There are a handful of definitions out there, but put simply, Machine Learning is the science of getting computers to execute tasks without being explicitly told to do so. Machine Learning is responsible for providing recommendations of products on Amazon or displaying recommendations on Netflix. Of course, it can (and does) get much more complex than that.
Machine Learning is a branch of Artificial Intelligence (AI) that is helping businesses analyze bigger, more complex data to uncover hidden patterns, reveal market trends, and identify customer preferences. By automating analytical model building, the insight gained is deeper and derived at a pace and scale that human analysts can’t match.
Most Common Machine Learning Algorithms
By using algorithms to build models that uncover connections, organizations can make better decisions. Below are the three most common types of Machine Learning Algorithms:
- Supervised Learning – Supervised learning occurs when an algorithm learns from example data and associated target responses that can consist of numeric values or string labels, such as classes or tags, in order to later predict the correct response when posed with new examples.
- Unsupervised Learning – Unsupervised learning occurs when an algorithm learns from plain examples without any associated response, leaving the algorithm to determine the data patterns on its own.
- Reinforcement Learning – Using this algorithm, the machine is trained to make specific decisions. The machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.
Machine Learning is Widely Applicable
Most industries working with big data have recognized the value of Machine Learning technology. By collecting insights from this data, organizations are able to work more efficiently or gain an advantage over competitors.
Below are seven industries that are leveraging Machine Learning:
1. The Healthcare Industry
Machine Learning is a fast-growing trend in the healthcare industry thanks to the advent of wearable devices and sensors that can use data to assess patient health in real time. In fact, as of 2017, 7.1 million Americans were enrolled in a digital health platform where vital signs are continually monitored by sensors worn on the body. This information is then sent to a Machine Learning analytics center that flags anomalies and alerts treatment professionals. The technology can also help medical experts analyze data to identify trends that may lead to improved diagnoses.
2. The Financial Services Industry
Banks and other businesses in the financial industry use Machine Learning technology for two key purposes: to identify important insights in data, and to prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cyber-surveillance to pinpoint warning signs of fraud.
3. The Retail Industry
Technologies powered by Machine Learning capture, analyze, and use data to personalize the shopping experience in real time. Algorithms discover similarities and differences in customer data to expedite and simplify segmentation for enhanced targeting. In fact, Machine Learning capabilities can present online shoppers with personalized product recommendations while adjusting pricing, coupons, and other incentives in real time.
4. The Automotive Industry
The automotive industry is taking steps to differentiate itself by leveraging Machine Learning capabilities and big data analytics to improve operations, marketing, and customer experience before, during, and after purchase. Predictive analytics lets manufacturers monitor and share vital information regarding potential vehicle or part failures with dealerships, reducing customer maintenance costs. And by identifying trends and patterns from large datasets on vehicle ownership, dealer networks can be optimized by location for accurate, real-time parts inventory and improved customer care.
5. Government Agencies
Government agencies, such as public safety and utilities, have a particular need for Machine Learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine Learning can also help detect fraud and minimize identity theft.
6. Transportation Industries
The data analysis and modeling aspects of Machine Learning are important tools to delivery companies, public transportation, and other transportation organizations. In fact, analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability.
7. Oil & Gas Industries
Machine Learning has become an integral part of the operations of most oil and gas companies, allowing them to gather large volumes of information in real-time and translate data sets into actionable insights. They now need to view data as an extremely valuable resource, with huge upside for companies with innovative, robust Machine Learning strategies. Saving time, reducing costs, boosting efficiencies, and improving safety are all crucial outcomes that can be realized from using Machine Learning in oil and gas operations.
Key Skill Areas within Machine Learning
According to a survey from Tech Pro Research, only 28% of companies have some experience with AI or Machine Learning, and more than 40% said their enterprise IT personnel don’t have the skills required to implement and support AI and/or Machine Learning. Below are some key skill areas that are required to work in the field of Machine Learning:
- Probability – Most machine Learning algorithms are about dealing with uncertainty and making reliable predictions. The mathematical tools to deal with such settings are found in principles of probability and its derivative techniques.
- Statistics – Also of importance are tools and techniques that enable the creation of models from data. Machine Learning algorithms are often built upon statistical models.
- Data Modeling – Data modeling is a representation of the data structures in a table for a company’s database and is a very powerful expression of the company's business requirements.
- Data Science – Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.
- Software Engineering – A Machine Learning Engineer’s typical output or deliverable is software. And often it is a small component that fits into a larger ecosystem of products and services. Some of the popular programming languages within Machine Learning in are Python, R, Java, and C++.
Common Job Roles within Machine Learning Teams
Generally, Machine Learning teams are comprised of Scientists, Engineers, Analysts, and Managers. Let’s take a look at each of the roles and their associated responsibilities.
- Data Analysts – Data Analysts monitor processes, evaluate data quality, and monitor production model performance. This allows for more senior roles to focus on innovation, not maintenance.
- Data Engineers – Data Engineers are responsible for building and maintaining the technical infrastructure required for modeling, predictions, and analysis. These professionals create and maintain databases, machine learning pipelines, and production processes.
- Data Scientists – Data Scientists own the modeling process. In general, they take input parameters from product or other team leads in order to understand the model’s business objective. They then work to articulate requirements to the engineers and other stakeholders. Once these criteria have been defined, the process of building tests, models, and evaluating performance begins.
- Machine Learning Engineers – With backgrounds and skills in data science, applied research, and heavy-duty coding, these professionals run the operations of a machine learning project and are responsible for managing the infrastructure and data pipelines needed to bring code to production.
Some Closing Thoughts
Nearly any organization that wants to capitalize on its data to gain insights, improve relationships with customers, increase sales, or be competitive will rely on Machine Learning. It has applications in government, healthcare, transportation, and more—virtually any business that wants to make predictions, and has a large enough data set, can use Machine Learning to achieve their goals.
However, Machine Learning's ability to automate, anticipate, and evolve is powerful, but that doesn't mean computers will take over the world. Machine Learning still requires human operators to provide context, to set parameters of operation, and to continue to improve the algorithms. Click here to view learning solutions from New Horizons surrounding Machine Learning.