AI vs ML vs DL vs Data Science Explained

 AI vs ML vs DL vs Data Science Explained

Welcome. And today, we'll be understanding the difference between Artificial intelligence, Machine Learning, Deep Learning, and Data Science These all are big buzzwords, right? But knowing when to use which one is quite important... so let's break it down 1. Data ScienceData science is all about data, and I’m pretty sure you already knew that. We all know that every single tech company out there is collecting huge amounts of data from us. And data in the 21st century is revenue. Why is that? That’s because of data science. The more data you have, the more business insights you can generate. Using data science, you can uncover patterns in data that you didn’t even know existed. For example, you can discover that some guy who went to New York City for a vacation is most likely to splurge on a luxury trip to Venice in the next three weeks. And if you’re a company offering luxury tours to exotic destinations, you might be interested in getting this guy’s contact number. Just like this, Companies are using data science to build recommendation engines, predicting user behavior, and doing so much more. And all of this is only possible when you have enough amount of data to get accurate results that can be applied to a business use case. Next, there is also something called prescriptive analytics in data science, which does pretty much the same predictions that we talked about in the rich tourist example above. But as an added benefit, prescriptive analytics will also tell you what kind of luxury tours to Venice a person might be interested in. For example, one person might want to fly first class but would be fine with a three-star accommodation, whereas another person could be ready to fly economy but definitely needs the most luxurious stay and cultural experience of Venice. So even though both these people will be your rich clients, both of them have different requirements. So, in this scenario, prescriptive analytics will help you a lot You might be wondering, hey, that sounds a lot like artificial intelligence. And you’re not entirely wrong, actually. Let’s see how. 2. Artificial IntelligenceArtificial intelligence, or AI for short, is the ability that can be imparted to computers which enables these machines to understand data, learn from the data, and make decisions based on patterns hidden in the data, or inferences that could otherwise be very difficult (or almost impossible) for humans to make manually. AI also enables machines to adjust their “knowledge” based on new inputs that were not part of the data used for training these machines. But there’s one thing you need to make sure of, that you have enough data for AI to learn from. If you have a very small data lake that you’reusing to train your AI model, the accuracy of the prediction or decision could below. So more the data, the better is the training of the AI model, and more accurate will be the outcome. Depending on the size of your training data, you can choose various algorithms for your model. And this is where machine learning and deep learning start to show up. 3. Machine LearningMachine Learning (ML) is considered a sub-set of AI. You can even say that ML is an implementation of AI. So whenever you think of AI, you can think of applying ML there. As the name makes it pretty clear, ML is usedin situations where we want the machine to learn from the huge amounts of data we give it, and then apply that knowledge to new pieces of data that are streamed into the system. But you might be thinking, how does a machine learn? Well, there are different ways of making machines learn, these broadly include supervised learning, non-supervised learning, semi-supervised learning, and reinforced machine learning. In some of these methods, a user tells the machine what are the features or independent variables (input) and which is the dependent variable (output). So the machine learns the relationship between the independent and dependent variables present in the data that is provided to the machine. This data which is provided is called the training set. And once the learning phase or the training is complete, the machine, or the ML model, is tested on a piece of data that the model has not encountered before. This new dataset is called the test dataset. Now on comparing actual results with the one you got from the model, you gain how accurately the machine has learned. There are many types of models and algorithms that can be employed to do this and I have created a short 5-minute video to help you get familiar with the basics, be sure to check the video link in the description. Moving forward, do note that there is a lot of data preparation or pre-processing steps that you need to take care of even before training your model. But ML libraries such as SciKit Learn have evolved so much that even an app developer without any background in mathematics or statistics, or even a formal AI education, can start using these libraries to build, train, test, deploy, and use ML models in the real world. But it always helps to know how these algorithms work, as they will help you to make informed decisions when you are to select an algorithm for your problem statement. With this knowledge of ML, let’s talk a bit about deep learning now. 4. Deep LearningDeep Learning (DL) is an advancement of ML, and is regarded as a subset of Machine learning. And while ML is super powerful for most applications, there are situations where ML leaves a lot to be desired. That is where deep learning steps in. It is generally believed that if your training dataset is relatively small, you go with ML. But if you have huge amounts of data on which you can train a model, and if the data has too many features, along with accuracy being super important in your case, you take the deep learning route. It is also important to note that deep learning requires much powerful hardware to run, that's why, mostly GPUs are used, and it takes significantly more time to train your models, and it is generally more difficult to implement compared to ML. But these are some of the compromises that you have to live with when the problem you’re trying to solve is that much more complex. Also, You might have heard of TensorFlow, which is a neural network that Google is extensively using and pushing to developers. It is the future advancements of deep learning that today we have the vision of building self-driving cars or are even able to use complex tools like google translate that is able to translate big paragraphs of text from one language to another in a matter of milliseconds. With that, I hope this video was helpful to you and served value. If you loved my content, be sure to smash that like button and if you haven't already subscribed to my channel, please do subscribe it keeps me motivated and helps create more content like this for you!

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