Machine learning is an area of artificial intelligence and computer science that includes the development of software and algorithms that can make predictions based on data. The software can make decisions and follow a path that is not specifically programmed. Machine learning is used within the field of data analytics to make predictions based on trends and insights in the data.
Types of Algorithms: There is no shortage of machine learning algorithms. They range from fairly simple to highly complex. Here are a few of the most commonly used models:
This class of machine learning algorithm involves identifying a correlation — generally between two variables — and using that correlation to make predictions about future data points.
- Decision trees- These models use observations about certain actions and identify an optimal path for arriving at the desired outcome.
- K-means clustering – This model groups a specified number of data points into a specific number of groupings based on like characteristics.
- Neural networks – These deep learning models utilize large amounts of training data to identify correlations between many variables to learn to process incoming data in the future.
- Reinforcement learning – This area of deep learning involves models iterating over many attempts to complete a process. Steps that produce favorable outcomes are rewarded and steps that produce undesired outcomes are penalized until the algorithm learns the optimal process.
Uses: Machine learning is being used in a wide range of applications today. One of the most well-known examples is Facebook’s News Feed which uses machine learning to personalize each member’s feed. This software simply uses statistical analysis to identify patterns in the user’s data and use those patterns to populate the News Feed. Machine learning is also entering an array of enterprise applications. Customer relationship management (CRM) systems use learning models to analyze email and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially effective responses. Business Intelligence(BI) and analytics vendors use machine learning in their software to help users automatically identify potentially important data points. Human resource (HR) systems use learning models to identify characteristics of effective employees and rely on this knowledge to find the best applicants for open positions.
Working: Machine learning algorithms are often categorized as supervised or unsupervised. Supervised algorithms require machine learning skills to provide input and desired output, in addition to furnishing feedback about the accuracy of predictions during algorithm training. Data scientists determine which variables, or features, the model should analyze and use to develop predictions. Once your machine learning training is complete, the algorithm will apply what was learned to new data.
Unsupervised algorithms do not need to be trained with desired outcome data. Instead, they use an iterative approach called deep learning to review data and arrive at conclusions. Unsupervised learning algorithms are used for more complex processing tasks than supervised learning systems, including image recognition, speech-to-text and natural language generation. These networks work by combing through millions of examples of training data and automatically identifying often subtle correlations between many variables. Once trained, the algorithm can use its bank of associations to interpret new data. These algorithms have only become feasible in the age of big data, as they require massive amounts of training data.
What the future holds: While machine learning algorithms have been around for decades, they’ve attained new popularity as artificial intelligence(AI) has grown in prominence. Deep learning models in particular power today’s most advanced AI applications. Machine learning platforms are among enterprise technology’s most competitive realms, with most major vendors, including Amazon, Google, Microsoft, IBM, and others, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection, model building, training, and application deployment.
As machine learning continues to increase in importance to business operations and AI becomes ever more practical in enterprise settings, the machine learning platform wars will only intensify. Continued research into deep learning and AI is increasingly focused on developing more general applications. Machine learning training involves extensive training in order to produce an algorithm that is highly optimized to perform one task. Some researchers are exploring ways to make models more flexible and able to apply context learned from one task to future, different tasks. Funding for research and development in the fields of machine learning and artificial intelligence is growing at a rapid pace.
This translates into strong demand for experts that can produce better insights from data. At the time of this writing, Indeed.com listed over 1300 full-time, open positions for machine learning specialists, people who can write, implement, test, and improve machine learning models. Top job titles include Machine Learning Engineer, Data Mining Engineer, AI Engineer, and Machine Learning Infrastructure Developer and salary estimates range as high as $130K per year.
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