Machine learning (ML) is a subfield of artificial intelligence (AI) in which computers learn from data and make predictions without being explicitly programmed. Typically, data that has previously been collected is used to train a machine learning model. ML uses algorithms to identify patterns and then applies those patterns to future datasets. Big data—the ability to collect and transform large amounts of data—is a key driver of modern machine learning.
The rapid progress and widespread adoption of artificial intelligence techniques has made way for multiple subfields of ML, including a more intensive version known as deep learning. Deep learning uses millions of data points while machine learning uses thousands. This evolution requires more time for training but results in greater versatility and capability, powering the most human-like AI.
Deep learning excels at automatically learning complex patterns and representations from data. It has gained significant attention and success in tasks like image and speech recognition. The primary objective of deep learning is to automatically learn hierarchical representations of data for classification, regression or feature extraction.
Machine learning begins with relevant raw data collection. The data is then prepared for machine learning, which may include handling missing values, normalizing or scaling data, and labeling. The next step is choosing the right ML model, among the multiple options researchers have developed. It’s important to make sure the right algorithm is used for the task at hand. Machine learning models often share a common underlying mathematic representation, typically based on matrix algebra.
After the model is selected, the training data is presented to the model, allowing it to learn the underlying patterns. Sometimes compute-intensive processing of the entire training dataset must occur multiple times, not just once. The goal is for the model to make correct predictions as often as possible. If the model performs well, it can be deployed in a real-world application to make predictions based on new incoming data.
Machine learning can be broken down into two main techniques: supervised learning and unsupervised learning.
- Supervised learning: In supervised learning, the algorithm is trained on a labeled dataset that corresponds with a target output. The goal is to develop a mathematical or computational relationship that links the input data to the corresponding desired output, which then allows the model to make predictions on new data. Common algorithms include linear regression, decision trees and neural networks.
- For example, photographs may be labeled to indicate whether an animal appears in them and, if so, what type of animal. This data can then be used to train a model that detects and recognizes specific types of animals in new photos that it hasn’t previously seen.
- Unsupervised learning: Unsupervised learning involves training on unlabeled data, and the goal is to discover patterns, structures or relationships within the data. Clustering (grouping data points based on similarities), dimensionality reduction (simplifying the training data by reducing the number of input variables while keeping essential information), and anomaly detection (identifying data points that don’t conform to the pattern) are common unsupervised learning tasks. Examples of algorithms include k-means clustering and principal component analysis (PCA).
Common benefits of machine learning include:
- Data analysis: In an age where massive amounts of data are being produced, machine learning has become a critical tool in rapid analysis processing and prediction.
- Error reduction: Machine learning significantly reduces the probability of human error.
- Automated decision-making: Machine learning models can quickly process vast amounts of information to make predictions or classifications without human intervention or human biases. Humans can take time on other tasks while AI makes automated decisions at high speeds.
- Pattern recognition: Machine learning excels at recognizing complex patterns and relationships in data, which can be challenging for humans to identify manually.
- Continuous improvement: Machine learning models can adapt over time, refining their decision-making as they receive new data.
- Predictive analytics: Machine learning is used for predictive modeling, allowing organizations to forecast trends, customer behavior and outcomes.
ML is no longer a technology of the future. Enterprises are taking action to incorporate ML into their operations, products and services. When considering machine learning use cases, it's important to be thoughtful as to what types of ML models are used and how ML capabilities are integrated into IT infrastructure.
Typical machine learning use cases include:
- Customer insights: Machine learning can provide valuable customer insights and predictions. This information helps marketers optimize leads, web traffic, returns from campaigns, and product targeting.
- Competitive advantage: Enterprises use a tactic known as transfer learning to inherit a generic pre-trained machine learning model from a third party and differentiate it through additional training with unique customer data. This approach can help enterprises gain a sustainable competitive advantage.
- Healthcare diagnostics: Machine learning can be applied to medical image analysis, disease prediction and patient risk assessment.
- Recommendation systems: Machine learning–based recommendation systems analyze user behavior to provide personalized product or content recommendations, as seen in platforms like Netflix, Amazon and YouTube.
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