Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on developing methods for computers to learn and improve their performance. It aims to replicate human learning processes, leading to gradual improvements in accuracy for specific tasks. The main goals of ML are: <\/p>\n
Machine learning has a wide range of applications, including language translation, consumer preference predictions, and medical diagnoses <\/p>\n<\/p>\n
Machine learning is a set of methods that computer scientists use to train computers how to learn. Instead of giving precise instructions by programming them, they give them a problem to solve and lots of examples (i.e., combinations of problem-solution) to learn from. <\/p>\n
For example, a computer may be given the task of identifying photos of cats and photos of trucks. For humans, this is a simple task, but if we had to make an exhaustive list of all the different characteristics of cats and trucks so that a computer could recognize them, it would be very hard. Similarly, if we had to trace all the mental steps we take to complete this task, it would also be difficult (this is an automatic process for adults, so we would likely miss some step or piece of information). <\/p>\n
Instead, ML teaches a computer in a way similar to how toddlers learn: by showing the computer a vast amount of pictures labeled as cat or truck, the computer learns to recognize the relevant features that constitute a cat or a truck. From that point onwards, the computer can recognize trucks and cats from photos it has never seen before (i.e., photos that were not used to train the computer). <\/p>\n
Performing machine learning involves a series of steps: <\/p>\n
It is important to keep in mind that ML implementation goes through an iterative cycle of building, training, and deploying a machine learning model: each step of the entire ML cycle is revisited until the model has gone through enough iterations to learn from the data. The goal is to obtain a model that can perform equally well on new data. <\/p>\n
Data is any type of information that can serve as input for a computer, while an algorithm is the mathematical or computational process that the computer follows to process the data, learn, and create the machine learning model. In other words, data and algorithms combined through training make up the machine learning model. <\/p>\n
Machine learning models are created by training algorithms on large datasets.There are three main approaches or frameworks for how a model learns from the training data: <\/p>\n
Algorithms provide the methods for supervised, unsupervised, and reinforcement learning. In other words, they dictate how exactly models learn from data, make predictions or classifications, or discover patterns within each learning approach. <\/p>\n
Finding the right algorithm is to some extent a trial-and-error process, but it also depends on the type of data available, the insights you want to to get from the data, and the end goal of the machine learning task (e.g., classification or prediction). For example, a linear regression algorithm is primarily used in supervised learning for predictive modeling, such as predicting house prices or estimating the amount of rainfall. <\/p>\n
Machine learning and deep learning are both subfields of artificial intelligence. However, deep learning is in fact a subfield of machine learning. The main difference between the two is how the algorithm learns: <\/p>\n
In other words, we can think of deep learning as an improvement on machine learning because it can work with all types of data and reduces human dependency. <\/p>\n<\/p>\n
Machine learning is a powerful problem-solving tool. However, it also has its limitations. Listed below are the main advantages and current challenges of machine learning: <\/p>\n
If you want to know more about ChatGPT, AI tools, fallacies, and research bias, make sure to check out some of our other articles with explanations and examples. <\/p>\n
Although the terms artificial intelligence and machine learning are often used interchangeably, they are distinct (but related) concepts: <\/p>\n
In other words, machine learning is a specific approach or technique used to achieve the overarching goal of AI to build intelligent systems. <\/p>\n
Traditional programming and machine learning are essentially different approaches to problem-solving. <\/p>\n
In traditional programming, a programmer manually provides specific instructions to the computer based on their understanding and analysis of the problem. If the data or the problem changes, the programmer needs to manually update the code. <\/p>\n
In contrast, in machine learning the process is automated: we feed data to a computer and it comes up with a solution (i.e. a model) without being explicitly instructed on how to do this. Because the ML model learns by itself, it can handle new data or new scenarios. <\/p>\n
Overall, traditional programming is a more fixed approach where the programmer designs the solution explicitly, while ML is a more flexible and adaptive approach where the ML model learns from data to generate a solution. <\/p>\n
A real-life application of machine learning is an email spam filter. To create such a filter, we would collect data consisting of various email messages and features (subject line, sender information, etc.) which we would label as spam or not spam. We would then train the model to recognize which features are associated with spam emails. In this way, the ML model would be able to classify any incoming emails as either unwanted or legitimate. <\/p>\n
We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below. <\/p>\n
Nikolopoulou, K. (2023, August 04). What Is Machine Learning? | A Beginner's Guide. Scribbr. Retrieved November 14, 2023, from https:\/\/www.scribbr.com\/ai-tools\/machine-learning\/<\/a> <\/p>\n Theobald, O. (2021). Machine Learning for Absolute Beginners: A Plain English Introduction (3rd Edition). <\/p>\n <\/p>\n