What is machine learning?
Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that entails collecting large amounts of data and using algorithms to help the machine learn like the human brain. The more the machine “learns,” the more accurate it becomes. The phrase “machine learning” was coined by IBM’s Arthur Samuels in the 1950s. ML is a crucial aspect of the rapidly growing field of data science, where the processing of massive data sets allows computers to make classifications and predictions to develop business insights in data mining projects.
There are several product categories on G2’s website that use ML, which include but are not limited to text analysis software, data science and machine learning platforms, and AI & machine learning operationalization software. In addition to platforms dedicated specifically to solely machine learning, many software also incorporate machine learning into the overall functionality of the tool. For example, medical transcription software converts words to text and talent intelligence software helps HR professionals discover potential candidates during the recruitment process.
Types of machine learning
There are three main types of ML— supervised, unsupervised, and reinforcement.
- Supervised learning: This type of ML uses known information sources to train the data, which is the process by which computers process massive amounts of data through algorithms to learn and make predictions. Once the algorithm and machine learning model is trained on known data sources, unknown sources can be entered into the algorithm to generate new responses. The most commonly used algorithms in supervised learning are polynomial regression, random forest, linear regression, logistic regression, decision trees, K-nearest neighbors, and Naive Bayes.
- Unsupervised learning: In this type of ML, unlabeled data sources that have not been reviewed before are entered into algorithms to train the model. The machine will then seek to find patterns. Alan Turing broke the Enigma machine during World War II using unsupervised learning. The most commonly used algorithms in unsupervised learning are partial least squares, fuzzy clustering, singular value decomposition, k-means clustering, apriori, hierarchical clustering, and principal component analysis.
- Reinforcement learning: Reinforcement learning entails using algorithms that use trial and error in a game-like situation to determine what action yields the highest reward based on the rules of the game. The three components of reinforcement learning are the agent, environment, and actions. The agent is the learner, the environment is the data the agent interacts with, and actions are what the agent does.
Benefits of machine learning
The explosive growth of big data evidences the usefulness of artificial intelligence and machine learning. Below are some of the key benefits of using ML and AI:
- Allows businesses to stay agile and adapt to market changes: ML algorithms allow for the virtually limitless collection of data, which is useful when business decisions need to be made in response to market changes and predictions. An example of this could be better preparing global supply chains when certain geographic regions of business are more impacted by climate change.
- Improves logistics and business functioning: ML can help logistics professionals predict consumer demand, assess stock levels, and make strategic inventory decisions.
- Offers robust user analysis for marketing and targeting: ML algorithms can also help measure the success of marketing campaigns to create recommendations for optimization. In addition, mass analysis of consumer data can help develop more insightful target profiles.
- Assists with medical imaging and diagnosis: The field of bioinformatics uses data science and machine learning to help with medical imaging and diagnosis as well as predicting the risk for future diseases, such as cancer.
Machine learning vs. natural language processing vs. neural networks vs. deep learning
ML is sometimes used interchangeably with deep learning, and it’s also associated with neural networks and natural language processing. It is, however, important to highlight the key distinctions between these concepts.
As mentioned above, ML is a branch of artificial intelligence and computer science. Natural language processing is a discipline within ML that focuses on helping AI learn the natural language of humans, both spoken and written. This field of ML is what helps run chatbots and assistants like Alexa and Siri.
Neural networks are classes of ML algorithms modeled on the human brain. With neural networks, information moves through algorithms like electrical impulses through the brain. Finally, deep learning is a neural network with many layers, and each layer determines the “weight” of each link in the network.
Machine learning discussions on G2
Anthony Orso
Anthony is a Market Research Analyst specializing in supply chain and logistics, as well as data science applications in the industry. Prior to joining G2, Anthony worked in the research and strategy department of advertising. When Anthony isn't studying for his master's program in data science, he enjoys film criticism, true crime, and playing classical music on his violin.
