7 Steps to Understanding Machine Learning
Did you know that Machine Learning is behind a task that you probably do every day?
It can be used to separate spam from emails or correct grammar and spelling mistakes (autocorrect). Because of how brilliant Machine Learning is, we can use it for tasks such as: detecting fake news, self-driving cars, chatbots on websites, and image and object recognition.
People talk about it in the tech world all the time, but not everyone knows exactly how it works. It’s not uncommon for people to confuse Artificial Intelligence (AI) and Machine Learning (ML) as the same thing; however, they are very different. ML is a subset of AI and allows computers to learn without explicitly being programmed. Let’s break it down even further; An “intelligent” computer uses AI to think like humans and perform tasks independently. Machine learning is how a computer system develops its intelligence.
Machine Learning is a topic that can be daunting and seemingly difficult to grasp; however, it’s pretty straightforward! Let’s dive deep into seven steps to understand Machine Learning better…
1. Collecting Data
It seems obvious, but machines learn from the data we provide to them! The quality of the data provided is the most important because that will determine how accurate the model is. If the information we collect is outdated or incorrect, there could be wrong outcomes. Remember that data should come from a reliable source, containing no repeated or missing values, and adequately representing the categories present!
2. Preparing the Data
Let’s say we have collected some meaningful data. The next stage is preparation.
First, we need to place the data together and randomise it. This will help ensure that all data is evenly distributed and that a specific order won’t impact the outcome.
Next, we “clean” our data! Essentially, cleaning means removing any unwanted data, missing values, rows, columns etc.
After this, we visualise the data to understand its structure and attempt to understand any relationships or correlations between variables present.
Lastly, we split the data into two sets — a Training set and a Testing set. The Training set is where our model will learn from, and the Testing set will be used to check the model’s accuracy after the training.
3. Deciding on a Model
Still with me? Good! Our next stage is deciding on a model. This will determine the output after running an algorithm on the data. (An algorithm is a set of instructions for solving a problem or accomplishing a task, similar to following a recipe in a cookbook!) Hundreds of models are suited for various tasks and situations; image recognition, prediction, speech recognition, etc. We need to choose a model that’s relevant to our problem and can handle the type of data we have in our dataset.
4. Instructing the Model
Once we select our model, we move to the most critical step in ML — training. In this step, we pass the prepared data to our ML model to find new patterns and make predictions. Over time, with more consistent training, the model will get better at predicting!
5. Evaluating the Model
After training, we need to see how it’s performing! This is done by testing the model's performance on other unused datasets (e.g. the Testing dataset from step 2!). The Training dataset should not be tested again because the model will already be good at finding those patterns and will give us a higher accuracy than it actually has!
6. Parameter Tuning
Parameters are the configuration variables within a model. Once we have evaluated our model, these parameters are used to see if the accuracy can be improved in any way. Let’s think of parameters as tuning pegs on a guitar, we can fine-tune the guitar (model) to get it pitch-perfect (100% accurate)!
7. Making Predictions
Finally, we can use our model on new datasets collected to make accurate predictions!
Not too complicated, right? That’s how easy it is to start using Machine Learning. We hope you enjoyed reading about how to get started and that ML is for everyone, not just data scientists!
