Machine Learning has begun to reshape how we live, so we need to understand what Machine Learning is and know why it matters.
What is Machine Learning?
A good start at a Machine Learning definition is that it is a core sub-area of Artificial Intelligence (AI). ML applications learn from experience (well data) like humans without direct programming. When exposed to new data, these applications learn, grow, change, and develop by themselves. In other words, with Machine Learning, computers find insightful information without being told where to look. Instead, they do this by leveraging algorithms that learn from data in an iterative process.
While the concept of Machine Learning has been around for a long time (think of the WWII Enigma Machine), the ability to automate the application of complex mathematical calculations to Big Data has been gaining momentum over the last several years.
At a high level, Machine Learning is the ability to adapt to new data independently and through iterations. Applications learn from previous computations and transactions and use “pattern recognition” to produce reliable and informed results.
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Why Machine Learning?
To better understand the uses of Machine Learning, consider some instances where Machine Learning is applied: the self-driving Google car; cyber fraud detection; and, online recommendation engines from Facebook, Netflix, and Amazon. Machines can enable all of these things by filtering useful pieces of information and piecing them together based on patterns to get accurate results.
The process flow depicted here represents how Machine Learning works:
The rapid evolution in Machine Learning has caused a subsequent rise in the use cases, demands—and, the sheer importance of ML in modern life. Big Data has also become a well-used buzzword in the last few years. This is, in part, due to the increased sophistication of Machine Learning, which enables the analysis of large chunks of Big Data. Machine Learning has also changed the way data extraction and interpretation are done by automating generic methods/algorithms, thereby replacing traditional statistical techniques.
Uses of Machine Learning
Typical results from Machine Learning applications we either see or don’t regularly include web search results, real-time ads on web pages and mobile devices, email spam filtering, network intrusion detection, and pattern and image recognition. All these are by-products of using Machine Learning to analyze massive volumes of data.
Traditionally, data analysis was trial and error-based, an approach that becomes impossible when data sets are large and heterogeneous. Machine Learning provides smart alternatives to analyzing vast volumes of data. By developing fast and efficient algorithms and data-driven models for real-time processing of data, Machine Learning can produce accurate results and analysis.
Pro Tip: For more on Big Data and how it’s revolutionizing industries globally, check out our article about what Big Data is and why you should care.
According to a related report by McKinsey, “As ever more of the analog world gets digitized, our ability to learn from data by developing and testing algorithms will only become more important for what is now seen as traditional businesses.” The same report also quotes Google’s chief economist Hal Varian who calls this “computer kaizen” and adds, “just as mass production changed the way products were assembled, and continuous improvement changed how manufacturing was done… so continuous (and often automatic) experimentation will improve the way we optimize business processes in our organizations.” Machine Learning is here to stay.
Data Mining, Machine Learning, and Deep Learning
While all three disciplines listed above are in the same family, it’s essential to understand how they differ. At a basic level, Machine Learning uses the same algorithms and techniques like data mining, but the types of predictions the two provide vary. Data mining discovers previously unknown patterns and knowledge, whereas Machine Learning reproduces known patterns and knowledge. ML then automatically applies that information to additional datasets, and, ultimately, the business strategy and outcomes.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
Popular Machine Learning Methods
How do machines learn? Two Machine Learning techniques are supervised learning and unsupervised learning. Approximately 70 percent of Machine Learning is supervised learning, while unsupervised learning ranges from 10 – 20 percent. Other methods that are used less often include semi-supervised and reinforcement learning.
This kind of learning is possible when inputs and outputs are identified, and algorithms are trained using labeled examples. To understand this better, let’s consider the following example: a piece of equipment could have data points labeled F (failed) or R (runs).
The supervised learning algorithm receives a set of inputs along with the corresponding output to find errors. Based on these inputs, it would modify the model accordingly. This is a form of pattern recognition since supervised learning uses methods like classification, regression, prediction, and gradient boosting. Supervised learning then uses these patterns to predict the values of the label on other unlabeled data.
Supervised learning is typically used in applications with which historical data predicts future events, such as fraudulent credit card transactions.
Unlike supervised learning, unsupervised learning works with data sets without historical data. An unsupervised learning algorithm explores collected data to find a structure. This works best for transactional data; for instance, it helps identify customer segments and clusters with specific attributes, often used in content personalization.
Popular techniques where unsupervised learning is used also include self-organizing maps, nearest-neighbor mapping, singular value decomposition, and k-means clustering. In other words: online recommendations, identification of data outliers, and segment text topics are examples of unsupervised 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 uses a small amount of labeled data with a large amount of unlabeled data.
We use this type of Machine Learning for classification, regression, and prediction. Examples of semi-supervised learning are face- and voice-recognition applications.
Like traditional types of data analysis, here, the algorithm discovers data through a process of trial and error and then decides what action results in higher rewards. Three major components make up reinforcement learning: 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 does.
Reinforcement learning occurs when the agent chooses actions that maximize the expected reward over a given time. This is easiest to achieve when the agent is working within a sound policy framework.
Some Machine Learning Algorithms And Processes
If you’re studying Machine Learning, you should familiarize yourself with common Machine Learning algorithms and processes. These include 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.
To get the most value out of Big Data, other Machine Learning tools and processes that leverage various algorithms 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
- An integrated end-to-end platform for the automation of the data-to-decision process
Machine Learning is Here to Stay
Whether you realize it or not, Machine Learning is one of the most important technology trends—it underlies so many things we use. Speech recognition, Amazon and Netflix recommendations, fraud detection, and financial trading are a few examples of Machine Learning commonly in use in today’s data-driven world.
Machine Learning is increasingly touching more aspects of our everyday lives. This also means that there are many lucrative Machine Learning careers available. If you want to get in on the action, we have the resources to help you get there.