate as it analyzes more of Paul's choices.

💡Feature

In machine learning, a feature is an individual measurable property or characteristic of a phenomenon being observed. Features are the inputs to the model. In the video, the weight of a coin is a feature used to predict its currency, and the tempo and intensity of a song are features used to predict Paul's preference.

💡Label

A label in machine learning is the correct output or response associated with a given input or feature. It's used during the training process to teach the model what each feature represents. In the video, the currency of a coin is the label that the model learns to associate with the feature of its weight.

💡Feedback

Feedback in the context of machine learning is the information provided to the model after it makes a prediction, indicating whether the prediction was correct or not. This is crucial for reinforcement learning, as the video describes, where the system learns to correctly identify images based on the feedback it receives after each prediction.

Highlights

Machine learning enables machines to learn from past data and perform tasks faster than humans.

Machine learning involves understanding, reasoning, and making predictions based on data.

Paul's preference for songs can be used as a simple example to illustrate machine learning concepts.

The K-nearest neighbors algorithm is a basic machine learning method for classification.

Supervised learning uses labeled data to train models, like predicting currency based on coin weights.

Unsupervised learning deals with unlabeled data, identifying patterns such as clustering players into batsmen and bowlers.

Reinforcement learning is a feedback-based learning method where the system learns from rewards or penalties.

Machine learning models learn from data, build prediction models, and improve accuracy with more data.

The availability of vast amounts of data has made machine learning more feasible in the modern era.

Increased memory and computational power of computers enable processing and analysis of large datasets.

Machine learning has numerous applications, including healthcare diagnostics, sentiment analysis, and fraud detection.

Uber uses machine learning for surge pricing and predictive modeling to manage demand and supply.

Machine learning models can adapt and improve over time with continuous learning and feedback.

The transcript provides a comprehensive introduction to the basics of machine learning, suitable for beginners.

The transcript includes a quiz to test understanding of supervised and unsupervised learning scenarios.

The transcript encourages viewers to share everyday examples of machine learning in action.

Transcripts

we know humans learn from their past

experiences

and machines follow instructions given

by humans

but what if humans can train the

machines to learn from the past data and

do what humans can do and much faster

well that's called machine learning but

it's a lot more than just learning it's

also about understanding and reasoning

so today we will learn about the basics

of machine learning

so that's paul he loves listening to new

songs

he either likes them or dislikes them

paul decides this on the basis of the

song's tempo

genre

intensity and the gender of voice for

simplicity let's just use tempo and

intensity for now so here tempo is on

the x axis ranging from relaxed to fast

whereas intensity is on the y axis

ranging from light to soaring we see

that paul likes the song with fast tempo

and soaring intensity while he dislikes

the song with relaxed tempo and light

intensity so now we know paul's choices

let's say paul listens to a new song

let's name it as song a song a has fast

tempo and a soaring intensity so it lies

somewhere here looking at the data can

you guess whether paul will like the

song or not correct so paul likes this

song by looking at paul's past choices

we were able to classify the unknown

song very easily right let's say now

paul listens to a new song let's label

it as song b so song b

lies somewhere here with medium tempo

and medium intensity neither relaxed nor

fast neither light nor soaring now can

you guess whether paul likes it or not

not able to guess whether paul will like

it or dislike it are the choices unclear

correct we could easily classify song a

but when the choice became complicated

as in the case of song b yes and that's

where machine learning comes in let's

see how in the same example for song b

if we draw a circle around the song b we

see that there are four votes for like

whereas one would for dislike if we go

for the majority votes we can say that

paul will definitely like the song

that's all this was a basic machine

learning algorithm also it's called k

nearest neighbors so this is just a

small example in one of the many machine

learning algorithms quite easy right

believe me it is but what happens when

the choices become complicated as in the

case of song b that's when machine

learning comes in it learns the data

builds the prediction model and when the

new data point comes in it can easily

predict for it more the data better the

model higher will be the accuracy there

are many ways in which the machine

learns it could be either supervised

learning unsupervised learning or

reinforcement learning let's first

quickly understand supervised learning

suppose your friend gives you one

million coins of three different

currencies say one rupee one euro and

one dirham each coin has different

weights for example a coin of one rupee

weighs three grams one euro weighs seven

grams and one dirham weighs four grams

your model will predict the currency of

the coin here your weight becomes the

feature of coins while currency becomes

the label when you feed this data to the

machine learning model it learns which

feature is associated with which label

for example it will learn that if a coin

is of 3 grams it will be a 1 rupee coin

let's give a new coin to the machine on

the basis of the weight of the new coin

your model will predict the currency

hence supervised learning uses labeled

data to train the model here the machine

knew the features of the object and also

the labels associated with those

features on this note let's move to

unsupervised learning and see the

difference suppose you have cricket data

set of various players with their

respective scores and wickets taken when

you feed this data set to the machine

the machine identifies the pattern of

player performance so it plots this data

with the respective wickets on the

x-axis while runs on the y-axis while

looking at the data you'll clearly see

that there are two clusters the one

cluster are the players who scored

higher runs and took less wickets while

the other cluster is of the players who

scored less runs but took many wickets

so here we interpret these two clusters

as batsmen and bowlers the important

point to note here is that there were no

labels of batsmen and bowlers hence the

learning with unlabeled data is

unsupervised learning so we saw

supervised learning where the data was

labeled and the unsupervised learning

where the data was unlabeled and then

there is reinforcement learning which is

a reward based learning or we can say

that it works on the principle of

feedback here let's say you provide the

system with an image of a dog and ask it

to identify it the system identifies it

as a cat so you give a negative feedback

to the machine saying that it's a dog's

image the machine will learn from the

feedback and finally if it comes across

any other image of a dog it will be able

to classify it correctly that is

reinforcement learning to generalize

machine learning model let's see a

flowchart input is given to a machine

learning model which then gives the

output according to the algorithm

applied if it's right we take the output

as a final result else we provide

feedback to the training model and ask

it to predict until it learns i hope

you've understood supervised and

unsupervised learning so let's have a

quick quiz you have to determine whether

the given scenarios uses supervised or

unsupervised learning simple right

scenario one facebook recognizes your

friend in a picture from an album of

tagged photographs

scenario 2 netflix recommends new movies

based on someone's past movie choices

scenario 3 analyzing bank data for

suspicious transactions and flagging the

fraud transactions think wisely and

comment below your answers moving on

don't you sometimes wonder how is

machine learning possible in today's era

well that's because today we have

humongous data available everybody is

online either making a transaction or

just surfing the internet and that's

generating a huge amount of data every

minute and that data my friend is the

key to analysis also the memory handling

capabilities of computers have largely

increased which helps them to process

such huge amount of data at hand without

any delay and yes computers now have

great computational powers so there are

a lot of applications of machine

learning out there to name a few machine

learning is used in healthcare where

diagnostics are predicted for doctor's

review the sentiment analysis that the

tech giants are doing on social media is

another interesting application of

machine learning fraud detection in the

finance sector and also to predict

customer churn in the e-commerce sector

while booking a gap you must have

encountered surge pricing often where it

says the fair of your trip has been

updated continue booking yes please i'm

getting late for office

well that's an interesting machine

learning model which is used by global

taxi giant uber and others where they

have differential pricing in real time

based on demand the number of cars

available bad weather rush r etc so they

use the surge pricing model to ensure

that those who need a cab can get one

also it uses predictive modeling to

predict where the demand will be high

with the goal that drivers can take care

of the demand and search pricing can be

minimized great hey siri can you remind

me to book a cab at 6 pm today ok i'll

remind you

thanks no problem comment below some

interesting everyday examples around you

where machines are learning and doing

amazing jobs so that's all for machine

learning basics today from my site keep

watching this space for more interesting

videos until then happy learning