Machine Learning: Understanding the Concept

A term you’ll hear frequently nowadays is Machine Learning, a concept introduced by Arthur Samuel in 1959, which he defined as “the ability of a computer to learn without being explicitly programmed.” Though this concept has existed for over 50 years, it has significantly evolved in recent times due to exponential advancements in computing power.

Machine Learning has seamlessly integrated into our daily lives, often without us even realizing it. According to a HubSpot study, 63% of people use technologies based on Machine Learning, such as Siri, Apple’s virtual assistant, or interactions with chatbots via platforms like Facebook or e-commerce websites. It’s also used for discovering new cures, translating languages, and many other applications.

The Importance of Data in Machine Learning
Any software program aiming to learn requires a key ingredient: data. This is why Machine Learning, a branch of Artificial Intelligence, is closely associated with Big Data.

How Do Machines Learn with Machine Learning?
To understand how machines learn, it’s essential to distinguish between two types of data:

1. Structured Data:
   These are the most commonly used by businesses and are typically found in databases. Organized in rows and columns, they are easy to process with data mining tools. For example, a CRM system with client names, emails, phone numbers, and past invoices represents structured data.
2. Unstructured Data:
   This refers to binary data without an internal structure, essentially a chaotic mass of data that lacks utility until processed and organized. For instance, emails exported with only their “subject” and “message” fields are unstructured until a data mining program processes and categorizes them.

A Deloitte study reveals that 90% of the world’s data comes from unstructured sources, making it vital to combine data science techniques with Machine Learning to organize and analyze this information effectively.

Types of Machine Learning

1. Supervised Learning:
   This method requires a training phase where labeled datasets are introduced to the system. For instance, to teach a program to differentiate between cats and dogs in photos, you’d need to input thousands of images labeled as “cat” or “dog.” The program then uses this training to classify new images.
   • Classification: Identifying categories (e.g., classifying customers as “interested” or “not interested”).
   • Regression: Predicting continuous values (e.g., forecasting electricity consumption based on past usage).
2. Unsupervised Learning:
   This approach doesn’t involve a training phase. Instead, the machine analyzes data to find patterns or clusters. For example, grouping customers based on purchasing behavior without predefined labels. This is known as Clustering, which is useful for data exploration and visualization.
3. Reinforcement Learning:
   Mimicking human learning, this method involves a reward-based system. The machine improves its performance through trial and error, learning to make better decisions over time. This approach is used in autonomous vehicles and robotics.

Applications of Machine Learning

• Classification: Identifying potential customers for a product.
• Regression: Predicting future behaviors, such as annual electricity usage.
• Clustering: Grouping customers or products based on similarities.
• Recommendation Systems: Suggesting products (e.g., Amazon’s “You may also like”).
• Profiling: Identifying typical behaviors of a customer segment.
• Causal Modeling: Determining whether marketing campaigns directly influenced sales.
• Link Prediction: Suggesting new connections (e.g., LinkedIn’s “People You May Know”).

SmartPanel and Machine Learning
Our SmartPanel platform simplifies the representation of complex structured and unstructured data, identifying patterns and grouping revenue sources to help you pinpoint the most profitable channels. It also provides accurate business forecasts for the entire year.

If you’re interested in applying Machine Learning or predictive techniques to your business, SmartPanel offers a no-obligation data consultancy. Contact us through our website to learn more.