AI vs Machine Learning vs Deep Learning | Machine Learning Training with Python | Edureka

Hello, everyone. This is Atul from Edureka and welcome to today’s topic
of discussion on AI vs Machine Learning
vs Deep Learning. These are the term
which have confused a lot of people and if you
too are one among them, let me resolve it for you. Well artificial intelligence
is a broader umbrella under which machine learning and deep learning come you
can also see in the diagram that even deep learning is
a subset of machine learning so you can say that all three of them
the AI the machine learning and deep learning are just
the subset of each other. So let’s move on and understand how exactly the differ
from each other. So let’s start
with artificial intelligence. The term artificial intelligence was first coined
in the year 1956. The concept is pretty old, but it has gained
its popularity recently. But why well, the reason is earlier we had
very small amount of data the data we had Was not enough
to predict the accurate result, but now there’s a tremendous
increase in the amount of data statistics suggest that by 2020 the accumulated
volume of data will increase from 4.4 zettabyte stew
roughly around 44 zettabytes or 44 trillion GBs of data along with such
enormous amount of data. Now, we have more
advanced algorithm and high-end computing
power and storage that can deal with such large
amount of data as a result. It is expected that 70% of Enterprise
will Implement ai over the next 12 months which is up from 40 percent
in 2016 and 51 percent in 2017. Just for your understanding
what does AI well, it’s nothing but a technique that enables the machine to act
like humans by replicating the behavior and nature
with AI it is possible for machine to learn
from the experience. The machines are just
the responses based on new input there
by performing human-like tasks. Artificial intelligence can
be trained to accomplish specific tasks by processing
large amount of data and recognizing pattern in them. You can consider that building an artificial
intelligence is like Building a Church, the first church
took generations to finish. So most of the workers were working in it never saw
the final outcome those working on it took pride
in their craft building bricks and chiseling stone that was going to be placed
into the great structure. So as AI researchers, we should think of ourselves
as humble brick makers whose job is to study how to build components
example Parts is planners or learning algorithm
or accept anything that someday someone
and somewhere will integrate into the intelligent systems
some of the examples of artificial intelligence
from our day-to-day life our Apple series just playing
computer Tesla self-driving car and many more these examples
are based on deep learning and natural language processing. Well, this was about what is AI
and how it gains its hype. So moving on ahead. Let’s discuss about machine
learning and see what it is and white pros of an introduced. Well Machine learning came into existence in the late 80s
and the early 90s, but what were the issues
with the people which made the machine learning
come into existence? Let us discuss them one by one
in the field of Statistics. The problem was how to efficiently train
large complex model in the field of computer science
and artificial intelligence. The problem was how to train
more robust version of AI system while in the case of Neuroscience problem
faced by the researchers was how to design operation
model of the brain. So these are some of the issues which had the largest influence
and led to the existence of the machine learning. Now this machine learning
shifted its focus from the symbolic approaches. It had inherited
from the AI and move towards the methods and model. It had borrowed from statistics
and probability Theory. So let’s proceed and see what exactly is
machine learning. Well Machine learning
is a subset of AI which The computer to act and make data-driven decisions
to carry out a certain task. These programs are algorithms
are designed in a way that they can learn
and improve over time when exposed to new data. Let’s see an example
of machine learning. Let’s say you want
to create a system which tells the expected weight
of a person based on its side. The first thing you do
is you collect the data. Let’s see there is how your data looks
like now each point on the graph represent
one data point to start with we can draw a simple line to predict the weight
based on the height. For example, a simple line W equal x minus hundred
where W is waiting kgs and edges hide and centimeter this line can
help us to make the prediction. Our main goal is
to reduce the difference between the estimated value
and the actual value. So in order to achieve it we
try to draw a straight line that fits through all
these different points and minimize the error. So our main goal is
to minimize the error and make them as small as
possible decreasing the error or the difference
between In the actual value and estimated value
increases the performance of the model further
on the more data points. We collect the better. Our model will become we can also improve our model
by adding more variables and creating different
production lines for them. Once the line is created. So from the next time
if we feed a new data, for example height
of a person to the model, it would easily predict the data
for you and it will tell you what has predicted
weight could be. I hope you got
a clear understanding of machine learning. So moving on ahead. Let’s learn about deep learning. Now what is deep learning? You can consider deep learning
model as a rocket engine and its fuel is
its huge amount of data that we feed to
these algorithms the concept of deep learning is not new, but recently it’s hype as increase and deep learning
is getting more attention. This field is a particular kind
of machine learning that is inspired by the functionality of
our brain cells called neurons which led to the concept
of artificial neural network. It simply takes the data connection between all
the artificial neurons and adjust them according
to the data pattern more neurons are added at the size of the data is large
it automatically features learning at multiple
levels of abstraction. Thereby allowing a system to learn complex function
mapping without depending on any specific algorithm. You know, what no one actually
knows what happens inside a neural network
and why it works so well, so currently you can call
it as a black box. Let us discuss some
of the example of deep learning and understand it
in a better way. Let me start with a simple
example and explain you how things happen
at a conceptual level. Let us try and understand how you recognize a square
from other shapes. The first thing
you do is you check whether there are four lines
associated with a figure or not simple concept, right? If yes, we further check if they are connected
and closed again a few years. We finally check whether it is perpendicular
and all its sides are equal, correct, if Fulfills. Yes, it is a square. Well, it is nothing but
a nested hierarchy of Concepts what we did here we
took a complex task of identifying a square and this case and broken
into simpler tasks. Now this deep learning
also does the same thing but at a larger scale, let’s take an example
of machine which recognizes the animal the task
of the machine is to recognize whether the given image is
of a cat or a dog. What if we were asked to resolve
the same issue using the concept of machine learning
what we would do first. We would Define
the features such as check whether the animal has
whiskers are not a check if the animal has pointed ears or not or whether its tail
is straight or curved in short. We will Define
the facial features and let the system identify which
features are more important in classifying a
particular animal now when it comes to deep learning
it takes this to one step ahead deep learning automatically
finds out the feature which are most important
for classification compare into machine learning where we Had to manually give
out that features by now. I guess you have understood that AI is a bigger picture
and machine learning and deep learning or it’s apart. So let’s move on and focus our discussion
on machine learning and deep learning the easiest
way to understand the difference between the machine learning
and deep learning is to know that deep learning is machine
learning more specifically. It is the next evolution
of machine learning. Let’s take few
important parameter and compare machine learning
with deep learning. So starting with
data dependencies, the most important difference
between deep learning and machine learning is
its performance as the volume of the data gets increased
from the below graph. You can see that
when the size of the data is small deep learning algorithm
doesn’t perform that well, but why well, this is because deep
learning algorithm needs a large amount of data
to understand it perfectly on the other hand
the machine learning algorithm can easily work
with smaller data set fine. Next comes the hardware
dependencies deep learning. Are heavily dependent
on high-end machines while the machine learning algorithm can work
on low and machines as well. This is because the requirement of deep learning
algorithm include gpus which is an integral part of its working the Deep learning
algorithm requires gpus as they do a large amount of matrix
multiplication operations, and these operations can only be efficiently
optimized using a GPU as it is built for this purpose. Only our third parameter will be feature engineering well
feature engineering is a process of putting the domain knowledge
to reduce the complexity of the data and make patterns more visible
to learning algorithms. This process is difficult
and expensive in terms of time and expertise in case
of machine learning. Most of the features are needed
to be identified by an expert and then hand coded
as per the domain and the data type. For example, the features can be a pixel value shapes
texture position orientation or anything fine the Performance
of most of the machine learning algorithm depends on how accurately
the features are identified and extracted whereas in case
of deep learning algorithms it try to learn high
level features from the data. This is a very distinctive part
of deep learning which makes it way ahead of traditional machine learning
deep learning reduces the task of developing new feature
extractor for every problem like in the case of CN n algorithm it first try
to learn the low-level features of the image such as
edges and lines and then it proceeds
to the parts of faces of people and then finally to
the high-level representation of the face. I hope that things
are getting clearer to you. So let’s move on ahead and see
the next parameter. So our next parameter is
problem solving approach when we are solving a problem using traditional
machine learning algorithm. It is generally recommended that we first break
down the problem into different sub parts
solve them individually and then finally combine them
to get the desired result. This is how the machine learning
algorithm handles the L’m on the other hand
the Deep learning algorithm solves the problem
from end to end. Let’s take an example
to understand this suppose. You have a task
of multiple object detection. And your task is to identify. What is the object and where it
is present in the image. So, let’s see and compare. How will you tackle this issue using the concept
of machine learning and deep learning starting
with machine learning in a typical machine
learning approach. You would first divide the problem into two step
first object detection and then object recognization. First of all, you would use a bounding
box detection algorithm like grab cut for example
to scan through the image and find out all
the possible objects. Now, once the objects are recognized you would use
object recognization algorithm like svm with hog
to recognize relevant objects. Now, finally, when you combine the result
you would be able to identify. What is the object and where it is present
in the image on the other hand in deep learning approach you
would do Process from end to end for example in a euro net which is a type
of deep learning algorithm. You would pass an image
and it would give out the location along
with the name of the object. Now, let’s move on to our fifth comparison
parameter its execution time. Usually a deep learning
algorithm takes a long time to train this is because there’s so many parameter in
a deep learning algorithm that makes the training longer
than usual the training might even last for two weeks
or more than that. If you are training
completely from the scratch, whereas in the case of machine
learning it relatively takes much less time to train ranging
from a few weeks to few Arts. Now, the execution time
is completely reversed when it comes to the testing
of data during testing the Deep learning algorithm
takes much less time to run. Whereas if you compare it
with a KNN algorithm, which is a type of machine learning algorithm the test
time increases as the size of the data increase last but not the least we
have interpretability as a factor for comparison
of machine learning and Running this fact
is the main reason why deep learning is still
thought ten times before anyone uses
it in the industry. Let’s take an example suppose. We use deep learning to give automated scoring two essays
the performance it gives and scoring is quite excellent and is near
to the human performance, but there’s an issue with it. It does not reveal white
has given that score indeed mathematically. It is possible to find out that which node of a deep
neural network were activated but we don’t know what the neurons
are supposed to model and what these layers of neuron
we’re doing collectively. So if able to interpret the result on the other
hand machine learning algorithm, like decision tree gives us
a crisp rule for void chose and watered chose. So it is particularly easy
to interpret the reasoning behind therefore the algorithms
like decision tree and linear or logistic regression are primarily used in
industry for interpretability. Before we end this session. Let me summarize things for you machine learning uses
algorithm to parse the data learn from the data and make informed decision based
on what it has learned fine. Now this deep learning
structures algorithms in layers to create
artificial neural network that can learn and make Intelligent Decisions
on their own finally deep learning is a subfield
of machine learning while both fall
under the broad category of artificial intelligence
deep learning is usually what’s behind
the most human-like artificial intelligence. Well, this was all for today’s discussion
in case you have any doubt feel free to add your query
to the comment section. Thank you. I hope you have enjoyed
listening to this video. Please be kind enough to like it and you can comment any
of your doubts and queries and we will reply them at the earliest do look out
for more videos in our playlist And subscribe to Edureka
channel to learn more. Happy learning.


  1. Ahmed Maher

    July 24, 2018 at 2:04 pm

    Thank you for the informative video. I do have 2 questions to point out, to integrate the AI technique in the business, do you need first to connect all the systems of the company together? Second,since the company I'm working for has already a facilities management software, can we only integrate AI with this software?

  2. Tridivsky Kumarov

    July 24, 2018 at 7:05 pm

    Great Explanation…!!!

  3. Rakesh Maurya

    July 25, 2018 at 12:42 am

    I am from market research industry with 7+ years of experience, which course I should pursue to become data scientist, and let me step by step course to become data scientist

  4. Harshit Tiwari

    July 29, 2018 at 5:40 am

    Hi I'm a beginner in this field and want learn AI but don't have any knowledge of Python or AI so please tell how to start??

  5. Shaukat Micromanage

    August 9, 2018 at 1:34 pm

    Thanks from DEEP DEEP for wonderful detailing.

  6. Abhay Chaturvedi

    August 14, 2018 at 7:51 am

    Hi I am a 7 years old PeopleSoft coder and want to build my career in AI. Especially fascinated by application of Deep Learning in most innovative applications like Self Driving cars. Please guide which course to take.

  7. Sakthi Dharani

    August 18, 2018 at 4:04 am

    Hii awesome video. how to collect dataset for training the data in machine learning?

  8. Muni Venkatesh

    August 18, 2018 at 11:31 am

    ThankQ for giving this video it is very helpful to me .I have one doubt Recently im completed my Python course so which course is better for me whether AI & MA with Python or any other

  9. Ravi Kumar

    August 22, 2018 at 5:15 am

    In what order we should learn these three great topics or subjects

  10. siddarth bali

    August 24, 2018 at 8:05 am

    Great explanation ,keep up this good work guys

  11. saravanan saravanan

    August 24, 2018 at 6:46 pm

    its good explanation for learners- thanks a lot, i need still more explanation.

  12. manisha jena

    August 28, 2018 at 9:14 am

    Hi , I am a professional working in an IT company. I work on windows platform . So could you please guide me on which python course should I opt for.

  13. Msd 1370

    September 3, 2018 at 9:16 am

    Thank you. Good explanation..

  14. Ananthakrishnan Balasubramanian

    September 6, 2018 at 11:38 am

    Wow, very nice video, Great one

  15. Milad Sayad

    September 8, 2018 at 10:34 pm

    What do you mean by parameters?

    I read this a lot unfortunately I don't understand the meaning. Parameters or vectors.

  16. Anuradha Kulkarni

    September 13, 2018 at 7:22 am

    Easy to understand

  17. Samaksh Khatri

    September 14, 2018 at 2:26 am

    So tell me if I am wrong, ML uses what is called the 'Divide and Conquer' algorithm and DL uses 'Greedy Method' algorithm?

  18. shivananad otari

    September 16, 2018 at 12:09 pm

    great explanation..!!
    could you please suggest, what should be the strategy for 3 years experience web developer to start career/learning/courses in these area?

  19. Nikhil Dubey

    September 16, 2018 at 6:42 pm

    What language or technology required before learning AI ?

  20. Dhruba Barman

    September 20, 2018 at 4:34 am

    If I want to learn about AI from scratch, Which training should I take first? Do I have to start from deep learning? Or directly I can start from ML or AI?

  21. Dibyananda Das

    September 20, 2018 at 5:41 am

    Good video . Well done . It would be very helpful to me .

  22. Godwin Precious

    September 21, 2018 at 3:13 pm

    I love this video.
    I want to learn AI and I know python so how do I start

  23. pradeep Sarvalkar

    September 21, 2018 at 5:55 pm

    Hii i am computer engineer and I want to make my career in AI what should I do??

  24. Vivek Upadhyay

    September 21, 2018 at 6:07 pm

    Excellent! πŸ™‚


    September 22, 2018 at 9:05 am

    Beautiful knowledge please make a video on blockchain developer

  26. suresh k

    September 25, 2018 at 12:09 pm

    Awesome explanation πŸ€—

  27. Business geek

    September 26, 2018 at 4:44 am

    simple and crisp explanation

  28. Anurag Chandel

    October 2, 2018 at 4:15 am

    i think deep learning has a great scope in future because it is kinda new to us and somewhat scaryπŸ˜… i am just a beginner in tech field and i suppose i should just start with python😁 thanks for the awesome explaination.

  29. arif mahmud faisal

    October 9, 2018 at 1:50 pm

    Thanks sir.

  30. Shubham Srivastava

    October 17, 2018 at 9:03 pm

    Wonderfully explained. Can you also explain the difference between Virtual Reality, Augumented Reality and Mixed Reality?

  31. shismohammad mulla

    October 25, 2018 at 11:25 am


  32. prashu jain

    October 28, 2018 at 10:43 am


  33. akshay kumar

    October 29, 2018 at 4:45 am

    I know Python. Can you please suggest me that what should i learn first to become a Machine learning engineer??
    Tensorflow or SciKit learn?

  34. swagmaster143

    October 30, 2018 at 5:45 pm

    Thanks for making this concept clear.

  35. Dhiren Mistry

    October 31, 2018 at 4:51 am

    My most favourite learning channel

  36. Tarun Kumar

    November 19, 2018 at 1:55 pm

    very well explained

  37. edureka!

    November 22, 2018 at 7:30 am

    Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka AI & Deep Learning Course curriculum, Visit our Website: Use code "YOUTUBE20" to get Flat 20% off on this training.

  38. Chaitany

    December 6, 2018 at 3:19 am

    Tell us how to learn it

  39. Chaitany

    December 6, 2018 at 3:21 am

    Learning ai

  40. steve cyberhero

    December 6, 2018 at 9:37 pm

    Nice video

  41. sasi chandra Morukurthi

    December 8, 2018 at 11:43 am

    super explanation

  42. Saurabh Tripathi

    December 9, 2018 at 9:48 am

    very good videos thanks

  43. Ali Hassan

    December 9, 2018 at 8:53 pm

    if we start learning now is it possible to catch up? in my understanding the field has already reached to a point where new beginers have no chance to catch up. correct of wrong

  44. akash singh

    December 13, 2018 at 7:46 am

    Nice narration.. loved it

  45. Leonora Dompor

    December 14, 2018 at 1:57 pm

    I love AI,machine learning and deep learning

  46. Leonora Dompor

    December 14, 2018 at 1:57 pm

    Thank you Ed !

  47. Luca7x

    December 15, 2018 at 9:31 am

    Nice the trinity.

  48. Umakant Dwivedi

    December 20, 2018 at 5:29 pm

    Very useful. Thanks for the effort.

  49. Nitesh Singour

    December 25, 2018 at 12:27 pm

    Thanks sir

  50. Mony Bernard

    January 14, 2019 at 1:10 pm


  51. ThaliaSolutions_ Media2

    January 19, 2019 at 1:26 pm

    Thanks for sharing. Excellent presentation!!!!

  52. chirag Sharma

    January 19, 2019 at 2:19 pm

    is a mechanical engineering student can learn it

  53. Prasad Shembekar

    January 26, 2019 at 2:04 pm


  54. Defi Norita

    February 1, 2019 at 8:13 am

    can be useddeep learning to solved Supply chain Management Problem?

  55. Muhammad Kiru

    February 1, 2019 at 8:04 pm

    A very good video. Thanks for opening my eyes.

  56. Ashmit Dwivedi

    February 3, 2019 at 5:56 am

    can you describe Deep learning more😊

  57. Desi Kichdi

    February 14, 2019 at 5:59 pm

    What are tools required to learn AI and MI?

  58. AThakker29

    February 15, 2019 at 3:33 pm

    Great presentation…helped me get a good understanding of AI, ML and DL. Thanks a lot!

  59. Kaki

    February 18, 2019 at 10:32 am

    I am sorry I didn't get your idea about the difference between the ML and deep learning on the hardware independence part. Can you illustrate a little bit?

  60. the_nobody _7

    February 20, 2019 at 7:39 pm

    What about AGI

  61. TechSmithy

    February 21, 2019 at 12:45 pm

    How to implement simple equations in tensor flow,
    Please help!!!

  62. O.Hari kumar

    February 26, 2019 at 2:38 pm

    Just two words, Excellent explanation….


    February 27, 2019 at 4:11 pm

    What is data science

  64. u n

    February 27, 2019 at 4:12 pm

    Good job πŸ‘πŸ‘πŸ‘ very nice keep it up , I hope you will upload complete course ty

  65. Sudheesh PP

    March 17, 2019 at 6:45 am

    whether speech recognition comes under machine or deep learning?


    March 19, 2019 at 11:14 am

    just the BEST

  67. Seeta Pawar

    March 23, 2019 at 8:21 am

    Very informative and presented in a crisp manner πŸ‘Œ

  68. Arun Kumar

    April 1, 2019 at 12:43 pm

    Excellent introduction

  69. Parshwadeep Lahane

    April 2, 2019 at 4:33 pm

    great introduction !

  70. Apex Mechanix

    April 7, 2019 at 6:05 am

    Putting this in laymen's terms, machine learning is the equivocal of an ANI(Artificial Narrow Intelligent Machine) handling a task whereas deep learning is the equivocal of an AGI(Artificial General Intelligent) machine. ANI has its base in machine learning and uses deep learning as a gateway to AGI. AGI has its base in deep learning. Everything after that is strictly artificial and synthetic, and no longer biological in nature(humans no longer apart of the development) as AGI will create ASI(Artificial Super Intelligence). Such an era will be known as the age of the singularity where man and machine become one.

  71. Maninder Singh

    April 8, 2019 at 8:17 am

    can dis be please shared as pdf


    April 11, 2019 at 6:53 am

    Nice sir

  73. sushil shipalkar

    April 14, 2019 at 9:51 am

    I am a .net developer, I would like to know how much time it will take to learn ML?

  74. shabuddin md

    April 15, 2019 at 6:31 pm

    thank you for the very neat and clean explanation I fan of your explanation. you are given all the answers to which I have questions thankyou again and one more thing your animation also very nice. great job

  75. John Macondo

    May 7, 2019 at 7:57 am

    Thanks, I have a question,
    Does that mean that google autocomplete in their search depends on machine learning, as it completes depending on what I have searched before?

  76. anil kumar

    May 12, 2019 at 2:58 am

    Hi I don’t have any knowledge about python but I want to start my career towards python developer, can any one please tell me how to start and which is the best way and fast way, I don’t have even degree but I’m much interesting to learn, I had watched many videos but those are make me confuse .

  77. Jyothi Jyothi

    May 21, 2019 at 2:12 pm


  78. raj 495

    May 22, 2019 at 6:34 pm

    It's nicely presented..

  79. ratna pillai

    May 28, 2019 at 3:04 pm

    Quite informative and a summarised version of presentation on AI, ML and DL. Thanks.

  80. Pappu Kumar

    June 3, 2019 at 1:29 am

    Nice explanation

  81. shanaka jayatilake

    June 17, 2019 at 6:57 pm

    amazing video..

  82. Kevin VH

    June 19, 2019 at 5:44 pm

    Awesome job man and very nice presentation thank you for this.

  83. Akki Akki

    June 21, 2019 at 8:28 am

    I wud like to know if I cn get into machince learning with no technical background?

  84. Dr. Errol Wirasinghe

    June 26, 2019 at 3:48 am

    Fantastic! Brilliant!

  85. gunit panch

    June 28, 2019 at 2:34 am


  86. diksha gupta

    June 28, 2019 at 1:28 pm

    thank you sir, its really awesome….but i have one doubt how we use the iris datasets in clustering??

  87. Akash Nag

    July 4, 2019 at 1:39 pm

    Thanks for the DEEP knowledge

  88. Parvinder Kumar

    August 3, 2019 at 11:39 am

    Nice explanation…!!

  89. Hernan Graffe

    August 9, 2019 at 10:03 pm

    The Best and more complete Postgraduate of AI

  90. Sai Nikhil Gona

    August 13, 2019 at 10:03 pm

    Nice video, loved it

  91. Aidid Rashed Efat

    August 21, 2019 at 11:04 am

    Properly described. Thanks from Bangladesh.πŸ‘πŸΌ

  92. Alhambra Qausi

    August 30, 2019 at 9:00 am

    Thanks For Information . Do You Provide Online Course For Machine Learning

  93. Google

    September 9, 2019 at 7:59 am

    Excellent explanation.

  94. Mr Zid

    September 18, 2019 at 7:36 am

    So data science is a subset of machine learning but not deep learning?

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