Machine learning has revolutionized industries across the board, offering powerful tools to professionals across nearly every industry.

Before it’s used, however, every machine learning model must first be built, trained, and tested. Unsurprisingly, each model is different, and is built for varying needs and goals. 

But which to choose from? Based on your needs, you might use one, two, or all of the machine learning methods to build your model. And if you’re not well-versed in building machine learning models, the answer to that question may feel even more convoluted. 

Let's delve into some key machine learning approaches and discover which ones are best suited for each profession.

What exactly is a machine learning model? 

At its core, a machine learning model is a digital brain that mimics human learning patterns. Just as we learn from experiences, these models learn from data. Imagine it as a virtual apprentice, fed with information, and tasked with understanding patterns, making predictions, or guiding decisions. It's like teaching a pet new tricks, but in the realm of algorithms and data.

These models are created using sophisticated algorithms that enable them to recognize complex patterns within data, transforming raw information into valuable insights. Think of them as the artist's palette, painting vivid pictures of trends, relationships, and predictions hidden within the data canvas. As they train on more data, these models grow wiser, becoming adept at adapting to new scenarios and challenges.

In essence, a machine learning model is your trusty sidekick in navigating the sea of data. It's the compass that points you to trends, the crystal ball that foresees outcomes, and the advisor that aids decision-making. So, whether you're deciphering customer behavior, optimizing supply chains, or diagnosing illnesses, a well-crafted machine learning model is your ticket to unlocking the secrets hidden within the data-driven universe.

4 machine learning models, many purposes

1. Supervised Machine Learning: Guiding insights for marketers

In supervised learning, algorithms harness the power of labeled training data to predict outcomes accurately. For marketing professionals, this means:

• predicting customer behaviors 
• optimizing ad targeting 
• tailoring campaigns

Real-world example: Imagine a marketing team using historical customer data to predict which customers are likely to respond positively to a new product launch. By training a supervised model on past purchase patterns and product preferences, marketers can focus their efforts on the right audience, leading to increased conversion rates and better ROI.

Want to learn more about how machine learning can help you achieve your marketing goals? Read about 6 machine learning marketing use cases here.

2. Unsupervised Learning: Crafting strategies for product development

Unsupervised learning thrives in situations where data lacks predefined output values. In the realm of unsupervised learning, the algorithm derives knowledge from unlabeled data, devoid of predefined target outcomes. This process involves the algorithm uncovering latent and concealed patterns, configurations, or associations within the data, all without direct instruction or human interference. 

This technique is a goldmine for product development professionals seeking to uncover hidden patterns and trends in customer preferences. 

Real-world example: Imagine an e-commerce company using unsupervised learning to analyze customer browsing and purchasing behavior. By grouping customers into segments based on shared preferences, the company can tailor product recommendations, improving cross-selling strategies and enhancing the overall shopping experience.

3. Semi-Supervised Learning: Empowering data scientists

Data scientists often grapple with limited labeled data and abundant unlabeled data. Semi-supervised learning bridges this gap by using a mix of labeled and unlabeled data. This approach is invaluable when developing models in scenarios where obtaining labeled data is costly or time-consuming. 

Real-world example: For instance, in the SaaS world, a data scientist might use a semi-supervised model to detect bugs or security threats. By training on a small set of labeled positive cases and a larger set of unlabeled data, the model can identify potential cases more effectively, aiding in early detection and quick resolution.

4. Reinforcement Learning: Guiding decision-making for data analysts

Reinforcement learning encompasses the process of instructing an agent to engage with an environment, and acquiring knowledge via iterative experimentation. Through this trial-and-error approach, the agent obtains feedback in the shape of rewards or penalties, contingent on its actions. This framework empowers the agent to acquire optimal behaviors or strategies as it adapts over time.

Data analysts can benefit from this method by optimizing complex systems through trial and error. 

Real-world example: Consider a supply chain scenario where a data analyst uses reinforcement learning to determine optimal inventory levels. The analyst's agent makes decisions on when to reorder products based on feedback in the form of stockouts and excess inventory costs. Over time, the agent learns to make decisions that balance inventory costs and customer satisfaction.

Putting your model to use for real results

Whether you’re in marketing, product, or data science, machine learning can be incredibly beneficial to you. Before you settle for just one model, know this: every model can be used by any team in any industry—you don’t have to choose just one!

To fully leverage the potential of ML and its models, you need a platform that guarantees algorithms operate with high-quality data, yielding real-time outcomes and practical insights. This is precisely where a decision intelligence platform, like Scuba, steps into the picture.

An ML-powered decision intelligence platform lets you:

• Unify all of your data streams, giving ML algorithms access to clean and cohesive data
• Monitor ad placements to keep your funnel full and of ideal prospects
• See a 360° view of customers' journeys and use AI to discover pain points
• Build custom KPI dashboards to track marketing performance in real-time
• Keep your marketing efforts compliant with security and privacy regulations

Learn how the right decision intelligence platform can help you use machine learning to elevate your business outcomes.