Machine Learning

Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that allows systems to learn from data and become better at tasks without having to be programmed explicitly to do so. The key distinction between machine learning vs AI is that AI is a much broader notion, therefore including any method that allows machines to exhibit human-like intelligence, while machine learning refers specifically to the subsample that learns from data.

Variety of Methods and Applications of Machine Learning

There are many methods of machine learning, each appropriate for different types of use-cases:

1. Supervised Learning

This method of machine learning uses models trained on a dataset that is labelled for the correct answer. In spam detection, a machine learning model is trained on a dataset that has spam messages and non-spam messages. The model learns to predict spam or non-spam emails based on a dataset with labelled examples. Supervised learning algorithms (e.g. decision trees, linear regression) are widely used for applications such as predictive analytics in business, where data is collected through time and applied to predict future trends.

2. Unsupervised Learning

Unsupervised learning, as opposed to supervised learning, is finding patterns in data without labels. It is used to discover patterns in data when there is not predetermined outcome. For instance, it can be used to identify hidden structures in data such as consumer segmentation. Thus, by studying a customer's purchase behaviors it could be useful as an unsupervised learning model could classify customers into different categories allowing the businesses to devise appropriate targeted marketing message. A number of different machine learning algorithms can be used for this type of analysis such as K-means clustering or hierarchical cluster among others. 

3.  Reinforcement Learning

In reinforcement learning the system learns by interacting with the environment, where it receives feedback through a mechanism of rewards and punishments. Many of the traditional uses of reinforcement learning have been various types of transportation systems, one relevant example is self-driving cars to provide a learning example. In those systems, the cars learn in a real-world environment how to navigate the road and are rewarded based on positive reinforcement when the car learns what makes good driving versus incorrect driving from rewarding and punishing mistakes along the way. 

How Machine Learning Helps Businesses

Here are several ways that machine learning can contribute to business and what it has to offer:

1. Predictive Analytics

One of the most useful methods to leverage in business is predictive analytics. By analyzing the historical data using machine learning models, people can predict the future. Examples include forecasting sales and analyzing trends in the market. One way to learn more is to check out our blog about how machine learning can boost your digital marketing.

2. Computer Vision with Facial Recognition

Computer vision is a useful aspect of machine learning. After training on a data set, the machine learning models can detect objects using computer vision. Depending on the business, tracking and analyzing facial characteristics can be important. Some businesses, particularly in retail, may want to incorporate facial recognition to support customer experiences and interactions or even to deliver personalized advertising. 

3. Recommendation Systems

It is unlikely that you’ve gone online recently without your recommendations being influenced by machine learning which is being used by platforms such as Netflix and Amazon to recommend product or media based on previous behavior. In its most basic sense, recommendation systems work by analyzing user behavior to identify preferences making predictive recommendations using machine learning systems to suggest what the user may want next, as this method recognizes that personalized recommendations ultimately lead to higher engagement, and increased sales.

4. AI Powered Cybersecurity

Another example of machine learning applications is cybersecurity. Machine learning-led improvements are inimically tied to increasing cybersecurity capabilities. These ML systems are required to analyze large and fast processing datasets to identify behaviours and anomalies that provide the potential to pre-empt and mitigate cyber threats in near real-time. These systems constantly adapt using the latest threat data to improve their responses to new emerging cyber- attacks. If you want to explore more about ideas regarding how machine learning has had a transformative effect in AI powered cybersecurity, explore our various offerings on our AI and Machine Learning services page.

The Importance of Machine Learning Models and Algorithms

Machine learning functions via machine learning models and machine learning algorithms, which are the basis of most machine learning applications. 

1. Machine Learning Models

These are the mathematical representations of real occasions that a machine learning algorithm produces. Machine Learning models can include decision tree models, support vector machine models, neural network models, ensemble models, etc. Machine learning models learn from historic data to make predictions on unseen data. Businesses use machine learning models to automate processes and make data-driven decisions by modeling its operations.

2. Machine Learning Algorithms

These are the methods to train machine learning models. The algorithm determines how the model will learn from the data, how it will adjust the parameters upon learning from the data, and how it will predict. Examples of machine learning algorithms are linear regression, k- nearest neighbors, random forests, and deep learning algorithms such as convolutional neural networks (CNNs). Each algorithm is best suited to complete varying tasks, such as image recognition, natural language processing, etc.

Machine learning can also be utilized in more niche fields. For example, consider our DeepSeek AI platform which takes AI to the next level with new machine learning capabilities, and is suitable for businesses who want to utilize ML within their workflow and want to implement the automation solution using n8n on a GCP VM. Our n8n on GCP VM setup guide has useful implementation information. 

Conclusion

Machine learning is an exciting field that is quickly moving along and helping businesses change their processes. Everything from understanding what is machine learning, its practices and methodology, how machine learning algorithms and machine learning models are executed; business leaders can harness machine learning technology to generate productivity gains, improve customer experience and stay ahead of the competition. Machine learning is here to stay whether it be predictive analytics, recommendation engines, or ai-powered cybersecurity.For more information on AI and machine learning topics please see our Center of Excellence.