What Is Machine Learning?

Machine learning uses statistics to find patterns in massive amounts of data, including numbers, words, images, clicks, and anything else that can be stored digitally.

Machine learning technology powers many of the services we rely on today, including recommendation systems like those found on Netflix, Disney+, YouTube, and Spotify. Machine learning is also key to the functionality of search engines, social media feeds like Facebook and Twitter, and voice assistants like Siri and Alexa.

In all of these instances, these platforms use machine learning to collect as much data about you as possible — which directors you like, what links you’re clicking, which statuses have provoked a reaction from you — and then make a highly educated guess about what you might want to buy, watch, or click next. Voice assistants, meanwhile, use machine learning to surmise which words match best with the sounds coming out of your mouth.

The process is, in fact, not that complicated: find the pattern, apply the pattern. But it pretty much runs the world. That’s in big part thanks to an invention in 1986, courtesy of Geoffrey Hinton, today known as the father of deep learning.

There are several main basic types of machine learning methods or ML paradigms:

Supervised learning (SL) allows you to collect data or produce a data output from a previous machine learning deployment. With supervised tasks, we give the computer a collection of labeled input-output data points called a training set that are used to train a system to classify data or make accurate outcome predictions. Supervised machine learning actually works in much the same way humans learn. The most popular paradigm for machine learning, supervised learning involves feeding input data into a machine learning algorithm or model, which adjusts its weights until the model has been fitted appropriately.

Some common applications for supervised learning algorithms include email spam filters, face recognition tech, and Internet ad placement.

Unsupervised learning helps you find all kinds of unknown patterns in data. The opposite of SL, unsupervised machine learning involves combing through unlabeled data to discover patterns that ultimately solve association or clustering problems. Machine learning algorithms are fed large amounts of data and equipped with the tools to understand the data. It then learns to cluster or organize that data in such a way that a human could understand it. Two common unsupervised learning tasks are clustering and dimensionality reduction.

You might see unsupervised learning in the recommendation systems on a streaming service or targeted ads based on buying habits or browsing data.

Reinforcement learning is the training of machine learning models to make a series of decisions. Where supervised and unsupervised learning are associated based on the presence or lack of labels, in reinforcement learning, artificial intelligence is rewarded or punished based on its actions. The AI gradually learns through trial and error how to solve a problem, usually beginning with random trials before finding more advanced and sophisticated tactics after learning the parameters of the game. Examples of reinforcement learning applications include in industrial simulations as well as video games.

Machine learning is a form of artificial intelligence (AI) that teaches computers to learn and improve upon past experiences and it works by exploring data and identifying patterns with minimal human intervention.

Almost any task that can be completed with a data-defined pattern or set of rules can be automated with machine learning. This gives companies the opportunity to transform processes that were previously only possible for humans to perform — for example, customer service calls, bookkeeping, or reviewing resumes.

A major part of what makes machine learning so valuable is its ability to detect what the human eye misses. Machine learning models are able to catch complex patterns that would have been overlooked during human analysis.

Machine Learning is a buzzy term in the business world right now for good reason. There are many real-world benefits to ML, including:

• Quickly spot trends and patterns. Deploying machine learning techniques allows companies to spot meaningful trends in data that humans wouldn’t detect. Ecommerce sites and streaming services can use ML to quickly comb through user data and produce products and deals that will be relevant.
• Improves security. Anti-virus software and email spam filters are two of the ways in which machine learning keeps our computers and profiles more secure.
• Engage and retain customers. Machine learning helps companies quickly process data about their customers and clients, which in turn allows for deep personalization options that improve user experience and keeps customers coming back.
• Handle unwieldy data. Machine learning algorithms are a good option for dealing with multi-dimensional data in a dynamic environment.

Machine learning is well suited to problems that have the characteristics of the handwriting recognition problem – that is, problems that are highly complex, where approximate solutions will suffice, and that are inherently statistical or probabilistic. Businesses are increasingly discovering that many of their problems have these traits. Consider the problem of flagging fraudulent credit card transactions.

• Complexity: The rules that identify fraudulent credit card transactions are complex and ever-changing.
• Approximations suffice: We are flagging transactions for further review, so it is alright if the program is wrong sometimes.
• Solutions are probabilistic: We are never certain that a transaction is fraudulent until we verify by contacting the customer.

And what do we need to implement a machine learning solution to a business problem like this? Data – a commodity that modern businesses have in high supply. For these reasons, businesses are discovering that machine learning tools fit quite naturally in their activities and objectives, which is why we are seeing such a dramatic rise in the application of machine learning tools and technologies in the business world.

Deep learning and machine learning are closely related subsets of artificial intelligence. Deep learning is machine learning taken to the next level: it gives machines an enhanced ability to find — and amplify — even the subtlest patterns. These are called deep neural networks — deep because there are many, many layers of simple computational nodes that work together to munch through data and deliver a final result in the form of the prediction.

Where the goal of machine learning is for computers to think with less human intervention, deep learning actually tends to require less human intervention on an ongoing basis.

Some of the other key differences between machine learning and deep learning include:

Data science refers to a scientific approach to pulling actionable business insights from structured and unstructured data, while machine learning refers to techniques Data Scientists teach computers to learn from data without explicit programming.

It can certainly be confusing to differentiate between machine learning and data science because the two disciplines are so closely related.

Although there is significant overlap, here are a few of the differences between machine learning and data science:

Artificial intelligence is the larger concept of machines having the capacity to carry out tasks in a way that we would consider “smart,” while machine learning is an application of AI based on the concept that if we give machines access to data, they are capable of learning for themselves.

Over the years, our idea of what constitutes AI has changed. Rather than increasingly complex calculations, work in the field of AI has concentrated on mimicking human decision making processes and carrying out tasks in ever more human ways.

Machine learning bias is a problem that arises when an algorithm produces results that are systemically prejudiced due to incorrect assumptions in the machine learning process.

Also sometimes called algorithm bias or AI bias, machine learning bias usually occurs because the people who design and/or train the machine learning systems create algorithms that reflect unintended cognitive biases or real-life prejudices, or it’s because they’re using incomplete, faulty or prejudicial data sets to train and/or validate their machine learning systems.

Overfitting refers to a situation in which the training data is modeled too well, meaning that a model has learned the detail and noise in the training data to an extent that it has a negative effect on the performance of the model on new data.

This means that the noise or random fluctuations in the training data are learned as concepts by the model. The problem? These concepts do not apply to new data and negatively impact the ability of the model to generalize.

Overfitting is more likely with nonparametric and nonlinear models that have more flexibility when learning a target function. That’s why many nonparametric machine learning algorithms also include parameters or techniques to limit and constrain how much detail the model learns.

Regression in machine learning consists of a set of methods that allow us to predict a real or continuous outcome variable from the value of one or multiple predictor variables. A continuous output variable is a real-value, such as an integer or floating point value, and they are often quantities –– in other words, amounts and sizes.

The goal of regression models is to develop equations that define one variable as the function of another variable.

While many different models can be used, the simplest is the linear regression. It tries to fit data with the best hyper-plane which goes through the points.

Regularization in machine learning is the process that regularizes or shrinks the coefficients toward zero to prevent overfitting.

In other words, regularization discourages learning a more complex or flexible model.

The basic idea is to penalize the complex models – for instance, adding a complexity term that would give a bigger loss for complex models.

Cross-validation is a technique for evaluating machine learning models by training several machine learning models on subsets of the available input data and evaluating them on the complementary subset of the data. Cross-validation is used to detect overfitting.