Welcome

Artificial intelligence always fascinated me as a domain. I however never gathered enough courage to dive into it until recently. After quite a few false starts over the years, I have recently gotten to a point where it feels like ...
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Don't be scared! The tech sector has a tendency to over-hype things at times. A few years ago, I started noticing that every job listing or start-up would make sure to add the machine learning buzz words every chance they ...
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Everyone is pretty hyped up about machine learning these days and I was pretty intimidated by these magical systems. Mr Clarke puts this succinctly "Any sufficiently advanced technology is indistinguishable from magic". Let's try to get a high-level understanding of ...
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Now that you have a high-level view, let's try to see what is happening behind the scene in simple terms. Let's continue with the previous supervised learning system example of the house prices. Each feature in our training set influences ...
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As a kid I always liked algebra, I found it a lot of fun. Probably the best part about algebra was that I could see it's utility very clearly in everyday life and that was a driver enough to learn ...
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This part was especially confusing. I was hearing and reading about quite a few names out there. TensorflowKerasPyTorchTheanoScikit-learnNumpyRAzure ML cloudiPython notebook / Jupyter notebook Surely I am missing a few names. It was hard for me to narrow down what ...
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Surely, as someone interested in data, you already have Excel on your computer or know how to use Google worksheet. Let's set up your coding environment now. I am just going to give you the general idea, not detailed step ...
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Let's continue with our example of house prices and see if we can clearly see the case for machine learning. As an example, we will try to predict the price of a 475 sqm house. Here is my sample data: ...
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Let's try to take our Excel data about house prices and get machine learning to make some predictions for us, using linear regression. Let's get Spyder going! Setting the environment After you see Spyder loaded up, take your time to ...
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Now that you have seen linear regression in action using both Excel and Python, let's try to figure out the maths behind it. In simple terms, linear regression finds a linear relationship between variables. In our case, we used it ...
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Now that we have seen simple linear regression in action, along with its maths, it's time to move on to a bit more advanced stuff. In a real-world scenario, in most cases, you would encounter situations where you have more ...
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We looked at the maths behind the simple linear regression, if you grasped it well, this part would be trivial. Using the line formula y = b + mx, we figured out the slope (m) and multiplied that with the ...
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Earlier when working with multiple linear regression, we encountered 3 different types of error at the end of the script's output. These errors help us ascertain the accuracy of the predictions across the test set. I will try to explain ...
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Linear regression is great for finding continuous dependent variables but what if we wanted to classify the data in categories or classes instead? When we come across this type of situation, we can look at logistic regression. which limits the ...
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We saw some new things in the logistic regression example, let's try to understand them a bit more. Sigmoid or s-curve Let's start with the S-curve. Imagine that your car battery has been giving you trouble, especially when the weather ...
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So far, we have been using small, well-structured data in our examples. However, the real world is messy and we need to spend a significant amount of effort to make the data usable. In fact, don't be surprised if you ...
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