AI Vs Machine Learning Vs Deep Learning - Explained in 4 min!!
Summary
TLDRIn this video, the host expresses gratitude for the channel's first month, highlighting the community's feedback. They introduce the basics of AI, explaining it as a broad term for technologies that enable machines to perform tasks requiring human intelligence. Machine learning is a subset, focusing on teaching machines through data to learn and improve without explicit programming. The video distinguishes between supervised and unsupervised learning, with examples like image recognition and Netflix's recommendation system. It concludes with an introduction to deep learning, which uses neural networks inspired by the human brain to process data.
Takeaways
- 🎉 The channel has been active for one month and is grateful for the audience's engagement and feedback.
- 📈 The creator initially focused on practical machine learning projects but received feedback to also cover basic concepts.
- 🧠 AI is an umbrella term for technologies that enable machines to perform tasks requiring human intelligence, such as reading, seeing, analyzing, and understanding.
- 🔧 Machine learning is a subset of AI and involves teaching machines with data so they can learn and improve from experience without explicit programming.
- 📊 There are two main types of machine learning: supervised, where the algorithm is given input and output data, and non-supervised, where only input data is provided.
- 🐱🐶 In supervised learning, examples include training an algorithm to distinguish between cats and dogs using labeled images or predicting ice cream sales based on temperature data.
- 🌲 Common supervised learning algorithms include regression, logistic regression, decision trees, random forest, and boosting, each suited for classification, regression, or both.
- 📚 Non-supervised learning involves analyzing input data to find patterns and generate insights, like Netflix's recommendation system that suggests what to watch based on user behavior.
- 🤖 Deep learning is a subset of machine learning that uses neural networks, which are inspired by the human brain's structure and function.
- 🧠 Neural networks consist of layers of nodes that process input data to produce output, mimicking the way neurons in the brain work.
- 🔑 The channel plans to discuss neural networks and various machine learning algorithms in detail in upcoming videos.
Q & A
What is the purpose of the channel mentioned in the transcript?
-The purpose of the channel is to educate and engage with the audience on practical machine learning projects and to discuss basic machine learning concepts based on audience feedback.
Why did the channel creator decide to explain basic machine learning concepts?
-The creator decided to explain basic machine learning concepts after receiving feedback requesting more foundational information before diving into practical projects.
What is the difference between AI and machine learning as described in the transcript?
-AI is a broader term that refers to the use of technologies to create smart machines capable of complex tasks requiring human intelligence. Machine learning is a subset of AI and refers to the method of teaching machines with data so they can learn and improve from experience.
What are the two types of machine learning algorithms mentioned in the transcript?
-The two types of machine learning algorithms mentioned are supervised machine learning, where the algorithm is provided with input and output data, and unsupervised machine learning, where only input data is provided without specific output labels.
How does a supervised machine learning algorithm learn to distinguish between different categories?
-A supervised machine learning algorithm learns by being provided with labeled input data, such as images of cats and dogs. It then learns to associate the input data with the correct label and can make predictions on new, unseen data.
What is an example of using supervised machine learning with numeric data?
-An example given in the transcript is using historical data of ice cream sales along with the temperature on a given day. The algorithm learns the relationship between temperature and sales and can predict ice cream sales based on new temperature data.
What are some commonly used supervised machine learning algorithms?
-Some commonly used supervised machine learning algorithms include regression, logistic regression, decision trees, random forest, and boosting.
How does an unsupervised machine learning algorithm differ from a supervised one?
-An unsupervised machine learning algorithm does not require labeled output data. Instead, it analyzes input data to find patterns and generate insights or recommendations, such as in the case of the Netflix recommendation system.
What is deep learning and how does it relate to machine learning?
-Deep learning is a subset of machine learning that uses neural networks, which are inspired by the structure of the human brain. It involves layers of nodes that process input data to produce an output, mimicking the way neurons in the brain work.
What is an example of an unsupervised machine learning algorithm mentioned in the transcript?
-An example of an unsupervised machine learning algorithm mentioned is K-means clustering, which is used to find patterns in data and group similar data points together.
What is the significance of neural networks in deep learning?
-Neural networks are significant in deep learning as they are the algorithms that process input data through layers of nodes, similar to how neurons process information in the human brain, allowing the system to learn complex patterns and make predictions.
Outlines
🎉 Introduction to the Channel and Future Content
The script begins with a warm welcome to the channel, celebrating its one-month anniversary. The speaker expresses gratitude for the audience's engagement and feedback, which is highly appreciated. They reflect on the channel's start with practical machine learning projects but acknowledge feedback requesting a deeper dive into basic machine learning concepts. As a response, the speaker announces upcoming videos that will cover fundamental machine learning concepts before returning to practical projects.
🤖 Defining AI, Machine Learning, and Deep Learning
This paragraph delves into defining AI, machine learning, and deep learning. AI is described as a broad term for technologies that enable machines to perform complex tasks requiring human intelligence, such as reading, seeing, analyzing, and understanding. Machine learning is presented as a subset of AI, focusing on the method of teaching machines through data to learn and improve from experience without explicit programming. The paragraph distinguishes between supervised and unsupervised machine learning, providing examples such as image recognition and the Netflix recommendation system, and mentions common algorithms used in each type.
Mindmap
Keywords
💡AI
💡Machine Learning
💡Deep Learning
💡Supervised Learning
💡Unsupervised Learning
💡Neural Networks
💡Regression
💡Classification
💡K-means Clustering
💡Feedback
💡Engagement
Highlights
Introduction of the channel and acknowledgment of one month anniversary.
Appreciation for audience engagement and feedback.
Initial focus on practical machine learning projects.
Feedback led decision to explain basic machine learning concepts.
Plan to discuss basic machine learning concepts in upcoming videos.
Definition of AI as an umbrella term for smart technologies.
Machine learning as a subset of AI and a method of teaching machines with data.
Explanation of supervised and non-supervised machine learning algorithms.
Supervised learning involves providing input and output labels for the algorithm to learn.
Example of supervised learning with image recognition of cats and dogs.
Regression as a common supervised learning algorithm for numeric data prediction.
Non-supervised learning involves analyzing data patterns without predefined labels.
Netflix recommendation system as an example of non-supervised learning.
K-means clustering as a commonly used non-supervised learning algorithm.
Deep learning as a subset of machine learning using neural networks.
Neural networks inspired by the structure of the human brain.
Upcoming detailed discussion on neural networks in future videos.
Call to action for subscription to stay updated with channel content.
Transcripts
hello everyone and welcome to the
channel it has been already one month
since we started this Channel and I
would like to thank you for your
engagement nice comments and feedback I
really appreciate it and it means a lot
to me when I started this channel one
month ago I started straight away into
practical machine learning projects but
then some of the feedback I received
which I highly appreciate is that can
you explain a little bit on the basic
machine learning Concepts that's why I
decided in the coming couple of videos
we will discuss about the basic machine
learning Concepts before we continue
doing some interesting p iCal projects
and in this video we will start with the
very basic one what is AI what is
machine learning and what is deep
learning AI is a general term refers to
the use of Technologies to make smart
machines that can perform complex tasks
that require human intelligence so that
the machines can read can see analyze
and understand just like human you can
think about it more of umbrella term
that everything will under it after that
machine learning on the other hand is a
subset of AI and it is more of a method
than a term it refers to the process of
teaching the machines with data so that
the machines can learn and improve from
experience to perform complex tasks
without being explicitly programmed when
we discuss machine learning we are
actually discussing algorithms and there
are two types of machine learning
algorithms there is the supervised
machine learning and nonsupervised
machine learning in supervised machine
learning we providing the algorithm with
an input and output which is often
called the label the input data could be
for example hundred of images for cats
and then we tell the algorithm that
these are cats and then we can provide
another hundred of images for dogs and
then we tell the algorithm that these
are dogs and then the algorithm will
learn from this input data and should be
able to distinguish between a cat and a
dog and then when whenever we provide a
new data and New Image the algorithm
should be able to tell us if this is a
cat or a dog the data could be also
numeric data for example if I have six
months of historical data of ice cream
sales together with temperature in a
given day I can provide the temperature
data as input and the sales of ice cream
as output and then the algorithm will
learn from this data when the
temperature was this the sales of ice
cream was this and then whenever I
provide new data for temperature the
algorithm should be able to predict for
me what would be the sales for ice cream
some of the commonly used supervised
machine learning algorithms are
regression logistic regression decision
trees random Forest rium boosting and so
on some of them are used for
classification some of them are used for
regression and some of them are used for
both we will discuss about each one of
them in the coming videos in
nonsupervised machine learning we don't
provide an output or a label we provide
only input data and then the algorithm
will try to analyze and understand the
pattern in this data and come up with
insights or
recommendations example for this is
Netflix recommendation system your
behavior on Netflix platform is the
input data and then Netflix algorithm
will analyze this data and analyze the
pattern in this data to understand your
preferences what you like most what you
don't like and to come up with
recommendations for you what to watch
next without us giving the algorithm any
labels about your
preferences so this is nonsupervised
machine learning one of the commonly
used nonsupervised machine learning
algorithm is the K mean clustering we
will discuss about this one in details
in the coming videos and lastly deep
learning deep learning is a subset of
machine learning in which we are using a
specific algorithm called neural network
neural network is inspired by the
structure of human brains in which we
are having billions of neurons
processing input data to come up with an
output similarly in neural networks we
have notes that represents neurons in
the form of layers that are processing
input data to come up with an output we
will discuss about neural networks in
details in the coming video vide don't
forget to subscribe to keep you updated
Посмотреть больше похожих видео
1.2. Supervised vs Unsupervised vs Reinforcement Learning | Types of Machine Learning
The Fundamentals of Machine Learning
Understanding Artificial Intelligence and Its Future | Neil Nie | TEDxDeerfield
Machine Learning vs Deep Learning
35. Che differenza c'è tra Intelligenza Artificiale, Machine Learning e Deep learning? #36
AI vs Machine Learning
5.0 / 5 (0 votes)