So you’ve heard about Machine Learning and AI and want to know more, huh? Don’t worry, you’ve come to the right place. This guide is for anyone who wants a quick intro to machine learning, from beginners with no technical background to programmers looking to expand their skill set. We’ll explain what machine learning is, why it’s important, and how it works.
Along the way, we’ll cover basic concepts and terms, real-world examples of machine learning in action, and resources to help you start building your machine-learning models.
What Is Machine Learning?
So what exactly is machine learning? In simple terms, machine learning is the field of study that focuses on computer algorithms that can learn and improve from experience without being explicitly programmed. It’s a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.
Some common types of machine learning algorithms include:
- Supervised learning: The algorithm learns from labeled examples in the data. It finds patterns that map the input data to the labeled outputs. Examples are classification, regression, and decision tree learning.
- Unsupervised learning: The algorithm finds hidden patterns or clusters in unlabeled data. It explores the data and finds natural groupings and patterns. Examples are clustering, dimensionality reduction, and association rule learning.
- Reinforcement learning: The algorithm learns from interactions by trial and error using feedback from the environment. Examples are Markov decision processes, temporal difference learning, and Q-learning.
- Deep learning: A type of machine learning that uses neural networks with many processing layers. It’s beneficial for finding complex patterns in large amounts of data. Examples are deep neural networks, convolutional neural networks, and recurrent neural networks.
The possibilities for applying machine learning seem endless. It has the potential to improve and optimize so many areas of life and society. But with great power comes great responsibility, so we must be thoughtful and intentional about developing and applying this technology.
How Machine Learning Algorithms Works
Machine learning algorithms are exposed to large amounts of training data and use statistical techniques to identify patterns in the data. As the algorithms are exposed to more data, they learn and their predictions become more accurate.
There are three basic steps in a machine learning process;
- Gathering data; The first step is to gather the data that will be used to train the machine learning model. The quality and quantity of the training data have a huge impact on the accuracy of the model.
- Training the model; The training data is fed into the chosen machine learning algorithm. The algorithm finds patterns in the data and creates a model. The goal of training the model is to optimize some parameters to make the most accurate predictions possible.
- Making predictions; Once the model has been trained, it can be used to make predictions on new data. The model uses the patterns identified in the training data to make predictions. The more data the model is exposed to during training, the more accurate it can become at making predictions on new data.
The three most common types of machine learning are:
- Supervised learning; The algorithm learns from labeled examples in the training data. It uses those examples to make predictions on new data. Examples include classification and regression.
- Unsupervised learning; The algorithm finds patterns in unlabeled data. It explores the data and finds natural clusters and patterns. Examples include clustering, dimensionality reduction, and association rule learning.
- Reinforcement learning; The algorithm learns from trial-and-error interactions with a dynamic environment. The algorithm is exposed to an environment and learns a sequence of actions to maximize some reward. Reinforcement learning is used for things like game playing and robotics.
Learn More About Machine by clicking here, What is Machine Learning; The Ultimate Starter Guide on ML
