Machine Learning Overview: Everything You Need to Know

In recent years machine learning is gaining more and more popularity, but what exactly is it?

Evolution of Machine Learning

The name “machine learning” initially originated from famous gaming researcher Arthur Lee Samuel. Samuel is the first person to bring self-learning programs into society.

This remarkable discovery shortly laid the foundation for machine learning algorithms. In later years, rising popularity in artificial intelligence give birth to many innovations in the field of Computers and Automation.

However, similar definitions and usage of ML & AI created ambiguity in distinguishing these two fields. In fact, few beginners in this field often use AI and ML interchangeably, but the fact is that they are the same. Artificial Intelligence is the integration of machine learning algorithms.

Artificial Intelligence models are used to perform multiple tasks such as Self driving cars, Humanoid Robots. On the other hand, machine learning is used to accomplish only specific tasks like spam detection, Movie recommendation, and Image classification.

Actually, it is a subfield of AI, the picture below clearly explains what I mean:

Machine learning is broadly segmented into three types:

Supervised
Unsupervised
Reinforcement Learning

Supervised Machine Learning

Supervised machine learning is the most commonly used technique. Many industries use it to train machine learning algorithms.

With this type, we supervise or teach the machine using labeled data. In other words, we show the sample data and tell the machine what the label is, for every sample in the data set.

Figure 1 clearly explains the working of supervised machine learning.

In figure 1, the data set consists of ‘n’ labelled cat and dog images, each image is labeled with a tag. Image 1 is labeled as a cat. Likewise, there will be ‘n’ labeled images from 1 to n.

In supervised learning, the teacher holds the actual values for every corresponding image in the data set. Similarly, the learning system will give predicted values for every corresponding image in the data set.

Once we get the image output values from the teacher, the learning system error function will calculate the error between actual and predicted values.

Using the feedback error, the learning system will keep on updating its parameters (weights) to minimize the error value. Eventually, this process of learning parameters (weights) will help the system to understand the model.

Unsupervised Machine Learning

In contrast to supervised, unsupervised machine learning doesn’t have any monitoring or teaching system. We let the machine figure out its own model from unlabeled data.

Like figure 1, in figure 2 the data set consists of ‘n’ images of Cats and dogs, but now this image is unlabeled (we don’t mention their tags to learning system). Now, Image 1 is unlabeled and may be cat or dog. Likewise, there will be ‘n’ unlabeled images from 1 to n.

To tackle this unsupervised, the learning system uses the concept of clustering, or a grouping technique in which similar featured objects are grouped together.

In figure 2, we have two different categories (cats vs dogs) and, accordingly, we design our learning system capable of clustering into two groups of images separately. We achieve this by the concept of feature selection, where similar images are grouped together and dissimilar images group together.

Reinforcement Learning

Reinforcement learning is a special type of machine learning especially used for game theory, signals and system, and control systems.

In reinforcement learning, whenever the environment is sending the information to the learning system, its output values and critic will comment on those values. For instance, if the output is appropriate for the machine, it will reward the learning system to perform the next task through actions.

However, if the output is erroneous it punishes learning system and sends a single through actions to repeat the same task with the different data point.

Likewise, it checks every point in the environment to get the reward from the critic, this process of validating every single point will consume huge memory, time and require high computation speed.

This is a major drawback of reinforcement learning.

However, machine learning researches are still working on it to compensate for these issues and to create much more cost-efficient systems. Hopefully in the near future we able to see one.

Most Machine Learning algorithms fall under these categories, but current advancement in machine learning created many more different algorithms among them Semi-Supervised Learning is worth of mentioning.

Why and Where We Use It

According to Forbes, machine learning is going to a common buzzword in Medical, Finance, Transportation, E-commerce and many more sectors. But why are popular companies focusing on it?

Have you ever thought about how Netflix is able to recommend the movies you are interested in? The simple answer is machine learning classification algorithms.

Likewise, there are a lot of applications of these algorithms in different fields: