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in this video I'll give you a complete
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guide to AI specifically for python
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developers you can treat this as a road
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map of topics and modules you may want
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to learn or simply a quick refresher
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that will keep you up to speed with
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everything going on in the AI world with
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that said let's dive in let's begin with
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machine learning algorithms now these
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are computational methods that allow
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computers to learn from and make
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decisions or predictions Based on data
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they do this by identifying patterns and
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relationships in large data sets during
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a process known as training now during
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training an algorithm will adjust its
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parameters in order to minimize errors
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once training is finished then this
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algorithm can make predictions usually
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with a high accuracy if it's been
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trained correctly on new data that it's
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never seen before this means that your
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program has actually learned something
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and without explicit programming it's
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able to take in some new data and make
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hopefully an accurate prediction about
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it there's all kinds of different
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machine learning algorithms but these
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are really at the core of AR icial
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intelligence and you should learn them
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first before moving on to anything more
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complicated now I have a list of machine
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learning algorithms that I recommend
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that you learn so let me read them out
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number one is linear regression followed
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by decision trees random forests K means
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K nearest neighbors and support Vector
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machines there are a ton of other ones
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that you can look at but these are some
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of the more popular ones and they'll
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give you a good foundation of machine
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learning algorithms and how they
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actually work now in order to build
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these out yourself or make them on your
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own you you can use various different
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python modules for example you can use S
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kit learn you can use numpy pandas map
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plot lib and Seaborn now these are all
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used for different tasks within actually
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implementing these types of algorithms
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but they're good to know and really
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where you should start if you want to be
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a python AI developer now obviously as a
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developer we can build out AI features
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on our own but often times it's a lot
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more helpful and much faster to work
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with pre-existing tools now that's where
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interact with their environment gather
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moving on we have neural networks neural
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networks are a specific type of machine
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learning algorithm whose architecture is
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inspired by the function of the human
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brain neural networks are comprised of
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layers of nodes or neurons which are
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interconnected and can transmit
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information between them now each of
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these connections between the neurons
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has something known as a weight this
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weight is adjusted during the training
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process to minimize errors and give a
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better accur for the network or the
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machine learning model now unlike
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traditional or more simple machine
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learning algorithms which typically rely
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on manually crafted features neural
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networks can automatically extract and
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learn more complex features of raw data
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this allows them to do more complex
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tasks and to come up with
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representations or find patterns and
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relationships that simple machine
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learning algorithms just can't do this
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also means that neural networks are a
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little bit more of a mystery after
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you've trained a neural network you're
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not quite exactly sure what it's using
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to come up with the correct answer but
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you know that it's adjusted the weights
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and different parameters within the
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network so that it's fine tune to the
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specific data that you've trained it on
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now this is a complex topic and not
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something we need to get into a ton but
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there are a few features you may want to
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consider learning about neural networks
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obviously the architecture so things
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like an input layer your hidden layers
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your output layer you want to learn
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about things like activation functions
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weights and biases and how data is
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propagated through the network then then
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there are two main terms you'll be
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interested in in terms of how a network
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is actually trained the first is back
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propagation this refers to actually
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going from the end result and going back
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through the network and adjusting all of
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the weights and biases to learn from
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mistakes or to move closer towards the
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correct answer or the correct features
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within the neural network the next is
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gradient descent gradient descent is
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something that refers to finding the
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local minimum or maximum in kind of
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three-dimensional or multi-dimensional
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space and this is something that's used
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to kind of guide the model in the right
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direction again I'm not an expert in
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this field and I don't want to confuse
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you in just a few minutes here but these
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are really cool things to learn about
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and those are some of the main topics
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you may want to focus on now in order to
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build and work with neural networks you
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can look at modules like P torch
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tensorflow and caras and you can also
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check out things like neat neuro
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evolution of augmented topologies which
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is a little bit more advanced but uses a
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neural network as its backbone moving on
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we have computer vision now computer
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vision refers to doing image and video
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analysis and typically things like
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object detection and tracking facial
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recognition image segmentation so
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picking out specific parts of images and
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can do all kinds of other amazing things
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for example I was just watching the
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Olympics and that's a great example
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where they have a ton of computer vision
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and they're doing things like tracking
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all of the places different athletes hit
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the ball or where they serve the ball on
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like a pingpong table really interesting
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applications and typically this can use
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basic machine learning algorithms or
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more advanced things things like
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convolutional neural networks now what I
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mean by that is we'll take an actual
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image and we'll analyze that image using
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machine learning algorithms those could
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be basic machine learning algorithms
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like some of the ones that we talked
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about before that are more specified for
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images or things like convolutional
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neural networks which are specific types
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of neural networks in terms of their
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architecture that analyze images and
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videos specifically again there's a lot
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of topics to get into within computer
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vision but if you want a few modules to
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check out that can help you do this the
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first that I definitely recommend is
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open CV this has a lot of built-in uh
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kind of models and features in it that
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allow you to really easily do computer
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vision tasks and then we have things
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like scikit image and pillow or pill p l
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check those out computer vision to me is
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one of the coolest Fields with in Ai and
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with python you can get started pretty
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quickly without having to be an expert
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now after computer vision we have large
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language models or llms which in my
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opinion are one of the most
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misunderstood forms of AI in terms of
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what they actually do and how you can
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use them large language models are
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designed to be able to understand and
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generate human text in a broad sense
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they're trained on tons of different
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textual data from things like books
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movies articles the internet doesn't
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matter they're trained on so much
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different text and this allows them to
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broadly be pretty good at a lot of
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different tasks they can answer
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questions they can generate essays for
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you they can write code they can do a
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lot of things decently well in so many
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different context because of the amount
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of information that they've been trained
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on that said most llms with the
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exception of things like chat GPT that
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have other features built into them
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don't have access to realtime
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information and sometimes they can
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hallucinate and give you responses that
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don't actually make sense even though it
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sounds like they do because their
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English is quite good this means if you
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want an llm to be really good for your
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specific task you should find tune it
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fine-tuning is the process of passing
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this specific data related to exactly
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what you want this llm to do for example
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medical diagnosis you could pass it a
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ton of past diagnosises and it can learn
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quickly and use that in combination with
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its natural language processing ability
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to really answer something quite well
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and to achieve higher performance now
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when we talk about llms there's two main
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types you want to be aware of the first
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is GPT or generative pre-trained
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Transformer now this process is text
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unidirectionally meaning going from left
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to right and is generally very good at
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generating new content now the next is
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Bert b t and I'm just going to read this
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so I don't get the name wrong this is
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bidirectional encoder representations
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from Transformers now this understand
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text and processes it from left to right
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and from right to left which allows it
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to be really good at comprehension and
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answering questions about information or
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textual data it's not quite as good as
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actually generating new content or text
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so generally speaking if you want to
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generate something new that doesn't
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already exist use GPT and if you want to
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understand something more deeply and
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have better comprehension you can use a
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Bert model now if you want to work with
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various llms in Python then I recommend
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looking at hugging face hugging face is
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a place that has pre-built Transformers
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for you that you can f- tune and bring
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into your python programs now moving on
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we get into the natural Next Step which
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is rag or retrieval augmented generation
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now this is a technique that you can use
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with llms to get better responses and
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use them for more context specific
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applications as I said before llms
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typically don't have access to realtime
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information or even if they do they
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might not have access the information
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you need them to have let's say you're a
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restaurant you have some reservations
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you have different menus you have a
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bunch of information that's constantly
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changing and maybe stored in some kind
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of database well you may want an llm
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internally or some kind of chatbot that
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you can really quickly ask questions too
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so you don't need to go and query all of
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that information on your own well if you
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just have a base LM it doesn't have
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access to all of that information so
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instead we use a technique called rag
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now rag involves your llm querying a
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specific type of database grabbing
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information that's relevant to what it
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needs to answer and then giving you a
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context specific response what I mean by
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that is you may ask the llm hey what's
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the menu tonight and what it will do is
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go to something known as a vector search
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database at least this is typically what
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you'll use it can very quickly find
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information relevant to the menu pull
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that Direct IR L into the llm read
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through it understand it and then use it
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to generate a response that makes sense
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so what you're doing is you're taking
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the data that you want this model to
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have access to storing it in a really
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fast database to look up stuff from
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again typically called a vector search
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or vector store database the llm will
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then use that database by providing some
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kind of prompt it will get relevant
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results back it will read and understand
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that and then generate something that's
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context specific this is actually not
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that difficult to implement I've made
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all kinds of videos on it on my channel
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and this is a way that you can really
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enhance llms and use that base level
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natural language processing and
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understanding ability to really enhance
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your own applications now if you want to
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build rag apps in Python you can do this
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using things like Lang chain olama llama
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index uh those are kind of the main ones
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you'll probably want to mess with moving
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on we're talking about AI agents now ai
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agents don't just have the ability to
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read information from something like a
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database but they have the ability to
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interact with their environment and use
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maybe various different tools that you
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give them access to an AI agent could be
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something as simple as having the
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ability to send an email or it could be
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something that has access to a full
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Suite of different tools and actually
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selects by itself what tool it should
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use based on the task you ask it for
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example you could make your own AI
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virtual assistant and in this case what
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it will do is have access to your
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calendar it can read that information it
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could create new events it could send
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emails on your behalf it could delete
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things it could clean up your desktop
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for you it can do whatever you want it
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to do if you give it access to those
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specific tools AI agents interact with
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the environment they have access to a
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set of tools and they can do things like
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determine the tasks they need to do or
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multiple tasks that they need to do
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collect data and then obviously as I
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said many times interact with the
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environment which is the key Point here
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to build out these type of AI agents you
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can use the same Frameworks I talked
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about before like Lang chain and llama
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index and they are super cool and
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something you should definitely mess
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with with that said guys that's going to
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wrap up this video wanted to give you a
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quick comprehensive guide to all of the
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AI stuff as a python developer you
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should know if you found this helpful
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make sure you leave a like subscribe and
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I will see you in the next one
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[Music]
