What is Machine Learning?

It’s 2021 and machine learning is one of the things that is creating meteoric and exciting changes across all levels of society. Machine learning is a modern software development technique and a type of artificial intelligence. It enables computers to solve problems by using examples of real-world data and it allows computers to automatically learn and improve from experience without being specifically programmed to do so.

  • It is the engine behind the recent advancements in industries such as driverless vehicles.
  • It allows for more accurate and rapid translation of the text into hundreds of languages.
  • It powers the AI assistants you might find in your home.
  • It can help improve worker safety.
  • It can speed up drug design.

Machine Learning (ML) is how components learn from data to discover patterns and make predictions. In this we do all this with several different techniques:

  • Supervised Learning: It is a type of ML technique in which every training sample from the dataset has a corresponding label or output value associated with it. In short, it is a type of ML technique in which the machine knows something beforehand. As a result, the algorithm learns to predict output values.
  • Unsupervised Learning: In this technique, there are no labels for the training data and all machine knows is that there is data in front of it. A machine learning algorithm tries to learn the patterns or distributions that govern the data.
  • Reinforcement Learning: It is another type of learning technique that takes a different approach. This machine learns through the consequences of action in an environment. The algorithm figures out which actions to take in a situation to maximize a reward (in the form of a number) on the way to reaching a specific goal.

Traditional Problem Solving VS Machine learning

Credit to Udacity

In traditional problem solving with software. A person analyses a problem and creates a solution in code to solve that problem. For many real-world examples, this process takes a lot of time and might even be impossible because a correct solution needs to solve numerous edge cases.

In machine learning, We have a flexible component called a model. And it uses a special program called the model training algorithm to adjust the model to real-world data. The result is a trained model which can be used to predict outcomes that are not part of the dataset used to train it. In a way, machine learning automates some of this statistical reasoning and pattern matching that the problem-solver would traditionally do. The overall goal is to use a model created by a model training algorithm to generate predictions or find patterns in data that can be used to solve a problem. The flexibility of the model is the key here.

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