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Introduction to Machine Learning

Hi guys! I’m Abe from Data Scientist team. Natan (Data Scientist Lead) and I participated in Machine Learning Bootcamp held by Google in Singapore. The course was very exciting and interesting, so I want to share with you guys about the knowledge I learned.

I believe many of you have heard about Machine Learning because it is one of the hottest terms you can find out there. It is very closely related to Artificial Intelligence (AI). At this point, you may think about Doraemon or J.A.R.V.I.S. from Ironman movie, because they were built based on AI. And yeah, these are very intelligent machines. However, the current technology is still very far away to get there. Machines are getting smarter, but they are still not that smart. By the way, JARVIS is also the name of Tokopedia’s Data Scientist team.

So, what is Machine Learning? Machine Learning is a way for machines to learn something without explicitly programmed. As programmers, we can create any programs to do anything we want. Let’s say we want to program a chatbot. An approach to do this is by writing all possible responses of all different inquiries. However, I’m sure that you will think that it is impossible, right? That’s why we need Machine Learning. We need to somehow ‘teach’ the machine to learn proper responses on its own without any specific rules.

In general, AI is classified into 2 different categories:

3 things building ML

So, after understanding all of these things, what do we need to build an ML model? In general, we need to define these 3 things:

Let’s take an example. Suppose that we want to build a model to recognize handwritten texts from an image. The task is obvious, which is to recognize texts from given handwriting images. In terms of performance measure, we can use the percentage of correct prediction as the metric. After that, the model will learn patterns from the given training data (experience), which consist of handwriting images with their corresponding text or word as the label.

Before building a model, we should understand the ML workflow. There are many different variations of workflow and this is just one of the examples. Remember, we need to define 3 things I mentioned previously (task, performance measures, experience) before starting the workflow. In general, there are 4 steps:

Before we start training our model, we should divide the data into train and test data. We train our model using train data, and validate the model using test data. The reason behind this segmentation is that we want to make sure that our model can adapt to any data apart from our train set instead of just remembering the dataset. Usually, the proportion of train and test data is 80:20.

Case Example

To make it clearer, I’ll give you an example. Let’s take a look at the dataset on this table below.

Rent Price Dataset

Suppose that we have a sample house rent pricing problem. Our task is to predict the Rent Price (M IDR) based on Area (m²). Then, we define the performance measure as the average error, and the experience is our training data on the table.

Let’s start our ML workflow.

Linear Regression

Yeah! Finally, we have finished creating a simple ML model (well, at least conceptually)!

Source: Pixabay

By the way, visit these links if you want to delve deeper!

There will be more interesting things coming about Machine Learning. Stay tuned at Tokopedia Data Squad Medium! Thanks for reading!

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