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welcome to the first episode of the AI
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for business course your ultimate
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non-technical guide to understanding Ai
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and how to apply it in the real world if
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you're not an AI expert but you truly
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wish to comprehend what it is and how to
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utilize it in your work this course will
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definitely assist you in achieving that
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if you haven't watched the course intro
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I recommend you do so through this link
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my name is Omar I've been helping many
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organizations all over the world apply
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machine learning to transform the
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day-to-day operations and today we'll
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dive into what a is and how it
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works this episode consists of three
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main parts first we'll start with a high
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level overview of AI machine learning
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and eat learning next we'll focus on
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machine learning the most influential
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form of AI today we'll explore its
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various types how each work and have a
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look at some use cases and lastly we'll
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delve into deep learning and take a look
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at some of the applications it's
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enabling today you might might be
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wondering do I really need to know all
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these details the truth is having a
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foundational understanding of AI is
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crucial to make the most out of it even
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if you're not going to be the one
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building it or writing code this
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knowledge equips you with the ability to
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understand which Solutions are feasible
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in which or not communicate and
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collaborate with AI teams and set the
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right expectations with management and
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stakeholders as promised we're not going
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to delve too deep in the weeds just
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explaining the key Concepts so let's
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Dive Right In AI or artificial
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intelligence is the science of enabling
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computers to achieve humanlike levels of
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intelligence this is accomplished by
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replicating the qualities of the human
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mind such as perception reasoning
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planning and decision- making machine
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learning is a subfield of AI that
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enables computers to learn from data by
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discovering and extracting patterns as
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opposed to being explicitly programmed
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while AI encompasses other fields
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machine learning is by far currently the
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most impactful and
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Powerful so consequently when people
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refer to AI today especially in
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industrial context they are usually
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referring to machine learning deep
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learning is a technique within machine
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learning that is inspired by the
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structure of our brains it has recently
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excelled at identifying patterns and
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learning from highly unstructured data
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such as images Voice and text AI is
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empowering a wide range of applications
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such as CH gpdm mid Journey virtual
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assistants like Siri and Alexa
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personalized recommendations for Netflix
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and Spotify email spam filters fraud
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detection systems language translation
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tools such as Google translate and
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more each of these applications employs
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machine learning at its core intrigued
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let's have a closer look at machine
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learning and how it works there are four
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major types of machine learning
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supervised unsupervised self-supervised
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and reinforcement learning many of the
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AI applications used in production today
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leverage the first type supervised
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learning which is learning how to make
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predictions based on examples these
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examples should map certain inputs to
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some output that we're trying to predict
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the more input output examples we show
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to the machine the more accurate its
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predictions going to be for newer inputs
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that it hasn't seen before let's say
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we're trying to predict house pricing
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the inputs could include square footage
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number of floors number of rooms whether
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the house has a backyard or not Etc the
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output could be the actual house price
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if we can get such data for thousands of
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houses we can use it to teach a model
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how to predict the pricing of new houses
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that it hasn't seen before provided we
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have the input attributes of those
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houses in supervised learning the
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process of teaching a model using input
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output examples is called training
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during training the model tries to find
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the underlying patterns between the
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inputs and the output and learns how to
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make predictions based on these
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patterns the output that comes with the
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training data set is called a label so a
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training data set with the inputs and
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corresponding outputs is called a label
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data set we use label data sets to train
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models to make predictions let's take
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another example for supervised learning
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email spam detection the training data
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in that case could include inputs like
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the sender email the time the email was
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sent the content of the email and the
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email subject line and the output could
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be an attribute dictating whether each
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email is classified as spam or not in
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the house pricing example we were trying
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to predict a quantity the house price
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using a technique called
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regression in the email spam detection
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example we were trying to predict a
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probability spam or not spam using
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another technique called
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classification supervis learning could
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leverage either regression or
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classification depending on what are we
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trying to predict supervised learning
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could also be used with other types of
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data we can use it for example to train
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a model to detect whether an image has a
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dog or Not by showing it different
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images some with dogs others with with
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no dogs you can use it to trade a model
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to detect sentiment in tweets whether
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it's positive negative or neutral if you
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show it a lot of examples of tweets with
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positive neutral or negative sentiment
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the examples are endless
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unsupervised learning is another form of
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machine learning that does not rely on
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labeled data instead it deals with
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inputs only and its goal is to find
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patterns relationships or structures
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within the input data without any
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guidance from output labels one of the
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most common techniques in unsupervised
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learning is clustering clustering groups
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data points with similar attributes
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allowing them to be assigned to distinct
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clusters this could be particularly
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useful for applications like customer
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segmentation
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where understanding distinct customer
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groups can be very important for
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businesses to help them come up with
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better targeted marketing campaigns for
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example or improve their product
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offerings anomaly detection is another
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application of unsupervised learning
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which aims at identifying data points
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that deviate significantly from the norm
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it could be particularly useful for
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applications like financial fraud
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detection or cyber security threat
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detection and so on in summary
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unsupervised learning can help you
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uncover hidden patterns and
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relationships in your data without the
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need for labeled examples I'm sure by
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now you have heard about Chad GPT and
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probably mid Journey both of which are
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prime examples of generative AI
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applications those two utilize an
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emerging category of machine learning
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called self-supervised learning which
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combines aspects of both supervised and
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unsupervised learning in self-supervised
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learning the model generates its own
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labels from the input data which could
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then be used to train the the model
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further similar to supervised learning
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the primary advantage of this approach
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is that it almost eliminates the need
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for large amounts of labels which is
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usually a timeconsuming process and
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costly to
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obtain chpt is a large language model
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that generates humanik text by
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predicting the next word in a sentence
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or a conversation based on the context
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of the words that came before in this
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approach the model treats the task of
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predicting the next word as a self
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supervised learning goal each next word
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in the provided data set act as a label
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for the preceding sequence of
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words this process allows chat GPT to
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learn from large amount of unlabelled
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text enabling it to provide coherent and
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contextually appropriate responses in a
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conversational setting another exciting
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development in self-supervised learning
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is the use of diffusion models which are
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capable of generating images from
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textual input those models such as DOL
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to and stable diffusion are able to take
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text input such as the description of a
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scene and generate corresponding images
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the fourth and last type of machine
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learning we're going to explore today is
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reinforcement learning this type focuses
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on learning by interacting with an
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environment optimizing a set of actions
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to achieve the highest possible outcome
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or reward in reinforcement learning an
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agent takes actions within an
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environment to achieve a specific goal
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the agent receives feedback in the form
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of reward or penalties for each action
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it takes allowing it to learn and adapt
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its strategy over time this trial and
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eror approach enables the agent to
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discover the optimal sequence of actions
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that lead to the highest cumulative
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reward effectively learning from its
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experiences reinforcement learning has
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found success in numerous real world
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applications including supply chain
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Logistics optimization robotics
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navigation and optimizing trading
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strategies it has has also shown great
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promise in the field of autonomous
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vehicles and game playing where agents
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need to make complex decisions based on
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Dynamic environments so to wrap up this
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part we have discussed four main types
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of machine learning first supervised
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learning which uses label data to make
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predictions second unsupervised learning
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which can help us find hidden patterns
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and relationships within our unlabeled
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data third self-supervised learning
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which combines aspects of both
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supervised and unsupervised learning and
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uses the data itself to generate labels
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and finally reinforcement learning which
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focuses on learning the best policies
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actions and decisions based on
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interacting with an
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environment those different types
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provide a powerful toolkit for
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addressing numerous real world
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challenges and help enable AI driven
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Solutions now that we' have explored
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different types of machine learning
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let's dive into deep learning which has
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been a driving force behind many of the
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recent AI breakthroughs deep learning is
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a subfield of machine learning that
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focuses on algorithms inspired by the
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structure and function of the human
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brain specifically neuron
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networks these neuron networks consist
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of layers of interconnected nodes or
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neurons that process and transmit
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information the depth of a neural
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network meaning the number of layers it
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has is what differentiates deep learning
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from traditional machine learning
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techniques the true power of deep
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learning lies in its ability to
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automatically learn complex
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representations and hierarchical
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features from vast amounts of data this
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capability is especially beneficial when
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dealing with high-dimensional and
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unstructured data such as images Voice
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and text in a deep learning model the
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input data is processed through multiple
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layers of neurons with each layer
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learning progressively more abstract
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features the final output layer provides
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the result such as a prediction or
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classification during training the model
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adjusts the weights of the connections
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between neurons to minimize the error
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between its predictions and the actual
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outcomes for example in image
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recognition a deep learning model might
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first learn to identify edges and
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textures in the lower layers then shapes
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and objects in Middle layers and finally
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more abstract Concepts like specific
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object categories in higher layers for
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instance it can differentiate between a
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Labrador and a bulldog in dog images or
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an SUV from a sedan in car images
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some popular deep learning architectures
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include convolutional neuron networks or
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cnns which are usually used for image
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recognition tasks long shortterm memory
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networks or lstms which are uniquely
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suited for modeling sequential data like
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time series for casting and Transformers
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which have become fundamental in
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advanced natural language processing
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tasks driving the success of models like
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GPT for text generation and also being
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adapted for computer vision tasks deep
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learning learning has become the goto
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method for most modern applications that
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process and extract insights from highly
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unstructured data for example CD employs
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deep learning for speech recognition
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chpt mid journey and do 2 they all use
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deep learning for Content generation for
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text and
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images and in computer vision deep
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learning has enabled the whole new level
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of object detection and image
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classification systems enabling
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applications like self-driving cars
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autonomous robots and autonomous drones
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with this knowledge in hand it's time to
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wrap up today's episode I hope you find
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this introduction to AI insightful and
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valuable now that you have a good
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understanding of the Core Concepts it's
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time to ask a crucial question what are
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the practically use cases that can make
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a real difference in your work today
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that's exactly what we're going to
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explore in the next episode we'll dive
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into the major AI use case patterns that
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apply across different Industries we'll
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also explore a lot of use cases in
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different sectors such as many
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manufacturing supply chain Healthcare
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retail banking Insurance government and
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more if you enjoyed this video I
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appreciate if you can share it with
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others you can benefit from it and give
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it a thumbs up if you want if you want
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to see more content like that you can
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subscribe to the channel and until we
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meet again thank you for your time and
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very much looking forward to seeing you
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in the next
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[Music]
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episode
