## Dealing with data

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 end up spending the main chunk of your time collecting and curating data. In this […]

## ROC & AUC

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 gets colder. Being the awesome data nerd you are, you start collecting data and see […]

## Classification with logistic regression

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 output between 0 and 1, helping us assign them into specific classes. Let’s continue to […]

## MAE vs MSE vs RMSE

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 them as simply as possible, to the best of my own understanding. MAE – Mean […]

## The maths behind multiple linear regression

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 house price, added the bias (b) and got our prediction. Now, given that we have […]

## Multiple linear regression

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 than one independent variable to deal with. In a situation where we use linear regression […]

## The maths behind simple linear regression

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 to map out the relationship between the price (dependent variable) and the area of the […]

## Our first piece of code

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 set up the interface as per your preference. Personally, I have the “Variable explorer” and […]

## A simple example

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: Land size sqm Price 326 \$446,000 346 \$451,500 371 \$463,500 400 \$530,000 407 \$526,000 448 […]

## Setting things up

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 by step instructions. You can find those elsewhere on the internet. First of all download […]

## The toolset

This part was especially confusing. I was hearing and reading about quite a few names out there. Tensorflow Keras PyTorch Theano Scikit-learn Numpy R Azure ML cloud iPython notebook / Jupyter notebook Surely I am missing a few names. It was hard for me to narrow down what exactly did I need to get going. […]

## Why is linear algebra important?

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 and practice it. However, in my later years when I came across linear algebra, it […]

## But really .. what is machine learning?

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 the final selling price, this influence is what we call the weight. Our machine learning […]

## So what is machine learning?

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 ML systems. AI vs ML vs DL First off, there are a few terms being […]

## What to expect from machine learning?

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 would get. This led to a bit of insecurity and FOMO. I counter-acted for this […]

## Introduction

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 I am making progress. The people I know who work in this domain are super […]