What is Machine Learning? | 100 Days of Machine Learning

Summary

In this introductory video, the YouTuber announces the launch of a '100 Days of Machine Learning' playlist. Aiming to fill the gap of an end-to-end learning resource, the series promises daily uploads for the next 100 days. The curriculum will cover the machine learning lifecycle, from basics to advanced concepts like bias-variance tradeoff, without delving into specific algorithms. Targeted at beginners to intermediate learners, the series will guide viewers through the essentials of machine learning projects, highlighting the importance and applications of ML in various scenarios, and touching on its growing industry relevance and job prospects.

Takeaways

• 🎥 The video is an announcement for a new '100 days of Machine Learning' playlist on the YouTube channel.
• 📚 The creator aims to provide a comprehensive curriculum on intermediate-level machine learning through daily videos over 100 days.
• 🔍 The playlist will focus on the 'Machine Learning Life Cycle' and the complete flow of a machine learning project, not just algorithms.
• 🚀 The creator intends to cover all essential topics, including challenges and techniques in machine learning projects, to take beginners to a proficient level.
• 📈 The playlist is designed to differentiate between ordinary and extraordinary machine learning engineers by covering advanced concepts like Bias-Variance Trade Off.
• 🔗 There is a separate playlist for machine learning algorithms for those who wish to delve into specific algorithms.
• 💡 The creator is open to suggestions for additional topics to include in the '100 days of Machine Learning' playlist.
• 👨‍🏫 The playlist is intended for both beginners and intermediate learners, offering a valuable resource for those looking to enhance their understanding of machine learning.
• 📈 Machine learning is becoming increasingly important due to its ability to handle complex scenarios where traditional programming falls short, such as email spam classification and image recognition.
• 💼 The demand for machine learning skills is high, and the creator predicts that as the field grows, the salaries will normalize as more professionals enter the market.
• 🌟 The video concludes with a teaser for the next video in the series, which will discuss the differences between AI, ML, and DL.

Q & A

What is the purpose of the new '100 days of Machine Learning' playlist?

The purpose of the '100 days of Machine Learning' playlist is to provide a comprehensive and structured curriculum on Machine Learning that covers the entire flow of Machine Learning, from basics to advanced topics, with the aim to teach intermediate-level machine learning and help beginners advance to a proficient level.

Why was there a need for an end-to-end Machine Learning playlist on the channel?

There was a need for an end-to-end Machine Learning playlist because many viewers expressed difficulty in finding such a resource on the channel. Although there were videos on various Machine Learning algorithms, a complete and structured playlist was missing.

What are the two essential aspects of Machine Learning that the creator believes beginners often overlook?

The two essential aspects that beginners often overlook are learning the algorithms, which is mandatory, and understanding how to develop an end-to-end Machine Learning project, including the complete flow, also known as the 'Machine Learning Life Cycle' or 'Product Life Cycle'.

What topics will be covered in the '100 days of Machine Learning' playlist?

The playlist will cover the basics of Machine Learning and the entire flow of a Machine Learning project, including potential challenges, techniques, deployment, imputation, pre-processing, analysis, model selection, feature selection, and important concepts like Bias-Variance Trade Off.

Why is the playlist not covering Machine Learning algorithms?

The playlist is not covering Machine Learning algorithms because there is already a separate playlist on the channel dedicated to algorithms. The '100 days of Machine Learning' playlist focuses on the techniques, flow, deployment, and other important concepts in Machine Learning.

Who is the intended audience for the '100 days of Machine Learning' playlist?

The intended audience for the playlist includes beginners who are starting their Machine Learning journey and intermediate learners who want to gain a deeper understanding of familiar topics or learn anything they might have missed.

What is the significance of the 'Machine Learning Life Cycle' or 'Product Life Cycle' in the context of the playlist?

The 'Machine Learning Life Cycle' or 'Product Life Cycle' is significant because it represents the complete flow of developing a Machine Learning project, from start to finish. The playlist aims to educate viewers on this entire process, which is crucial for understanding how to build end-to-end Machine Learning projects.

How does the speaker plan to make the '100 days of Machine Learning' playlist meaningful for the viewers?

The speaker plans to make the playlist meaningful by covering a wide range of topics in Machine Learning, ensuring no topic is left untouched, and by being honest and putting maximum effort into the creation of each video. Additionally, the speaker is open to suggestions from viewers for specific topics they want to be covered.

What is the difference between traditional programming and Machine Learning as explained in the script?

Traditional programming involves writing code for each specific scenario, whereas Machine Learning involves providing data and an algorithm that explores the data to identify patterns between input and output. Machine Learning algorithms generate logic automatically, without the need for explicit programming for each condition or case.

Can you provide an example from the script that illustrates the power of Machine Learning over traditional programming?

An example provided in the script is the task of building an e-mail spam classifier. Traditional programming would require writing specific conditions for what constitutes spam, which can become outdated as spammers change their tactics. Machine Learning, on the other hand, learns from the data and automatically adjusts to new patterns, making it more effective over time.

What are some scenarios where Machine Learning is more useful than traditional software development, according to the script?

The script mentions three scenarios where Machine Learning is more useful: 1) When you can't write cases for everything, such as in spam classification. 2) When the number of cases is unimaginable, like in image classification for detecting dogs. 3) In Data Mining, where hidden patterns need to be extracted from data that cannot be discovered through traditional data analysis methods.

Outlines

00:00

🎥 Launching '100 Days of Machine Learning' Playlist

The YouTuber introduces a new initiative called '100 Days of Machine Learning', aimed at creating a comprehensive learning resource for machine learning. The channel previously lacked a complete end-to-end machine learning guide, which the creator intends to address with this new playlist. The plan is to upload one educational video daily for 100 days, covering intermediate-level machine learning topics and providing a structured curriculum. The content will not repeat the algorithms already available in a separate playlist but will instead focus on the 'Machine Learning Life Cycle', including project development, challenges, and essential techniques like imputation, pre-processing, analysis, model and feature selection, as well as advanced concepts like the Bias-Variance Trade Off. The creator invites suggestions for specific topics and positions the playlist as a valuable resource for both beginners and intermediate learners, promising to cover a wide range of topics in machine learning.

05:02

🤖 Understanding Machine Learning and Its Applications

This paragraph delves into the formal definition of machine learning, highlighting its distinction from traditional programming through the concept of 'explicit programming'. The video explains how machine learning algorithms learn from data to identify patterns and make predictions without being explicitly programmed for each scenario. It contrasts this with conventional programming, where code is written for specific cases. The YouTuber uses the example of a program for adding two numbers versus a machine learning model that can generalize the addition operation after being trained on a dataset. The video also discusses scenarios where machine learning is particularly useful, such as creating an email spam classifier, where the number of possible conditions is too vast to code manually. It emphasizes the adaptability of machine learning algorithms to changing data and their power in industries, setting the stage for further exploration of machine learning's practical applications.

10:02

📈 Machine Learning's Versatility and Historical Growth

The script discusses the versatility of machine learning in scenarios like image classification and data mining, where the complexity and volume of data make traditional programming approaches impractical. It explains how machine learning algorithms can learn to recognize patterns in vast datasets, such as distinguishing between different dog breeds in images or identifying spam emails through hidden patterns. The paragraph also touches on the history of machine learning, comparing its growth to that of actor Nawazuddin Siddiqui, highlighting its long-standing presence and recent rise to prominence. The discussion points to the significant role of data availability and advancements in hardware in fueling the growth of machine learning, suggesting a continued upward trend in the field's importance and job opportunities.

15:03

🚀 The Future of Machine Learning and Career Opportunities

The final paragraph addresses the future of machine learning, emphasizing the current upward trajectory of the field and the associated job market. It draws a parallel with the early days of Java, explaining how the scarcity of machine learning professionals leads to higher salaries, but predicts that this will normalize as more people learn the skills. The video encourages viewers to learn machine learning now to capitalize on the current growth phase and the opportunities it presents. The YouTuber wraps up the introduction by thanking viewers for their time and inviting them to subscribe for the upcoming series, promising to cover the differences between AI, ML, and DL in the next video.

Keywords

💡Machine Learning

Machine Learning is a field of computer science that focuses on the development of algorithms and statistical models that enable computers to learn from and make decisions based on data. It is central to the video's theme as the creator announces the launch of a new playlist dedicated to teaching intermediate-level machine learning over 100 days. The script discusses machine learning's ability to identify patterns and make predictions without explicit programming, highlighting its power and utility in various applications such as email spam classification and image recognition.

💡Playlist

In the context of YouTube, a Playlist is a collection of videos compiled by a user or creator, organized around a specific theme or purpose. The video's creator mentions the creation of a new playlist titled '100 days of Machine Learning,' which is the main subject of the announcement. This playlist aims to guide viewers through a structured curriculum on machine learning, indicating the educational intent and structure of the content to be provided.

💡End-to-end Machine Learning

End-to-end Machine Learning refers to the complete process of building a machine learning system, from data collection and preprocessing to model training, evaluation, and deployment. The script emphasizes the importance of understanding this full cycle, especially for beginners, and the creator's intention to cover the entire flow of machine learning in the new playlist, thereby providing a comprehensive learning experience.

💡Machine Learning Algorithms

Machine Learning Algorithms are the computational processes that enable machine learning models to learn from data. The script mentions that while the creator has previously made videos on various machine learning algorithms, the new playlist will not delve into algorithms specifically. Instead, it will focus on the broader aspects of machine learning, such as project development and deployment, which are essential for a well-rounded understanding of the field.

💡Bias-Variance Trade Off

Bias-Variance Trade Off is a fundamental concept in machine learning that refers to the compromise between the accuracy and complexity of a model. A model with high bias may oversimplify the data, while one with high variance may be overly sensitive to small fluctuations. The script mentions this concept as an important topic that differentiates ordinary from extraordinary machine learning engineers, indicating its significance in the development of robust machine learning models.

💡Pre-processing

Pre-processing is a crucial step in machine learning where the raw data is cleaned, normalized, and transformed to make it suitable for model training. The script indicates that the new playlist will cover pre-processing techniques, which are essential for preparing data to ensure that machine learning models perform optimally.

💡Feature Selection

Feature Selection is the process of selecting the most relevant features or variables from the data to be used in model training. It helps to improve model performance and reduce complexity. The script mentions feature selection as one of the important concepts that will be covered in the playlist, highlighting its role in creating effective machine learning models.

💡Data Mining

Data Mining is the process of extracting valuable insights or patterns from large datasets. In the script, data mining is presented as a significant application of machine learning, where algorithms are applied to discover hidden patterns and extract useful information that is not easily discernible through traditional data analysis methods.

💡Machine Learning Lifecycle

The Machine Learning Lifecycle, also known as the Product Lifecycle, refers to the stages a machine learning project goes through from conception to deployment and maintenance. The script emphasizes the importance of understanding this lifecycle for beginners, as it encompasses all aspects of a machine learning project, from data gathering to model deployment and evaluation.

💡Explicit Programming

Explicit Programming is the traditional approach to software development where programmers write specific instructions for each task the program is expected to perform. The script contrasts explicit programming with machine learning, where algorithms learn from data and adapt without being explicitly programmed for each scenario, demonstrating the adaptability and efficiency of machine learning approaches.

💡Imputation

Imputation is a technique used in data pre-processing to deal with missing data by filling in the gaps with substitute values. The script mentions imputation as one of the topics to be covered in the playlist, indicating its importance in preparing datasets for machine learning models, as handling missing data is a common challenge in real-world data sets.

Highlights

Introduction of a new '100 days of Machine Learning' playlist on the YouTube channel.

The playlist aims to provide an end-to-end machine learning course.

The creator will upload one video every day for 100 days, covering a structured curriculum on intermediate machine learning.

The course is designed for beginners and those with some experience, aiming to advance them to a proficient level in machine learning.

The playlist will not cover machine learning algorithms, as they already have a separate playlist for that.

Focus will be on the 'Machine Learning Life Cycle' and developing end-to-end machine learning projects.

Topics will include imputation, pre-processing, analysis, model selection, feature selection, and understanding bias-variance trade off.

The curriculum is still being developed and will be shared in the coming days.

Viewers can suggest topics they want to be covered in the playlist.

The playlist is intended for everyone, including students and professionals, and will be a valuable resource for those who may have missed certain topics.

The first topic of the series will be 'What is Machine Learning?' starting from scratch with a formal definition.

Machine learning is distinguished from explicit programming by its ability to learn from data and identify patterns to make decisions.

Examples given to illustrate the power of machine learning in scenarios where traditional programming falls short, such as email spam classification.

Machine learning's utility in image classification and data mining where the number of potential cases is immense.

A brief history of machine learning, comparing its growth to the career of actor Nawazuddin Siddiqui, highlighting its rise to prominence in the 2010s.

The impact of internet, smartphones, data generation, and advancements in hardware on the growth of machine learning.

Economic principles applied to the job market and salaries in the machine learning industry, and predictions for the future.

Encouragement for viewers to learn machine learning while it's on an upward trajectory for potential success.

Upcoming video in the series will cover the difference between AI, ML, and DL.

Transcripts

Hey Guys,

Welcome to my YouTube Channel

In this video,

I am going to make an announcement.

that, I'm going to create a new Playlist

for my YouTube Channel.

and I'm going to call that Playlist

"100 days of Machine Learning"

So...

You might be thinking

Sounds interesting

But what is it exactly?

Here's the thing

In the past few days,

Many of you messaged me..

over WhatsApp or through Youtube comments regarding

They weren't able to find..

a end-to-end machine learning playlist on my channel.

and that is true actually.

If you go to my channel,

you would find videos related to Machine Learning algorithms

I've created videos on various machine learning algorithms.

But a complete end-to-end playlist isn't available in my channel.

So,I thought why not create one comprehensive Playlist on Machine Learning?

So, that's the plan.

In the coming 100 days,

I have planned to shoot and upload one video every day.

I've developed a structured curriculum

To the best of my experience and knowledge,

I think this Playlist will be more than sufficient to teach you..

Intermediate-level machine learning.

For advanced-level machine learning, it's completely in your hands.

but if you're a beginner or slightly more experienced,

If you follow the course throughout,

I hope you'll advance to a proficient level in Machine Learning.

Now comes the question about

What are the topics we'll be covering?

So, I will tell you something about myself

When I began learning machine learning,

my primary focus was on mastering ML algorithms.

Gradually, I realized that after learning the algorithms

and working on few projects,

there are two essential aspects to focus on in Machine Learning

One of them is learning algorithms

knowing about algorithms is mandatory.

But along with that,

Simultaneously, it's crucial to understand how to develop an end-to-end machine learning project,

including the complete flow.

We call this as "Machine Learning Life Cycle"

also known as the "Product Life Cycle".

and beginners usually doesn't focus much on this aspect.

Beginners often believe that knowing Machine Learning algorithms alone is sufficient.

but not really.

So, I planned in the following 100 days..

I will start with the basics of Machine Learning

and I'll cover the entire flow of Machine Learning.

If you want to do a Machine Learning project,

I'll cover all the potential challenges you might encounter while working on an Machine Learning project.

Since we're covering 100 topics in 100 days,

no topic will be left untouched.

Keep in mind, we won't be delving into algorithms in this Playlist.

We are not going to cover any of the algorithms.

For algorithms, we already have a separate playlist in our channel.

If you want to learn about any algorithms,

then you can go to the playlist of that particular algorithm.

But in this "100 days of Machine Learning",

Here, we learn the techniques

we learn the flow

we learn about how we deploy.

We'll learn how to perform imputation,

how to perform pre-processing,

how to perform analysis,

model selection, feature selection,

and such important concepts like

What is Bias-Variance Trade Off?

and the weighted and important topics

that differentiates ordinary Machine Learning engineers

from extraordinary Machine Learning engineers.

We'll cover such topics here.

I am still working on the curriculum

it is not completed yet.

I will be uploading that curriculum as well in the coming few days.

In fact, if want any certain topic to be covered,

you can share that with me

and I'll be covering that topic as well.

But I'm thinking in this 100 days,

I have to create a resource that is meaningful for you

and your Machine Learning journey.

and the other question which may pop out in your mind

For whom is this playlist intended?

If you are a beginner, then it is definitely for you.

and even if you know intermediate machine learning,

it could be a great resource for you

as you can come here

and use this as a valuable resource

to watch and learn anything that you could've missed,

or gain a deeper understanding of familiar topics with clarity.

This will be helpful for you in such cases.

In short, this will benefit everyone following the channel,

including students and professionals.

I'll try to be very honest while making these videos

I'll try to put my maximum effort

So, yeah..

This is the announcement I was planning to make.

Now, I am not going waste any time

I will start it today itself.

Today we are going to cover the first topic

which is "What is Machine Learning?"

I know that many of you already knew it.

However, I want to begin everything from scratch.

So let's cover this topic: What is Machine Learning?

So, let's dive into what machine learning is

We'll start with the formal definition

According to definition,

Machine learning is a field of computer science

that uses statistical techniques to give computer systems

the ability to "learn" with data, without being explicitly programmed.

To put it in simpler terms,

Machine Learning is all about learning from data.

There is it a term in this definition called as "Explicit Programming"

Let's explore the concept of explicit programming.

Explicit programming involves writing code for each specific scenario.

To handle that scenario, you write a code.

But in Machine Learning, you don't do that.

what you do is that you've got some data

and you've got an algorithm

You instruct the algorithm to explore the data

and identify patterns between input and output.

Once you've identified the patterns,

we provide new input to the algorithm to derive the output.

If you check out this flow diagram,

you can observe that in conventional programming approach

we write a program

for which logic is written by us.

If you give input to that program, you'll get your output.

But in Machine Learning, things are different.

what you do is you provide some data.

In that data, you give an input as well as an output

But you haven't written any program or logic.

That logic is generated by none other than the Machine Learning algorithm.

The good part is that you don't have to write code for each condition/case.

It is automatically handled by Machine Learning algorithm.

For example, you've written code for adding two numbers

Whenever you give two numbers to that program,

it returns you the value of sum

But in Machine Learning, what you will do is you give data

You'll give an excel file in which the rows contain the numbers and their respective sum.

Whenever Machine learning models train on that data,

The model realises the pattern as addition.

After training, irrespective of giving two or four or ten numbers as an input,

your machine learning model knows that it has to perform addition.

It adds all of them and gives to you.

Whereas in the code written for sum of two numbers,

If you give more than two numbers as input, then the program doesn't function

since it is explicitly coded to perform sum of two numbers.

That's the key difference

I hope from the example

you can understand the reason behind the powerful nature of machine learning in the industry.

Now that we know about Machine Learning,

Let's also discuss when and where Machine Learning is used

Let us know in which type of scenarios, Machine Learning can be used

and is useful than traditional software development

I'll provide you three scenarios

and there are other scenarios as well.

These three scenarios feel significant to me.

First scenario is that you can't perform few things using programming

You can't write cases for everything

In such situations, we use Machine Learning

I will give you one real life scenario

Consider that you are trying to build an e-mail spam classifier

to detect whether the given email is spam or not.

If you were given to write a program for that as a software developer,

Then what would you possibly do?

you will pick a bunch of e-mails

and you'll have the information about that e-mail, whether is spam or not

then you will try to create patterns for them.

like any word such as 'discount' or 'sale' or 'awesome' is repeated more often

or it is filled with bunch of pictures,

then you create a long if-else ladder by using if else for each and every condition

That would be the possible program for you Spam classifier.

but let us say that you've written something like

if 'huge' is repeated more than three times,

huge as in discount is used,

you'll label that e-mail to be spam.

and somehow advertising companies got to know that

if code is written to classify the mail as spam if the world is repeated more than three times

then those companies might use 'big' or 'massive' instead of 'huge'

then the program couldn't pick that mail as spam.

then you've to modify the logic of the code

to handle a new scenario

but again, advertising companies can experiment with different words if they get to know about it

In short, you've to keep changing the logic more frequently

to make sure that the code runs effectively.

In machine learning, it doesn't happen that way

since it learns from data, if data changes, then it will automatically get reflected in logic

That's the beauty of 'Machine Learning'

You just have to write just one algorithm,

and everything will be handled by that algorithm itself

and second scenario where Machine Learning is useful over traditional programming

A scenario where you cannot even imagine the number of cases

like 'Image Classification'

Let us say you want to classify dogs, that if dog is present in picture or not

There will be hundreds of breeds which varies in looks,

few large and few short,

vary in colours and other characteristics.

So, if you were to create a program that detects the presence of dog in a picture

Can you imagine the number of cases you've to write inorder to cover characteristics of every breed?

You cannot do so.

You cannot code it

We have use the technique that we, humans, use to identify dogs

We were thought from our childhood to identify that particular animal is dog

That one is not a dog, that is a cat

Our mind mentally keeps tagging the name with the animal

It keeps learning from the data

It is also one of the scenarios where you cannot use conventional software development approach

You'll have to use the Machine Learning approach

One more important use case is Data Mining.

What exactly is Data Mining?

First, let's discuss about what data analysis is.

Data analysis is a process where you extract patterns or search for hidden information

by plotting graphs

That is 'Data Analysis'

but sometimes the information is more hidden which you won't able to get through graphs

I'll give you one scenario

Just by looking at the e-mail content,

if we're unable to detect key words due to which we can the e-mail as spam,

then you perform Data Mining.

In Data Mining, you

apply Machine Learning algorithm on the data

you create a prediction model

just like e-mail spam classifier

you can check the patterns extracted by the Machine Learning model

like if 'huge' is occurring more frequently, then there it is labeled as a spam

if it is not that frequent, it is treated as not spam

After applying Machine Learning,

If you are able to extract important data from the information,

That is known as "Data Mining"

Most of the times in order to extract the hidden patterns,

in order to perform such data analysis, we use Machine Learning

and this is called as Data Mining

Machine learning is a very important tool to perform data mining

You should have understood the importance of machine learning using these 3 to 4 scenarios

And why is it taking over the world

Next you should be knowing a little history about Machine Learning

You should be learning about it's history if you are starting on any new technology

I feel that history of machine learning is more or less like Nawazuddin Siddiqui

I hope you all know about him, he is a great actor

Just like Nawazuddin Siddiqui, machine learning is already existing from a long time in the industry

He was playing a very small role in Munna Bhai MBBS

Similarly, machine learning is there from 40 to 50 years

But it couldn't get into limelight like other important technologies

Until the recent 2010s

Only from then,it raised to the level that machine learning is today

If you talk about Nawazuddin Siddiqui, what could be the success reason behind him?

It might be the OTTs or the audience preferring the content-based films

So there is a paradigm shift which led him to be one of the biggest actors of the country.

Similar events occurred in the case of machine learning also

All the theory and Maths existed from very long time

but machine learning is not that famous

because of the reason that machine learning requires significant amount of data

Unfortunately,back then gathering and labelling the data is a quite tedious task

Also, the inefficiency of hardware to run algorithms on such data back then

After 2010, with evolution of internet and smartphone,

These two problems are sorted out.

We are generating data at a heavy pace,

You can consider the example of your own life from morning to evening before going to bed

So much of data is generated by yourself alone

Then imagine the data generated by 4 billion Internet users around the globe

In fact, the amount of digital data created from the starting of mankind till 2015

is generated in 2016 alone

That is the speed at which we are generating data

this data is aiding the growth of machine learning

Second thing is hardware

In the modern day world, we ourselves are carrying up to 12GB of RAM in our pocket in the form of mobile

We are carrying GPUs in our pocket

which were not available to research scientists.

Even 128MB RAM was a big deal then

but we are equipped with good hardware, data and algorithms now

That is the reason why machine learning is enjoying it's fruits

This is not going to stop any time soon

The growth curve will keep on growing exponentially

and that is the reason for jobs in this sector

If we talk about jobs,

I will also have to discuss about few things

Do you think the jobs that are available now, the salaries which we get now in this industry

Will they continue to be same in the future?

The answer is NO

This is just pure economics

When Java entered the market, only a handful of people were familiar with the language.

But companies needed Java because their competitors were implementing it

Therefore, as a company, I also need Java in my software

For that, I have to hire some professionals

But when I reach out to market for hiring process, I realised that there is a lack of talent in this space

So all the companies will be fighting for those few available professionals

So that would obviously lead to more salary of that professional

SIMPLE ECONOMICS

The similar trend is now going on with machine learning

Even in the colleges, machine learning is not being taught

and many of the engineers doesn't know machine learning as of now, which is changing gradually

Every time when a company goes to a college, they only find less number of students who know Machine Learning

So they have to fight for those students resulting in a higher salary

Over time, as salaries increased, more people became interested in learning the technology to secure those jobs.

When everyone in the market learns machine learning in the coming few years

Majority of the population would be knowing machine learning just like Java today

Once most of the people knows machine learning, the salaries would be automatically normalised

as companies will have a lot more options

and that's the reason why they won't be paying high salaries that they're paying today

The good part is that we are at an initial growing phase

The graph of any technology will be like this,

initially increasing and then decreasing

The positive aspect is that for those learning machine learning, we are on an upward trajectory.

and there is still time which I feel

If we learn correctly now,

we can expect to achieve the same level of success that machine learning is currently experiencing.

I understand it's been a lengthy video,

but this was the introduction I wanted to provide on machine learning.

In the next video, we'll delve into an essential topic that often raises doubts among beginners.

What is the difference between AI, ML and DL?

We will be covering this on the next day, which is Monday.

That's a wrap for this video. I hope you like it.

If you plan to follow this series, '100 Days of ML,' please consider subscribing to the channel.

Thank you for watching!