AI vs Machine Learning vs Deep Learning vs Data Science

Key Takeaways

ever wondered about the differences between AI m LD L and D s well we're about to explore all of those today stay tuned so let's dive right into it so AI versus ml versus do versus dears a whole bunch of jargon but we're going to clarify all of that right up so let's kick things off and take a look at AI so AI is really to do with the ability of computers and machines to perform tasks without explicitly programming them otherwise known as the ability for computers and machines to think by themselves so we typically break out AI into two key categories these are general AI and neuro a on general AI typically refers to the ability for a computer or a machine to be able to handle a wide variety of tasks us as humans have the ability to do a whole heap of stuff we can see we can speak we can hear we can read we can drive we can do a whole range of things the ability for AI and machines to be able to do a broad range of tasks similar to humans is what we typically refer to as general AI now we're still a little bit of a while away from true general AI but that's not to say it's not to come now narrow AI on the other hand is the ability for a machine to handle a really simple or a really narrow range of tasks so that could possibly be the ability to translate speech to text or to classify images as having different categories or the ability to predict house prices for example all of these are examples of narrow AI so I'm going to be painting a bunch of visual imagery to help you remember some of these topics so the first one in terms of breaking out general and narrow AI or the ability to remember general and our AI is just picture a really narrow or really skinny journal in your mind so that way you know that there's two different types of AI general and narrow now on to the next topic machine learning so if taking a look at AI as being broken up into journal and narrow but how does machine learning fit into this well machine learning is the application of narrow AI to specific tasks now when we typically talk about machine learning we often compare it to traditional programming so in traditional programming we supply data plus rules or conditional logic and we get answers now in machine learning on the other hand we provide data plus historical answers to get rules we can then pass new data to get new answers so this is a bit of a change in the paradigm of how computer scientists and machine learning engineers are building programs these days so what are some typical machine learning tasks well we broadly break out machine learning into three key categories these are supervised learning unsupervised learning and semi-supervised learning so let's take a look at supervised learning first so supervised learning can be broadly broken out into two key categories these are classification and regression classification is all to do with grouping things into categories or labels so say you had a big data set on all the different types of pizzas you've liked and whether or not you've liked them yes or no you could take that data and pass it through to a classification algorithm to help but learn which types of pizzas you like so then when you pass through a new list of ingredients it would be able to predict yes you would like that Pizza or no you might not regression on the other hand is all to do with predicting continuous variables some great examples of regression are sales forecasting and predicting prices of houses so that encapsulates supervised learning now what about unsupervised learning well there's two key things to think about when you think of unsupervised learning these are really clustering so the ability to group people together so say you wanted to group together high performing and low performing and medium performing employees or high-value low value medium value customers or a whole bunch of other different types of data but really it's all to do with grouping things together now dimensionality reduction on the other hand is all to do with condensing the features that you've got within a machine learning model so a lot of the time you might start out with a huge data set with a lot of columns and you're not really sure which of those columns are important for your machine learning model dimensionality reduction helps you reduce the number of columns that you've got so that you can really focus on the important ones now in order to remember supervised learning and unsupervised learning I'd suggest you remember this initialism Christopher Robin quarter duck so that way you remember classification regression clustering and dimensionality reduction so that takes care of supervised and unsupervised learning but what about semi-supervised learning well this is where reinforcement learning comes in now reinforcement learning has four key things these are an agent and action and environment and of reward it's similar to how you might choose to condition a dog a dog might do something right and you might reward it with a piece of food in a similar way we train reinforcement learning models to act in a correct way in a given environment in order to learn appropriate actions given that specific environment now the best way to remember reinforcement learning techniques is to remember area 51 so that way you remember agent reward environment and actions okay so that takes care of machine learning now we're gonna delve a little bit deeper and get into deep learning so deep learning is a subset of machine learning and really it's to do with performing machine learning tasks using deep neural networks now deep neural networks and networks that have multiple hidden layers so if you've ever seen a diagram that looks sort of like this this is a representation of a neural network but specifically in this case this is a deep neural network because it has multiple hidden layers now the best way to remember deep learning is to remember that deep learning is just like an onion it has multiple layers a little bit like Shrek now that's what it covers AI ml and deal what about data science well data science is the practice that sits over AI ml and deal it basically is the art of extracting knowledge insight and meaning from data the best way to remember the key components of data science are to look at the crisp DM framework so the crisp DM framework stands for the cross industry standard process for data mining and basically it's a framework to help you along your way to producing really good data science projects now there's six key steps in the data science process these are business understanding so understanding the business that you're working with and the environment in which they operate - data understanding so understanding the data that you've got on hand so whether or not you've got missing values visualizing that data and taking a look at some summary statistics we've then got data preparation so this is all to do with getting our data ready for modeling in this step we might perform some feature engineering and create some new columns we might fill in some missing values and a whole bunch of other data preparation steps like for example splitting our data to training and testing next we've got my favorite which is modeling this is all to do with training your machine learning algorithms to perform well on a specific task once we've trained our models in that modeling step we get onto evaluation given that we've trained our model we want to make sure that it's going to work well once we deploy it into the real world this is what the evaluation step is all about in this step we try to check whether or not our model is likely to perform well using specific evaluation metrics now once we've gone through all of that the last step is to go and deploy our model in order to deploy our model we could release it as a REST API containerize it up or save it as a binary so we can go and use it elsewhere now a great way to remember Chris PMS remember Barry drove directly to the medical emergency department that way you remember business understanding data understanding data preparation modeling evaluation and deployment now I've talked a lot about theory but where do the Python packages that you typically see used fit into this framework well in terms of data science numpy pandas and matplotlib are probably going to be the most important packages that you see floating around numpy and pandas help you traverse and explore your data and really work with your data in terms of performing manipulations and data preparation matplotlib and Seabourn help you visualize that data and explore it even further now the most important library in terms of machine learning is probably scikit-learn so scikit-learn is been around for quite some time and gives you a whole bunch of really powerful algorithms and utilities to help use them to train your machine learning models now deep learning is becoming increasingly popular and there's a large number of libraries that can help you perform deep learning some of which which are notable are tensorflow keris pi torch and piano just to name a few and that about wraps up AI versus MO versus do versus DES thanks so much for tuning in guys hopefully you found this video useful if you did be sure to give it a thumbs up and hit subscribe until next time peace [Music]