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Introduction to Machine Learning


What is Machine Learning?

How about having a nap on the driving seat while driving through a busy city? If I said this a decade ago, you might think I’m insane or talking about a scene from a sci-fi movie. But, now autonomous vehicle technology makes it possible to run driverless cars on roads and machine learning plays a major role in AI systems to power driverless cars. So what is machine learning? How it became a buzzword in the tech world? In 1959, Arthur Samuel, a computer scientist, first coined the term machine learning.

Many people use terms of machine learning and artificial intelligence interchangeably to refer to the same technology. But, as a subfield of artificial intelligence, machine learning has some distinctive features. It allows systems to autonomously learn from real-world experience without being explicitly programmed, then machines can act as humans do. Earlier, machine learning had used pattern recognition methods to identify patterns and regularities in data. But after big data became a technological breakthrough, a large amount of data fed to the systems and the machine learning applications were able to learn more from that data. In fact, without big data, machine learning cannot reach its expected goals and by interacting with these complex datasets, machine learning systems can do more accurate predictions for businesses.

One of the most common uses of machine learning is recommendation engines. You might have experienced the output of recommendation engines without even knowing the technology behind that. For instance, when you are doing online shopping, you do many searching before buying a product and this process generates a huge amount of data (big data). Based on that data, recommendation engines suggest other similar products and in the future, you may get email newsletters regarding the products you have searched with some offers, discounts, etc. Machine learning has many other important uses including face detection or image recognition in social media platforms, voice recognition by translating spoken words into the text, fraud detection in online transactions, filter email spams and malware, etc.


Machine learning methods

Machine learning methods can be categorized into two main parts, namely supervised learning and unsupervised learning. Though these two methods are being widely used, semi-supervised learning and reinforcement learning are also used as machine learning algorithms.

Supervised learning

The supervised learning approach is similar to students learning under the supervision of a teacher. For instance, imagine a teacher explaining about animals and labeling pictures of animals with names. Later, when students see an animal, they are able to identify the animal, because they have already learned about it. But actual supervised learning algorithms are complex than that because it does predictions based on more parameters. In supervised learning algorithms, models are trained using labeled datasets which consists of both input and output parameters. When the new data arrives, models can make predictions based on past examples. Supervised learning problems can be further divided as classification and regression.

Classification – This technique is used when the output is having discrete values or a finite set of outcomes such as 0 /1, true/false, spam/non-spam, etc. The binary classification algorithm is a widely used technique in solving problems. For instance, binary classification is used for spam detection in emails, Naive Bayes classifier is the most popular binary classification algorithm for spam filtering. Multi-class classification algorithm can be deployed if there are more than two outcomes. Some algorithms used in classification include decision trees, k-nearest neighbor, Naïve Bayes, neural networks, support vector machines, etc.

Regression – If the predicting output has continuous values, the regression technique is suitable to get the outcome. Predictions in regression come as quantity including temperature, wind speed, price, etc. Values of these results often come in real numbers, therefore regression technique is ideal for that. Common regression algorithms are including linear regression, logistic regression, polynomial regression, support vector regression, Random forest regression, etc.

Unsupervised learning

In unsupervised learning, algorithms are dealing with unlabeled data and therefore, data processing is more complex than the supervised learning. Since the capacity of unlabeled data is also much higher than the labeled data, it’s difficult for a human to recognize patterns and structures in that datasets. In this approach, algorithms cannot figure out the correct output, but it tries to find underlying patterns and structures of a particular dataset by grouping data according to resemblances. But, it cannot label the data as in supervised learning. Unsupervised learning techniques can be used for marketing campaigns because it works well with consumer data. For instance, by analyzing consumer data, unsupervised learning algorithms can group the selling patterns of products based on consumers’ attributes such as age, income level, location, etc. Then marketing campaigns can be launched for those products by targeting the specific consumer segments in order to increase the sale. Some of the popular unsupervised learning algorithms including clustering, anomaly detection, neural networks, latent variable models, etc.

Semi-supervised learning

Simply put, semi-supervised learning is a blend of supervised learning and unsupervised learning. Since unlabeled data is abundant and inexpensive, this method uses a large volume of unlabeled data with a small volume of labeled data. This combination will increase the accuracy of learning than other methods. In supervised learning, data scientists have to label the data in datasets. But, in this method, data scientists use the unsupervised learning algorithm to classify similar data to clusters and then get labeled all the unlabeled data using the labeled data. Semi-supervised learning is widely used in areas such as speech analysis, web content classification, protein sequence classification, etc.

Reinforcement learning

Reinforcement learning has the same approach as to how humans learn by trial and error. In this method, algorithms don’t use training data and it’s learning through the experience to find solutions. Reinforcement learning algorithms consist of two components, the agent and the environment. The agent interacts with the environment and if it performs correctly, it will receive rewards, otherwise receives penalties for failures. The agent should continually try to perform the right actions that receive the maximum rewards in order to efficiently achieve the target. For instance, nowadays self-driving cars use for transportation purposes, if reinforcement learning algorithm apply to perform this task, the car act as an agent and it interacts with the surrounding environment. The algorithm has to make sure that, the car should safely reach the planned destination by adding rewards and penalties accordingly. Some common usage of reinforcement learning including video games, robotics, web system configuration, etc.


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