machine learning

What is Machine Learning? Everything You Need To Know

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Machine learning (ML) is a facet of artificial intelligence that uses statistical algorithms to analyze big data and make predictions.

Simply put, it is a technology that learns from examples – and gets smarter each time – so that businesses and other organizations can make data-driven, strategic decisions.

In this post we'll examine some key questions to help you anwer the question of what is machine learning and how to leverage it, such as:

The ML algorithm starts by observing data and learning from specific examples and direct experience. It will then begin recognizing patterns and trends so that when new data is presented in the future, it can make predictions and provide valuable insights.

The goal of machine learning is the have the algorithm learn automatically, without any intervention from humans. This capability allows it to adjust predictions and outputs accordingly!

When it comes to training a machine learning model, the training data is not limited to numbers. The algorithms can find patterns in everything from words and clicks to images. If you can store the information digitally, you can likely use it to train algorithms.

It works best, though, when you process the data first so that only the features relevant to the model's goal are included.

What is machine learning? In broad terms, there are three types of machine learning:

  1. supervised learning
  2. unsupervised learning
  3. reinforcement learning.

Supervised Machine Learning

The most common type of machine learning style is supervised learning. With this option, you use a pre-labelled set of data to teach the algorithm. The training data set uses past examples so that the model can pick up on trends and start to recognize underlying relationships. All this data must be labeled so that the machine learning model knows exactly what to look for - think of it like training a dog to sniff out a specific scent.

Once training is complete, the machine learning model can ingest new data and make predictions for future events. It can also compare the predictions it makes with the correct, intended output to modify its process accordingly.

Unsupervised Machine Learning

Unsupervised learning algorithms learn by analyzing unlabelled data and seek to determine the relationships and trends on their own. This is a great option when the model's goal is to find hidden relationships or gather additional insights that may not be obvious to a human.

Rather than looking for a correct answer, unsupervised learning focuses on exploring the data in different ways to draw inferences. This method has gained traction in cybersecurity since it allows the algorithm to discover new patterns consistently.

Reinforcement Learning

The newest method for training algorithms is reinforcement learning. This style allows the machine learning model to learn through trial and error, and the goal is to meet a specific objective.

The model is given free rein to test out various options, and it will be penalized or rewarded depending on whether it reaches its objective. The model can interact with its environment to allow data scientists to find ideal behaviours within specific scenarios to generate the maximum performance.

Reinforcement learning was used by Google's AlphaGo, an artificial intelligence program that could beat top human players in the game Go.

What Are Deep Learning & Neural Networks?

Deep learning takes machine learning to the next level by amplifying the algorithm's ability to recognize even the tiniest patterns. A deep learning algorithm uses neural networks that analyze data at multiple levels.

A neural network is a sophisticated type of machine learning that works similarly to the neural network of the human brain. It was inspired by the neurons in our minds that allow us to make impressive connections and link data together. They are what enable deep learning to occur.

These advances in artificial intelligence and machine learning technology enable tools like speech recognition, information retrieval, sentiment analysis, and more.

Have you ever wondered how your Amazon Alexa can understand your questions and provide you with a quick response? The key is deep learning and neural networks! There are also impressive implications in personalized healthcare, robot reinforcement learning, and other advanced algorithms.

These algorithms work together to find trends and underlying relationships using nodes that mimic how humans think. Let's review two types of neural networks: feed-forward networks and recurrent neural networks.

A feed-forward neural network organizes its neurons in two separate layers. It starts with an input layer then the data flows through many hidden processing layers. There is typically one output layer, and any outputs that result will flow directly into the next layer.

With this machine learning algorithm, some connections may skip over one or several intermediate layers using shortcut connections.

Recurrent neural networks are unique because of their use of feedback loops. The neurons can influence themselves both directly or indirectly through the next layer, and this type of algorithm is commonly used with time-series data. For instance, recurrent neural networks may help predict future stock prices or forecasting sales.

How Can Businesses Use Machine Learning?

Although artificial intelligence and machine learning can sometimes seem like the technology of the future, businesses around the world are already implementing it today.

This type of technology has been around for many years, but it used to be too expensive for average organizations to utilizing their operations. However, improvements in technology and computing power have made it so anyone can access machine learning!

Although many applications of this technology are geared towards data scientists, almost every industry and business can find ways to use machine learning models to help them make strategic decisions and gain a competitive advantage.

Here are some of the most helpful ways machine learning can impact businesses:

Customer Service

Have you ever been shopping online and notice a chatbot feature? Generally, businesses put these in place to help customers find the answer to basic questions or process an order. The average person uses chatbots regularly, and ML algorithms drive them.

The machine learning models that run chatbots can learn from previous conversations and understand human speech. They can take everything that they have learned to understand it answer your question, and they have been optimized to help you in the most efficient way possible!

Process Automation

Machine learning algorithms can help businesses automate repetitive and time-consuming processes so that their employees can focus on more value-added tasks.

For instance, this technology can automatically screen invoices to detect vendor or purchase order numbers. Based on this information, the machine learning model can determine which department is responsible for paying this invoice and process the workflow accordingly.

Similarly, machine learning models can automatically screen documents like resumes to pick up keywords that align with this specific job description. Allowing technology to take care of the initial screening process will free up time for your human resources professionals to conduct a more thorough analysis once the applicant pool has been narrowed down.

Predictions

Another valuable advantage that machine learning algorithms give businesses is the ability to make predictions about their customers.

Your organization can collect data about customer turnover, average customer revenue, fraud detection, and other valuable metrics to teach an algorithm how to flag important milestones. A machine learning model can predict when customers are likely to cancel their subscriptions based on specific behaviours. It can also determine the best time to send clients a coupon because they are likely to purchase something.

Machine learning also impacts customer experiences through predictive analytics. Think of this as the tools that allow Amazon to recommend products that you might like or Netflix to choose the next show that is just what you were looking for.

Every time you click on a show or browse through a catalogue of products, an ML algorithm analyzes this information in the background to predict what your likes and dislikes are. It can figure out what you are looking for and make recommendations to make the process easy and convenient for you.

What Are the Most Useful Machine Learning Algorithms?

So, what are the most useful machine learning algorithms? The short answer is it depends on the goal of the model.

As you may have guessed, there are many different ways to teach machines to make predictions, and whether you decide to use neural networks or a simple decision tree will vary based on your unique business needs. It may not always be obvious what the right answer is, so you may need to try a few different algorithms to see which one works best for your objective.

If the algorithms are not performing well or achieving the desired outcome, it might be due to problems with the training data set. Perhaps there is not enough information for the machine learning model to recognize trends, or the data skews in a way that prevents it from producing valuable outputs.

Some of the most common machine learning algorithms include classification, regression, and clustering.

Classification Algorithms

Classification algorithms are some of the most basic when it comes to machine learning. This type of model uses a supervised learning algorithm, requiring it to choose between two or more classes. In some cases, it may include determining the probability for each class.

These machine learning models may use decision trees to determine their outputs or more complex calculations like logistic regression, support vector machine, K-nearest neighbours, random forest, or logistic regression.

Your email provider uses classification algorithms to determine whether or not an email is spam mail. If the email is not spam, the instruction is to move it to your inbox. However, if the algorithm determines that the message is junk, it will instead move to your spam box.

Regression Algorithms

Regression algorithms are another type of supervised learning that teaches machine learning models to predict a number. Linear regression is a simple calculation that will provide fast results, but it may not work well with complex data sets.

Other options include learning vector quantization, Naive Bayes, LARS lasso, random forest, and elastic net. There is some overlap between classification and regression algorithms.

Regression algorithms will predict things like a customer's age based on the purchases that he or she makes. It can also determine the likelihood that you would like a specific show or movie based on your previous viewing history.

Clustering Algorithms

A clustering algorithm is an unsupervised learning option that requires the model to group similar data points. K-Means Clustering is the most popular calculation in this category, but other methods include mean-shift clustering, hierarchical agglomerative clustering, and gaussian mixture models.

When you utilized this algorithm, there is no predetermined right answer. The goal is to divide the data set into natural groups or clusters, so you can evaluate them and make inferences.

How Can I Get Started with Machine Learning in AWS?

That was a lot of information, but hopefully now you understand what machine learning is, how businesses can use it, and some examples of the best algorithms. Now you may be wondering how to get started.

Getting started with machine learning in AWS is a great option. AWS provides users with a broad set of machine learning services so that everyone has access to technology that can enhance their ability to do business, and you may not need a dedicated machine learning engineer or data scientist to do it.

Their product offerings include tools to help you build, train, and deploy machine learning algorithms, infrastructure for your machine learning, and access to deep learning frameworks.

They offer a free tier option that includes some basic tutorials and simple solutions but figuring out how to move forward on your own can be a little bit complex. The first thing you should do when taking on a machine learning project is to hire the consultants at Pilotcore with specific experience with AWS. We've helped clients all over the world incorporate innovative machine learning technology into their businesses. We'll help you make this process seamless and ensure that you get everything done right the first time around!

Learn more about our services and reach out for a free consultation to get started.