A subfield of artificial intelligence, machine learning (ML) uses specific algorithms to analyze datasets and predict outcomes. These algorithms are known as machine learning algorithms.
Initially, AI made use of rule-based algorithms – strict systems that used information that was already hard-coded into the system. It wasn’t a flexible approach and could not learn and adapt to new information thrown at it. More recently, the dynamics have shifted towards an ML-based approach, where the algorithm learns and generates an output that is best fitted for that particular dataset. It can turn a dataset into a model, which can be used to analyze similar datasets.
Types of Algorithms:
- Supervised learning: The algorithm is fed a specific dataset and expected output. The algorithm must figure out how to arrive at the said output. The subtypes include Classification, Regression, and Forecasting.
- Semi-supervised learning: The algorithm analyzes both labeled and unlabeled data. The labeled data consists of tags so that the algorithm can understand it. By doing so, eventually, it can explore and label the unlabeled data too.
- Unsupervised learning: In this case, the algorithm does the heavy lifting. It’ll analyze what patterns emerge from the dataset and will group them accordingly. The subtypes include Clustering and Dimension Reduction.
- Reinforcement: The algorithm is fed a specific set of actions, parameters, and end values. Based on this information, it analyzes the data and identifies different outputs, and generates the most optimal one for the specific scenario. Eventually, it learns to do that for any dataset – and helps achieve a result best suited to that scenario.
Other Definitions of Machine Learning Algorithms:
- “Programmed algorithms that receive and analyze input data to predict output values within an acceptable range.” (SAS)
- “Computer programs that adapt and evolve based on the data they process to produce predetermined outcomes. They are essentially mathematical models that ‘learn’ by being fed data – often referred to as training data.” (Arm)
- “The method by which the AI system conducts its task, generally predicting output values from given input data. The two main processes of machine learning algorithms are classification and regression.” (TechTarget)
Use Cases Include:
- Spam filtering: Machine learning algorithms can be trained to identify specific trigger words, email domains, attachments, and more. Once algorithms are trained to identify these datasets, they can filter emails and mark them as spam – after which, it’s up to the discretion of the user to approve these emails.
- Fraud detection: Machine learning algorithms can understand what kind of patterns occur when common fraud campaigns are ongoing. For example, if unusual activity is noticed in a specific bank account, they can flag that behavior and enable a temporary hold on the account.
- Customer segmentation: Machine learning algorithms can help e-commerce platforms understand their customers’ behaviors, needs, and expectations. Based on that, they can segment the audience and provide personalized recommendations. In addition, they can also identify opportunities to target potential customers.
Benefits of Machine Learning Algorithms:
- Can be trained to identify common errors and inaccuracies in documents when they’re uploaded into the system
- Can help businesses make accurate and real-time decisions by analyzing data quickly
- Can be trained to model any dataset and generate the necessary output – making it beneficial to any industry
- Can continuously learn and adapt to any kind of dataset, resulting in a more precise output
- While initially, humans need to feed the dataset and train the model – over time, it can work without any human intervention
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