Machine Learning Explained (A Beginner’s Guide)

‘Machine Learning’ is one the main keywords that has been buzzing around on LinkedIn over the past few years, and everyone wants to know what it’s all about.

To the average person, ‘Machine Learning’ may be more likely to conjure up imagery of a physical, automatic machine that completes repetitive tasks, like the machine that replaced Charlie’s dad in Charlie and the Chocolate Factory.

So, what exactly is Machine Learning?

Machine learning essentially involves humans teaching machines. Familiar examples to you may include computers or cleaning robots, and how they independently learn about the environment that surrounds us and them. In order to ‘learn’, machines look at patterns and differences, which they then use to make ‘guesses’. However, machines require lots of data before becoming increasingly precise with their guesses. Machines build models with the data they are given. Subsequently, they use the model to make predictions.

For example, platforms that offer recommendations to enhance your user experience use Machine Learning models to make predictions based on the data they have gathered from your past experiences. Clever!

One mainstream example could be IKEA having a sale and strategically locating the flash sale items in warehouses near densely populated cities. IKEA would employ Machine Learning to predict the location from where customers are more likely to place orders.

Types of Machine Learning

Let’s take a look at the different ways that humans teach machines to learn by themselves – which isn’t as ‘robots taking over the world’ as it sounds.

We can compare it to the different types of revisions humans do when preparing for exams, from studying by heart the content to creating mental maps.

There are four different types of Machine Learning:

• Supervised Learning
• Unsupervised Learning
• Semi-supervised Learning
• Reinforcement Learning
  

But let’s dive into Supervised Learning.

Supervised Learning

As you can infer from the name, supervised learning happens under the supervision of a data scientist. This person provides the machine with explicit learning data and tells the machine what variable they want the machine to analyse and assess. The data scientist needs thousands of data points to enable the machine to understand and create its own models.

Supervised learning is used for many tasks, such as:

• Dividing into two categories
• Choosing between two answers
• Predicting a continuous model

For example, a data scientist provides the model with 10 thousands of images of cats and dogs and then requires the model to divide the images in two categories.

The data scientists provide the machine with clearly labelled training data of images of cats and dogs, which the machine can then use in order to ‘learn’ and therefore assess the images and divide into the relevant categories.

From easily collecting data or creating data output based on historical data to optimising performance criteria, Supervised Learning presents loads of benefits. On the other hand, multiple companies struggle to implement supervised learning. From hiring and training costs of data scientists to collecting enough data, supervised learning is not for the faint hearted.

This is where outsourcing to experts comes in handy. TUBR makes Machine Learning accessible to different types of companies with different types of budgets. It cuts down numerous costs, from hiring and training costs of data scientists to data collection as TUBR only needs 20% of the entire dataset in less than a month.

Now, doesn’t that sound efficient?

Why Do We Need Machine Learning?

From a corporate perspective, there are numerous reasons why Machine Learning is important to the daily operations of a business. Machine Learning can give companies insight into future demand in order to effectively manage their assets, reduce waste and drive customer satisfaction and loyalty.

Machine Learning also allows the company to make predictions such as:

• Predicting supply and demand at a brick and mortar shop
• Predicting the staff needed to deliver a service
• Predicting how many assets will be needed to deliver a service

Finally, Machine Learning allows the company to process automation on repetitive tasks so that customers can focus on tasks that add value to the business.

Machine Learning In Business: A Case For Active Mobility

Machine Learning can bring numerous benefits to various industries. One of them is active mobility. Among the many challenges that affect this industry, an efficient use of resources is one of the most predominant one. From ensuring that operation and environmental costs are minimised, the active mobility industry is a perfect playing field for Machine Learning.

Recently, shared electric bikes, such as Line or Santander bikes, have become popular among commuters, bringing numerous environmental and socioeconomic benefits. However, the location of electric bikes and improving their usage is a major challenge for city councils and manufacturers.

In this scenario, a time-series Machine Learning approach could be implemented to predict the usage and the location of parking spaces to meet the demands of as many people as possible.

Think time-series Machine Learning could have an impact on your business? TUBR is here to help.