Introduction
Machine learning is part of a larger field called artificial intelligence (AI), which is the study of making computers do things that require human intelligence, such as recognizing objects and speaking. AI has been around since the days of Alan Turing and other pioneers, but it wasn’t until recently that computers started being able to “think” like humans. Machine learning algorithms are one type of AI that can be programmed to learn from data in order to improve its performance over time—they’re what make services like Amazon Alexa smart enough to understand a question or Google search smart enough to find an answer for you.
Supervised Learning
Supervised learning is a type of machine learning that uses labeled data to learn from. It’s called supervised because we have the answers, or labels, as well as an expectation on what the right answer should be. We can use these labeled examples to train our model and make predictions based on new data points.
Supervised learning problems are categorized into regression and classification problems:
- Regression – Regression aims to predict continuous values (for example: house prices), while classification attempts to categorize items into one of two or more categories (for instance: whether someone is healthy or not).
Unsupervised Learning
In unsupervised learning, the algorithm is presented with input data and it learns from it without any teacher. Unsupervised learning is used to analyze and make sense of data without being given a specific result or answer. The algorithm learns about the structure within a set of unlabeled examples and uses this knowledge to classify future examples into categories based on their similarities.
Unsupervised machine learning algorithms include clustering algorithms (such as k-means) and dimensionality reduction techniques such as principal component analysis (PCA).
Reinforcement Learning
Reinforcement learning is a type of machine learning that learns by trial and error. It’s used in robotics, gaming and other areas where the system needs to learn how to perform tasks by interacting with its environment.
In reinforcement learning, the agent (the robot) is given rewards for performing certain actions. The algorithm then uses this information to determine which actions should be taken in future situations based on their consequences (rewards).
Semi-Supervised Learning
Semi-supervised learning is a machine learning algorithm that uses both labeled and unlabeled data to train a model. It’s useful for situations where you have a small amount of labeled data (e.g., 10{6f258d09c8f40db517fd593714b0f1e1849617172a4381e4955c3e4e87edc1af} or less), but can still use the rest of your dataset to help train your model.
For example, imagine you’re training a classifier for breast cancer detection using medical imaging scans that have been manually annotated by doctors as either containing or not containing breast cancer lesions. Since there are only so many images available with these annotations, it may be difficult to build an accurate classifier using only these images alone–you’d need thousands more samples with no lesions at all! Instead, semi-supervised learning allows us access both types of images so we can create better models overall:
Active Learning
Active learning is a machine learning technique in which the algorithm chooses which examples to ask the user to label. Active learning is used to improve the accuracy of the model by reducing the number of instances that need to be labeled. Active learning is useful when you cannot know ahead of time what data you will need for training your model, or where those data points are located within your dataset.
The goal of active learning is not only to obtain better performance on unseen test sets but also reduce human effort required for labeling data points by automating this process as much as possible
Different types of machine learning algorithms work in different ways.
Different types of machine learning algorithms work in different ways.
- Algorithms are used to classify, recommend and search (for example, Netflix uses machine learning to determine what movies you might enjoy).
- There are many types of algorithms: some are supervised, others unsupervised; some use reinforcement learning while others use semi-supervised techniques.
Conclusion
Machine learning is one of the most exciting fields in computer science today. It has the potential to revolutionize how we interact with computers and make our lives easier, but it also raises many ethical questions about privacy and security. In this article, we’ve explored some of those issues as well as the different types of machine learning algorithms used by companies like Google and Facebook to power their services–and hopefully given you some insight into what goes on behind the scenes when you use one!
