Best Machine Learning software of 2020
Finding the best machine learning software
Machine learning software provides you with tools for extracting insights and patterns from raw data, many of which would otherwise go unnoticed by humans. With these insights, you and your business can make smarter decisions and implement new technologies more effectively.
Machine learning software can take on various forms, many of which are built for specific tasks and applications. Some common examples of machine learning software include chatbots, digital assistants, self-driving cars, and predictive financial analysis platforms—just to name a few.
Almost every industry can benefit from machine learning software in some way and those that haven’t may soon fall behind.
Why use machine learning software?
Data has quickly become the most valuable commodity of the 21st century, similar to what oil was in the 20th century. Much like oil, the value of data lies in its “refinement;” here, refinement data yields valuable insights and patterns which help drive business decisions.
Unlike oil, however, the value of data isn’t inherent to itself. Where crude oil is valuable on its own, data is only as valuable as the insights it contains. As such, raw data without usable insights, patterns, of trends is practically worthless.
For example, consider a social media company selling user data to advertisers. Advertisers could use machine learning software to analyze this user data in hopes of identifying common trends in activity, conversation, searches, and so on—in other words, anything to help them better understand their market. By identifying their customer’s preferences and behavioral trends, the advertisers can sell more effectively.
However, it’s easy to imagine cases where data in this scenario can be either extremely valuable or extremely worthless. For example, if the user data was that of 100,000 random users across multiple countries and demographics, then machine learning software would likely have difficulty identifying common trends. However, if the user data was that of 5,000 single men aged 25-45 in the Boise, Idaho area, then the machine learning software would be more likely to identify valuable (read: marketable) trends.
Of course, market analysis is far from the only use for machine learning software. For example, many businesses use chatbots on their websites, which process user inputs in hopes of understanding the user’s intention (e.g., “what are your operating hours?”). Through this understanding, the chatbot can respond to the user in a way that (hopefully) answers their questions (e.g., “our operating hours are 9-5, Monday through Saturday”).
Similarly, self-driving cars process visual inputs to identify traffic signals, cars, pedestrians, and so on. In any case, data can provide extremely valuable insights and, when used correctly, can be an extremely powerful tool.
Still, machine learning is very much a numbers game: The most valuable insights often come after processing datasets containing millions of individual data points. Due to the ever-growing size of these datasets, however, it has become nearly impossible for any human – let alone any team of humans – to perform efficient analysis using traditional methods. As a result, machine learning has become a preferred method for data processing.
Machine learning software not only processes large quantities of raw data more efficiently than humans but also does so more effectively: A machine learning algorithm is not only more likely to pick up on subtle (yet powerful) trends in data but can also automatically construct models which use the trends to generate even better insights as new data comes forward.
Revisiting the user data example from earlier, for example, machine learning software eliminated the need for advertisers to comb through thousands of individual profiles and, afterward, attempt to identify common trends—even when there might not have been any.
While some human teams can still accomplish these tasks in some capacity, machine learning software has quickly become the most cost- and value-effective option. By being able to efficiently and effectively extract trends from raw data, you and your business will be better equipped to make intelligent decisions and implement new technology.
Who uses machine learning software?
Since machine learning software comes in such a wide variety of forms, it’s no surprise that its users do, too; everyone from small business owners to data scientists use machine learning software in some way. Machine learning software has become even more widespread as data becomes an increasingly valuable commodity in many industries.
Data Scientists and AI Engineers
The most direct use of machine learning software is in applications of data science and artificial intelligence (AI), where engineers and computer scientists use generalized machine learning platforms to custom-build neural networks and other computational models to solve specific problems.
One such example would be software for self-driving cars. Here, software engineers design and program neural networks trained to identify cars, pedestrians, and traffic signals from captured by the car’s camera. A similar example is the facial recognition software built into many smartphones and apps.
Software Developers and Software Engineers
Software developers and software engineers are beginning to use machine learning software for more general purposes, especially as their respective companies begin to implement various forms of machine learning and AI into their product offerings. For example, many software developers are now tasked with programming basic neural networks and machine learning software to analyze business data in a useful way.
Building a neural network from scratch usually sits at the cutting edge of innovation, however, and most companies with a non-AI product offering probably won’t need to build a neural network by themselves. Thankfully, businesses can still leverage machine learning software in powerful ways, especially since most software packages come included with time-tested, purpose-specific machine learning tools.
General Businesses and eCommerce Businesses
Most non-AI businesses using machine learning software often have practical applications in mind, such as customer support chatbots or business intelligence. Regardless of the specific application, however, most businesses generally use machine learning software to automate and optimize the mundane.
Consider the chatbot example again: Businesses moving their operations online quickly find that customer support is no longer limited to normal business hours. As a result, many businesses are using chatbots – a specific type of machine learning software – to make basic customer support available 24/7. While chatbots cannot fully replace human support teams (yet!), they have become increasingly adept at handling basic queries that don’t require direct human interaction.
Some businesses use machine learning software at even higher levels, such as in specific applications of business analytics and intelligence. These businesses usually have access to large amounts of data and deploy prebuilt (or customized) machine learning models to identify market trends. Leading financial firms, for example, are now using this capability to make smarter investments by identifying subtle trends and changes in market data.
...And Everyone Else!
The examples above are just a few ways that businesses are leveraging machine learning software. From chatbots to advanced analytics, however, most machine learning software shares some common features.
Machine learning software should offer learning methods applicable to your business goals. Machine learning models are typically categorized by how they “learn;” a model’s learning style determines how exactly it will use and process data. Different models and learning styles are often suited to different tasks.
The two major categories of learning are supervised learning and unsupervised learning. In supervised learning, a machine learning model uses previous data to make decisions. For example, machine learning software tasked with filtering spam emails will analyze previously flagged emails to “learn” what qualifies an email as spam. Such a qualifier could be, for example, words like “free” or “click” used a certain number of times.
In unsupervised learning, machine learning models extract their own insights over time. An abstract example of this would be a model tasked with categorizing pictures of different species of animals. After viewing many different animal pictures, the model might eventually group pictures of the same species (such as cats, dogs, and so on) into distinct categories. While the model wouldn’t have a conceptual understanding of the English word “cat,” it would have still learned to identify what a “cat” is based on common visual features.
Other categories of learning include reinforcement learning and semi-supervised learning, but these are generally less common than supervised and unsupervised learning.
Machine learning software should constantly improve with new data. The goal of machine learning software is to “learn.” Whether supervised or unsupervised, a machine learning model should continuously adapt to new data as it arrives. Ideally, new data will improve and optimize the model over time.
Consider the animal classification example from earlier. The model would be useless if it didn’t gradually develop a better understanding of what a “cat” is, especially after being shown hundreds of different pictures of cats! While the machine learning software you use probably won’t be looking at cat pictures, the principle still applies: Any machine learning software you employ must adapt, learn, and improve with new data.
Machine learning software should be the primary source of intelligence for connected applications. In many applications, machine learning is the “brain” of software. While traditional software is purely logical, machine learning attempts to “think” in a way similar to humans. In other words, machine learning allows programs to think outside the box of simple logical instructions.
While impressive, this processing capability often requires a lot of processing power, however. As a result, machine learning software should be a “centralized” source of cognition for software, the latter of which should remain relatively lightweight when deployed.
Machine learning software should be able to process data from multiple inputs/sources. In most cases, data doesn’t come from just one source: Data comes from everywhere!
Since data sources can take on many different forms, machine learning software should be able to take on data from any number of different sources and process it as needed. While certain data formats and storage methods will require some initial configuration, the machine learning software you use should have no problem conforming to your exact specifications.
Machine learning software should provide a useful output. Since the ultimate goal of implementing machine learning is to extract insights in some way or another, machine learning software should provide useful results. More importantly, results should also be relevant to your business goals and capabilities.
Q: What is machine learning?
A: Machine learning is the ability of a computer program to adapt and learn over time. This ability differs from that of “traditional” software, which uses predefined instructions and definitions to process information. In other words, the instructions in machine learning aren’t concrete, and instead, continuously adapt and improve with new data.
The animal classification example from earlier can help clarify this definition. In this case, machine learning is what allows the program to gradually understand what a “cat” is as it processes each new cat picture. By contrast, traditional software would have to rely on pre-defined definitions of what “cats” are.
Some shortcomings are immediately apparent here. What if the programmer didn’t do a good job defining what a “cat” is? For example, both cats and dogs have whiskers, while cats and some dogs both have pointy ears. If the programmer defines a “cat” as anything with both whiskers and pointy ears, then their program would likely misclassify certain dogs as cats.
Another problem presents itself once the program encounters unexpected species of animals. The programmer may have worked hard to come up with definitions for each animal, but the program’s capabilities are ultimately limited by the programmer. In other words, if the programmer forgets to define what an “aardvark” is, then the program will never be able to classify an aardvark unless the programmer writes a precise definition.
In essence, traditional programs are only as good as their programmers.
Machine learning provides a more dynamic solution to the animal classification example: Instead of having to pre-define the visual features of different animals, machine learning would allow the program to simply observe animal pictures and note the differences over time. Eventually, the program would build its own definitions based on what it observes. While humans would ultimately have to define each category (e.g., “cats,” “dogs,” etc.), the program would still have some sort of inherent visual understanding of what a “cat” is.
In essence, where traditional software is limited by the programmer, machine learning continuously creates its own instructions based on observations and data. This capability is what allows machine learning to transcend many human limitations and develop unique (but accurate) insights.
Q: Is machine learning AI?
A: Technically speaking, machine learning is a subcategory of artificial intelligence (AI). AI is a broad category in and of itself, encompassing everything from machine learning to brain simulation.
In the business world, however, “machine learning” and “AI” are often used interchangeably. While this syntax is not formally correct, it’s often good enough; businesses rarely employ the abstract forms of artificial intelligence found at research universities.
Generally defined, “artificial intelligence” is anything that attempts to replicate human intelligence and behavior through artificial means. Despite the relatively lower complexity of machine learning, it still benefits immensely from advances in AI research.
Q: What is a machine learning model?
A: A machine learning “model” is the specific learning structure used when processing data. Models are generally categorized as one of the major learning types discussed earlier: supervised learning (learning based on previous data) and unsupervised learning (learning based on observing patterns). Several unique models can exist for each of these learning types.
Two common supervised machine learning models are regression and classification. Both models use previously defined data to group data, either as a continuous output (regression) or as a discrete value (classification).
Unsupervised machine learning models include clustering and dimensionality reduction. In contrast to supervised models, unsupervised models continuously perform “grouping” without any previously defined data. In summary, clustering involves continuously grouping “similar” data points, whereas dimensional analysis continuously condenses large amounts of data into simpler forms and definitions.
While exploring the full range of machine learning models is beyond the scope of this FAQ, it’s still helpful to keep these general ideas in mind when comparing machine learning software.
Q: What are machine learning algorithms?
A: Machine learning algorithms are the specific methods by which machine learning models (see above) learn. Here, a model is simply a machine learning algorithm that has been trained with data.
The spam email filter example from earlier is an example of a supervised classification model. Here, the algorithm is simply the spam filter’s basic ability to compare current data against previous data (e.g., “does this email contain elements commonly found in spam emails?”); the spam filter only becomes a model once there are emails (data) to compare.
In essence, all of the definitions and categories assigned to machine learning models can also be assigned to algorithms. Again, they’re interrelated: models are only algorithms with data.
Q: Is there automated machine learning?
A: Ideally, machine learning implementations should be automated in the sense that they can learn and adapt on their own. Whether supervised or unsupervised, a machine learning model should improve and develop over time; in this sense alone, machine learning is automated.
However, in many cases, machine learning often requires a lot of setup. Unless your machine learning software comes with pre-trained models, many software packages will require additional training data before it can deliver a reliably functional output. In this case, machine learning often requires “training wheels” while it conforms to your specific application(s). From that point on, however, machine learning software can typically process data automatically.
Q: How is Python used in machine learning?
A: Python is an open-source programming language known for its straightforward syntax and strong mathematical tools. Python is also heavily favored by the AI industry, where the majority of machine learning frameworks are built with Python as a result. Some popular examples of Python-based machine learning frameworks include TensorFlow and PyTorch.
Even if you’re not building machine learning models from scratch, many machine learning implementations require Python in some way. Other programming languages used for machine learning include R and MATLAB, although these are becoming less common for use in generalized, enterprise-level machine learning applications.
Q: What is bias in machine learning?
A: A “bias” in machine learning is a recurring result generated due to a classification error. For example, consider the spam email filter from earlier: If the model in that scenario was accidentally trained to flag any email containing the word “hello” as spam, it would quickly demonstrate bias by misclassifying non-spam emails (i.e., those containing the basic greeting “hello”) as spam. Bias is avoided through proper training, which any time-tested machine learning software should include.
Q: What are the applications of machine learning in finance?
A: The financial industry benefits immensely from machine learning. While financial specialists are still tasked with manually processing financial data, machine learning has helped eliminate many of the mundane tasks associated with financial analysis. As a result, machine learning has automated many important financial functions.
Among the most common examples of machine learning in finance are algorithmic trading and portfolio management (“robo-investors”). Since many trading activities come down to data analysis, machine learning allows financial institutions to instantly identify and react to subtle market changes. This capability has led to more effective investing.
Stock portfolios aren’t the only application of machine learning in finance: Activities related to fraud detection, risk management, and document analysis are becoming increasingly streamlined thanks to machine learning.
Q: What are the applications of machine learning in medicine?
A: Medicine is another data-rich industry that benefits from machine learning. Many healthcare institutions already employ machine learning for analyzing patient health records, which has become especially useful for medical imaging; with machine learning, doctors can now identify tumors and other anomalies that would have otherwise gone unnoticed.
Machine learning is also being used for research and drug discovery, where unsupervised learning models are tasked with identifying subtle trends in chemical data. Through identifying these subtle trends, medical researchers have become better equipped to develop new drugs and novel therapies.
Machine learning software can be a powerful tool for extracting valuable insights and trends from raw data. While there are many types and flavors of machine learning software, almost every business can benefit from machine learning in some way.