What is Machine Learning?

Machine learning is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Machine learning algorithms are often categorized as supervised or unsupervised.

Supervised Machine Learning

Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.

Y = f(X)

The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.

It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance.

Supervised learning problems can be further grouped into regression and classification problems.

• Classification: A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”.

• Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.

Some popular examples of supervised machine learning algorithms are:

• Linear regression for regression problems.
• Random forest for classification and regression problems.
• Support vector machines for classification problems.

Here are some Python Notebook's, Check thus out Kaggle Repository (opens in a new tab)

Unsupervised Machine Learning

In unsupervised learning, only the input data is provided, and the algorithm must find structure in the data. It is called unsupervised learning because unlike supervised learning there is no correct answers and there is no teacher. Algorithms are left to their own devises to find structure in the data.

Unsupervised learning problems can be further grouped into clustering and association problems.

• Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.

• Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.

Some popular examples of unsupervised learning algorithms are:

• k-means for clustering problems.
• Apriori algorithm for association rule learning problems.

Reinforcement Machine Learning

Reinforcement learning is a type of Machine Learning algorithm which allows software agents and machines to automatically determine the ideal behavior within a specific context, in order to maximize its performance. Simple reward feedback is required for the agent to learn its behavior; this is known as the reinforcement signal.

Reinforcement learning is different from supervised learning in that the algorithm isn’t trained on a pre-defined dataset. Instead, the algorithm learns from the consequences of its actions and can be thought of as a trial and error approach.

Reinforcement learning is often used for robotics, gaming, and navigation.

Some popular examples of reinforcement learning algorithms are:

• Q-learning
• Deep Adversarial Networks

Summary

In summary, machine learning is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Machine learning is a subset of artificial intelligence. Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Machine learning is a powerful tool for making predictions based on data.

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.