Have you ever wondered how your voice or face can be recognized by a machine, or how it can know what movies you want to watch? Machine Learning (ML) is emerging as one of the hottest fields today. It has penetrated into many aspects of our everyday lives. Let’s begin with what machine learning is.
What is Machine Learning?
Machine learning is a technique in data analytics that teaches computers to do what humans and animals are naturally capable of, which means learning from their experiences. Machine learning algorithms use computational methods that learn information through experience from data without the need to rely on a predetermined model. The algorithms adaptively improve their performance as the amount of learning samples available increases and find patterns in massive amounts of data. Machine learning is powering many of the services we use nowadays—recommendation engines like the ones on Netflix, YouTube, search engines like Google, Amazon.
Machine learning is subcategorized to three types.
The name “supervised” learning originates from the idea that training this type of algorithm can be considered as having a teacher supervising the whole process. They are designed to learn by example. The training set consists of pairs of input and desired output, and the goal is to learn a mapping between input and output spaces. Once it has been trained based on the given data, it can generate a prediction for the outcome of the new data. Supervised learning can be further divided into two subcategories: Classification and regression.
- Classification: Classification is used for predicting discrete responses. Where output is categorized into a specific group. Some examples of classification include spam detection.
- Regression: In a regression problem, we are trying to predict results within a continuous output, which is a real computed value. It is useful to predict the price of a house in a specific city.
Another class of machine learning algorithms is named as unsupervised learning, where the input dataset is not labeled, classified or categorized. Unsupervised learning can instead be used to discover interesting patterns of the data. It is more applicable to real-world problems since you do not know what the outcomes should be and there is no way to determine the accuracy. The most common algorithms used in unsupervised learning include Clustering which allows you to automatically split the dataset into groups according to similarity.
The third category of machine learning techniques is called reinforcement learning. This works in a different way from supervised learning however it still uses a feedback component to improve its performance. The machine is exposed to an environment where it gets trained by trial and error method. The machine learns from previous experience and tries to capture the best possible knowledge to make the right decisions on the basis of the feedback received. Instead of telling the learner what actions to take, he must discover which actions offer the most reward by doing them. An example of a reinforcement problem can be found in learning to play a game against a human component where the agent strives to get a high score and can make moves in the game and gain feedback in terms of penalties or rewards.
Why Is Machine Learning Important?
Recent years have shown that Machine Learning can be used to automate several different tasks that were conceived as tasks that only humans can perform. Machine learning would have tremendous impacts on the economy and on life in general. It is possible to automate entire work activities and change the job market forever. Machine learning as a service allows identifying more quickly profitable opportunities and potential risks. The future of Machine Learning is likely to replace the existing technology style we are seeing in these days.