Have you ever wondered how Twitter curates your timeline? Or how Netflix knows exactly which show to recommend for you? Well, the answer is a type of AI solution called machine learning. In this article, we'll explain how machine learning works and what impact it has on the business world.

What Is Machine Learning (ML)?

Machine learning (ML) is the study of algorithms that allow software applications to systematically improve through experience.

What Does Machine Learning Do?

Machine learning gives a machine the capability to make data-driven decisions on its own, rather than being manually programmed to carry out assigned tasks. These systems are designed to automatically learn and evolve over time when exposed to new data.

How Does Machine Learning Work?

Data scientists must first train a machine with practice data for the system to create a model. Once the machine learning model training process is complete and the machine is fully capable of making accurate predictions on its own. New data is introduced to the machine learning algorithm. At this point, the machine should be able to process massive amounts of data, extract useful information and predict outcomes all by itself.

Deep Learning vs. Machine Learning

Both deep learning and machine learning fall under the category of artificial intelligence, but the two are very different concepts.

Machine learning models use algorithms to interpret data, learn from it, and make informed decisions based on its findings. Machine learning technology is often faster and more efficient, although it does require human guidance to some extent. If an inaccurate prediction is made, an engineer will then have to step in and make corrections.

On the other hand, deep learning is a subset of machine learning and works by structuring algorithms into layers to create artificial neural networks (ANNs). Deep learning is behind the most human-like artificial intelligence technology, as it requires zero training or supervision. ANNs allow algorithms to autonomously determine the accuracy of a prediction.

AI vs. Machine Learning

Is there a difference between AI and machine learning? Artificial intelligence (AI) is the science of making computers behave in an intelligent, human-like manner. There are various subsets of AI, including deep learning, robotics, natural language processing, speech recognition, and more. Machine learning is also a subset of AI, focusing primarily on developing computer programs that use data to teach themselves. These models are designed to automatically learn and evolve from experiences without being deliberately programmed to do so.

Machine Learning vs. Neural Networks

Machine learning and artificial neural networks (ANNs) both fall under the AI category. Neural network is a subcategory of machine learning, which is a subcategory of AI. ANNs arrange layers of algorithms that can learn and make intelligent decisions on their own. Data passes through interconnected layers of neurons, identifying characteristics and information of each layer before continuing to the next. In machine learning, decisions can only be made based on what the system has already been taught. There are many types of machine learning algorithms that can be used, such as regression, logistic regression, classification, decision trees, and more.

How Many Machine Learning Models Are There?

Machine learning can be divided into categories based on how an algorithm learns and performs. The three major recognized categories of machine learning are supervised learning, unsupervised learning and reinforcement learning. Knowing the differences between these models will help you choose the best algorithm for your business. So, how do machine learning models work? Keep reading to find out.

1. Supervised Machine Learning

Supervised machine learning occurs when a human teaches the machine by presenting it with training datasets. In these datasets are example inputs and their corresponding outputs. In this learning method, humans are responsible for labeling data to train the machine’s algorithms. After a sufficient amount of training, the machine should have a strong understanding of the relationships between parameters and how the test data works. Once the supervised machine learning system is deployed, it will continue to use new data to train itself, continuously learning new relationships and using machine learning to find patterns. Furthermore, supervised machine learning can be split into two subcategories: regression and classification. A classification model’s output is a category, such as “black or white,” or “male or female.” A regression model's output would be a real value, such as “height” or “minutes.”

2. Unsupervised Machine Learning

In unsupervised learning, the system is trained with unlabeled data, as opposed to supervised learning where humans are responsible for tagging data. Unsupervised learning algorithms search datasets for any significant connections and use self-organization to identify patterns. This method forces the system to create an internal representation of its world. In addition, unsupervised learning can be divided into two subcategories: parametric and non-parametric unsupervised learning.

• Parametric unsupervised learning – algorithms make assumptions about the mapping of the input-to-output variables and have a fixed number of parameters. This method requires less data and the training process is shorter, however, the results may not be as powerful.
• Non-parametric unsupervised learning – algorithms make few or no assumptions about the target function. This method requires a significant amount of data, making the training process longer, but often results in more powerful models.

The unsupervised learning method is advantageous in cases where manually annotating large datasets is too expensive or time-consuming.

3. Reinforcement Learning

In reinforcement learning, algorithms learn from errors. This method works by teaching a machine to accomplish a multi-step task with a clearly defined set of rules. Data scientists program an algorithm to complete a specific task, giving it positive or negative feedback as it works through task. However, it is up to the algorithm to figure out what steps to take along the way. A prime example of reinforcement learning is when a computer advances to the point where it can defeat humans in online games.

How to Get Data for Machine Learning

Now that you have a better understanding of the machine learning process, let's find out what resources you can use to collect data. There are many places to find open data, but here are a few of the most popular machine learning public datasets to help get you started:

• Google's Dataset Search
• Microsoft Research Open Data
• Amazon Datasets
• UCI Machine Learning Repository
• Government Datasets

Importance of Datasets in Machine Learning

To completely understand machine learning, you need to first understand the data that supports it. Machine learning models rely on four main types of data: 1. Numerical data – any data that can be measured 2. Categorical data – data that is sorted by defining characteristics 3. Text data – words, sentences, or paragraphs that offer insight to the machine 4. Time series data – data points that are indexed at certain points in time

Without these datasets, there would be no machine learning. Data is a major component to machine learning. Teaching a machine to perform an action takes a few steps, all of which depend heavily on datasets.

What Role Do Datasets Play in Machine Learning?

First, training datasets are fed into the ML algorithm, followed by testing datasets to make sure that the model is accurately deciphering the data. Once both datasets have been fed into the system, you can use additional datasets to further sculpt your machine learning. The more data you supply a system with, the faster it will learn and advance. The quality of the data will affect the efficiency of the system, so it's important to beware of bad data.

How Can Machine Learning Be Used?

So, what are machine learning models used for? There are countless machine learning applications that play key roles in several industries such as healthcare, transportation, finance, government, manufacturing, and more. The following are real-world examples of businesses utilizing machine learning technology.

Customer Churn Modeling

Churn modeling is a machine learning application that helps you identify customers that are likely to stop engaging with your business and why. This feedback can provide insight and allow you to take necessary actions, such as implementing discount offers, email campaigns, and other marketing strategies to keep your customers interested.

Recommendation Engines

Recommendation engines analyze vast quantities of data to make predictions about customers' interests or actions. If the algorithm determines that a customer is likely to purchase an item or appreciate a particular piece of content, it will make corresponding suggestions. This machine learning application results in an improved customer experience and better engagement.

Customer Segmentation

Data scientists use classification algorithms to cluster customers into groups that match their characteristics. Algorithms consider various factors such as demographics, affinity, and browsing behavior. Making the connection between personal traits and patterns of purchasing behavior allows companies to implement personalized campaigns as opposed to less effective, generalized campaigns.

NITCO Can Help Your Business Harness the Power of Machine Learning

NITCO has experience with several technologies used in machine learning and can help your organization implement ML to enhance your processes. This learning can be used to power Artificial Intelligence Solutions, AI Chatbots, and Robotic Process Automation for your business.

Contact the NITCO team today to schedule a free consultation regarding the impact ML can have for your company.