The process flow depicted here represents how machine learning works:
(https://d112vpovu2xa8r.cloudfront.net/portal_simplilearn_curatasite_com/media/ezl0iv4WYZMaD37.jpeg)
There are two popular methods of machine learning named supervised learning and unsupervised learning. It is estimated that about 70 percent of machine learning is supervised learning, while unsupervised learning ranges from 10 – 20 percent. Other methods that are less-often used are semi-supervised and reinforcement learning.
Supervised Learning
This kind of learning is possible when inputs and the outputs are clearly identified, and algorithms are trained using labeled examples. To understand this better, let’s consider the following example: an equipment could have data points labeled F (failed) or R (runs).
(http://s3.amazonaws.com/static2.simplilearn.com/ice9/free_resources_article_thumb/Machine_Learning_2.jpg)
The learning algorithm using supervised learning would receive a set of inputs along with the corresponding correct output to find errors. Based on these inputs, it would further modify the model accordingly. This is a form of pattern recognition, as supervised learning happens through methods like classification, regression, prediction, and gradient boosting. Supervised learning uses patterns to predict the values of the label on additional unlabeled data.
Supervised learning is more commonly used in applications where historical data predict future events, such as fraudulent credit card transactions.
Unsupervised Learning
Unsupervised learning, unlike supervised learning, is used with data sets without historical data. An unsupervised learning algorithm explores surpassed data to find the structure. This kind of learning works best for transactional data; for instance, it helps in identifying customer segments and clusters with certain attributes—this is often used in content personalization.
(http://s3.amazonaws.com/static2.simplilearn.com/ice9/free_resources_article_thumb/Machine_Learning_3.jpg)
Popular techniques where unsupervised learning is used also include self-organizing maps, nearest neighbor mappig, singular value decomposition, and k-means clustering. Basically, online recommendations, identification of data outliers, and segment text topics are all examples of unsupervised learning.
Semi-Supervised Learning
As the name suggests, semi-supervised learning is a bit of both supervised and unsupervised learning and uses both labeled and unlabeled data for training. In a typical scenario, the algorithm would use a small amount of labeled data with a large amount of unlabeled data.
(http://s3.amazonaws.com/static2.simplilearn.com/ice9/free_resources_article_thumb/Machine_Learning_4.jpg)
This type of learning can again be used with methods such as classification, regression, and prediction. Examples of semi-supervised learning would be face and voice recognition techniques.
Reinforcement Learning
This is a bit similar to the traditional type of data analysis; the algorithm discovers through trial and error and decides which action results in greater rewards. Three major components can be identified in reinforcement learning functionality: the agent, the environment, and the actions. The agent is the learner or decision-maker, the environment includes everything that the agent interacts with, and the actions are what the agent can do.
(http://s3.amazonaws.com/static2.simplilearn.com/ice9/free_resources_article_thumb/Machine_Learning_5.jpg)
Reinforcement learning occurs when the agent chooses actions that maximize the expected reward over a given time. This is best achieved when the agent has a good policy to follow.
Some Machine Learning Algorithms And Processes
If you’re studying machine learning, you should familiarize yourself with these common machine learning algorithms and processes: neural networks, decision trees, random forests, associations and sequence discovery, gradient boosting and bagging, support vector machines, self-organizing maps, k-means clustering, Bayesian networks, Gaussian mixture models, and more.
Other tools and processes that pair up with the best algorithms to aid in deriving the most value from big data include:
- Comprehensive data quality and management
- GUIs for building models and process flows
- Interactive data exploration and visualization of model results
- Comparisons of different machine learning models to quickly identify the best one
- Automated ensemble model evaluation to identify the best performers
- Easy model deployment so you can get repeatable, reliable results quickly
- Integrated end-to-end platform for the automation of the data-to-decision process
Source: simplilearn