What is Machine Learning?

Machine learning focuses on applications that learn from experience and improve their decision-making or predictive accuracy over time.

In this article I will explain what is Machine Learning?, where it is used, types of machine learning and much more.

Machine Learning is an application of artificial intelligence that provides system ability to learn and improve from experience without being explicitly programmed. Machine learning is used to develop computer programs that can access data and use the data to learn for themselves.

Machine-learning algorithms use statistics to find patterns in massive amounts of data. A Machine Learning system learns from historical data, builds the prediction models, and whenever it receives new data, predicts the output for it. The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately.

Below are some most trending real-world applications of Machine Learning.

1. Image Recognition
2. Speech Recognition
3. Product recommendations
4. Self-driving cars
5. Email Spam Filtering etc.
6. Medical Diagnosis

Classification of Machine Learning

Machine learning can be classified into three types:

1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning

In supervised learning, the most prevalent, the data is labeled to tell the machine exactly what patterns it should look for. It can be compared to learning which takes place in the presence of a supervisor or a teacher. A supervised learning algorithm learns from training data, helps you to predict outcomes for unforeseen data.

Unsupervised learning is where you only have input data and don’t know about corresponding output variables. The goal of the unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the given data. Unlike supervised learning above there is no correct answers and there is no teacher.

Reinforcement Learning’s main concept is about taking suitable actions to maximize reward in a particular situation. Reinforcement learning is where a system learns by being ‘rewarded’ for good decisions. These rewards reinforce the right decisions and behaviours, so the machine repeats them next time. Gradually, reinforcement learning allows machines to find the best possible decision or action to take in each situation. Rather than being spoon-fed the correct course of action, reinforcement learning allows machines to learn by trial and error.

Check my other post to learn more about machine learning

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