If someone asked you whether you’ve ever interacted with machine learning, what would you say? Would you even know what it is? Unless you work in the world of technology or use it to streamline your business, you might not fully understand what machine learning is or how it works.
Chances are that you encounter machine learning (ML) on a daily basis, even if you’re not aware of it. From asking Alexa to play a certain song to engaging in your social media feed to getting new show suggestions from your favorite streaming service, machine learning is driving ease of use across every industry. It’s not surprising then that the global machine-learning market is expected to grow from $21.7 billion today to a staggering $209.91 billion by 2029.
Machine learning has advanced significantly since Alan Turing first created the “Turing Test” in 1950 to determine whether a computer was capable of real intelligence. Since then, movies like “2001: A Space Odyssey,” “The Matrix,” and “Ex Machina” have caused the idea of learning machines to slowly creep into our consciousness–and usually in an ominous way that suggests they’re getting too smart for our own good. Now, six decades later, the role of artificial intelligence is confirming that machines really do have the ability to self-learn and replace humans in many ways–something that is equally as fascinating as it is terrifying.
While it may be easy to live in fear of the unknown, the truth is, most people today appreciate the convenience artificial intelligence brings to their lives. And many industries rely on this technology to manage their businesses more effectively. Whether it’s a financial institution enhancing its fraud detection, a healthcare provider offering earlier diagnoses, or an e-commerce brand amping up its personalization, machine learning has many applications that accelerate business operations and reduce manual workloads.
With nearly half of all businesses using some form of ML or AI, understanding the basics of what exactly machine learning is is becoming increasingly important. Keep reading to learn the fundamentals of machine learning, including what it is, how it works, and its relationship with artificial intelligence.
What is machine learning?
Machine learning is all about creating models that can make predictions or decisions without being explicitly programmed to complete a specific task.
Essentially, it trains computers to think similarly to humans, which is by understanding and improving upon past experiences. Rather than relying on direct instructions, ML systems use statistical methods and algorithms to optimize their efficiency over time. The learning process involves feeding large volumes of data into a model so it can identify patterns that enable it to make well-informed decisions or projections. The more data it receives, the better it becomes at making accurate choices.
Machine self-learning is commonly used synonymously with the broader concept of machine learning. However, it can also refer to the autonomous ability of a machine to learn, adapt, and refine its capabilities over time, based on its experiences and the data it encounters. This key feature enables systems to consistently improve their performance without continual human interference, making them more effective in a wide range of applications.
The Difference Between Artificial Intelligence and Machine Learning
Though artificial intelligence (AI) and machine learning (ML) are interconnected, they’re not the same.
AI is the comprehensive discipline of computer science that focuses on the creation of machines or systems that can perform tasks usually executed by humans. To achieve this, a diverse scope of approaches and techniques are used to mimic human intelligence, including learning, insight, rationalization, problem-solving, speech recognition, and natural language understanding.
Machine learning is a subset of AI that specifically focuses on algorithms that can learn from data. This allows the AI to not only analyze data, but use it to gain insights and ultimately “get smarter” without the need for further programming. Simply put, artificial intelligence is the replication of human behavior, and machine learning is the underlying process and mathematical tools to do just that.
To put this into perspective, let’s use reading as an example. At some point, you learned how to read. Later on, you picked up a book and read it. While both are forms of human intelligence, only the former is “learning”, while the latter–reading a book–is the intelligent application. Just as human learning is an essential element of human intelligence, machine learning is the same to artificial intelligence.
How does machine learning work?
In machine learning, you have big data on one side and a problem you think that data can solve on the other. Generally, big data has a set of distinct characteristics:
- Volume – The quantity of generated and stored data that determines its value and potential insight, and whether it can even be considered big data.
- Variety – The type and nature of the data, usually extracted from text, images, video, and audio, that helps analyze it to effectively use the resulting insight to their best abilities.
- Velocity – The speed the data is generated and processed, which is produced more continually than small data and is available in real-time.
- Veracity – The extension of big data that includes data quality and data value.
To further understand this, let’s break down some of the key components that make up machine learning.
Machine Learning Algorithms
Machine learning hinges on algorithms, which are designed to classify things, find patterns, predict outcomes, and make informed decisions. These mathematical models enable computers to learn patterns and forecast outcomes based on the data they ingest.
Because machine learning isn’t a one-size-fits-all approach, different techniques are utilized within different algorithms. These can be categorized into three main types of algorithms: supervised learning, unsupervised learning, and reinforcement learning.
- Objective: To make predictions or classify new, unseen data based on the patterns learned from a labeled training set
- Key Differentiators:
- Learns from a labeled dataset where the input data is paired with corresponding output labels
- Receives explicit feedback in the form of labeled data
- Common Tasks:
- Regression, which predicts a continuous output
- Classification, which assigns labels to inputs from a predefined set
- Example Applications:
- Predicting house prices
- Spam detection
- To examine and uncover the inherent structure of the data
- Key Differentiators:
- Works with unlabeled data
- Explores the inherent structure or patterns within the data without explicit guidance
- Common Tasks:
- Clustering, which groups similar data points based on specific criteria
- Dimensionality reduction, which decreases the number of features while retaining essential information
- Example Applications:
- Risk mitigation
- Inventory stock management
- To learn a strategy or policy that maximizes cumulative rewards
- Key Differentiators:
- Involves an agent that learns by interacting with an environment
- Receives feedback in the form of rewards or penalties based on actions taken
- Common Tasks:
- Doesn’t have specific tasks, but instead determines an ideal outcome by trial and error
While all three of these techniques vary, they all share the underlying belief that machines can improve their performance by learning from experience.
In AI, heuristics act as guiding principles in various stages of the machine learning pipeline. From feature engineering to hyperparameter tuning, heuristics provide rules of thumb and strategies to enhance model performance. They also play a pivotal role in determining how data is sampled, features are represented, and models are combined in ensembles.
Algorithms and heuristics work in tandem, with algorithms providing the mathematical foundation and heuristics offering practical insights to navigate the complexities of real-world data and optimize machine learning systems for effective performance, in turn, leading to more intelligent decisions.
The Machine Learning Process
The machine learning process can be summarized in three simple steps:
1. Data Collection – Data is divided into two sets: training data to teach the model, and testing data to evaluate its performance.
2. Model Training – The model is exposed to the training data and learns to recognize patterns or relationships within the information.
3. Prediction – Once the model is trained, it can be fed new, unseen data to make predictions or decisions, with the goal of generalizing its learning to handle new, previously unseen inputs effectively.
Let’s say a peach and a nectarine are placed in front of you. You’ve never seen these fruits before, but your mind starts to differentiate them. While similar in shape and color, you notice the nectarine is smooth while the peach is a bit fuzzy. This leads you to determine that texture is one feature that separates them apart. Soon after, a plum is set before you. Though comparable in shape to the other two fruits, you notice its deep purple color, which becomes another distinguishing trait. Lastly, you’re given a pineapple, which has a completely different shape than the others. This becomes another characterization that sets it apart.
This example wasn’t meant to get you to increase your daily fruit intake (though pineapple sounds really good right now) – it’s a simple analogy to show you how machine learning works.
Why is machine learning so important?
What makes ML valuable is its ability to notice things that we humans may have overlooked. Thanks to advances in smart technology like deep learning and natural language processing, ML is freeing up time for businesses to prioritize tasks that improve the quality and efficiency of their products and services.
Besides being able to process and analyze massive amounts of data at rates no human can match, machine learning works 24/7, without the need for breaks or rest. For e-commerce businesses specifically, ML has vastly helped enhance the customer experience and optimize business operations, thanks to many key applications, including:
- Customer segmentation
- Predictive analytics
- Personalized product recommendations
- Search relevance
- Chatbots and virtual assistants
- Dynamic pricing
- Supply chain optimization
- Data protection and security
And the best part? As machine learning technology continues to advance, its applications are going to expand even further.
Byte-sized thoughts, or big time opportunities?
Why not both? Machine learning is a dynamic and rapidly evolving field with the potential to transform the way we live our lives and operate our businesses. From its data-driven training approach to the diverse algorithms and heuristics it employs, ML is leading the way in technological advancement. And we’re just scratching the surface.
As we continue to generate and analyze vast datasets, the capabilities of machine learning will undoubtedly flourish. Take some time to dive deeper into the intricacies of machine learning, keeping in mind that practice and hands-on experience are crucial for leveraging all that this exciting new technology has to offer. While AI can instill fear of the unknown, try to embrace human ingenuity and machine learning coming together for extraordinary opportunities.
Granify: Machine-Learning Minds for E-Commerce Finds
Granify's machine learning technology has been a game-changer for some of the world’s largest e-commerce brands, offering a sophisticated solution that significantly enhances the shopper experience and boosts conversion rates. Our advanced algorithms and real-time data analysis can identify patterns in user behavior, allowing us to deliver personalized and timely recommendations that minimize cart abandonment and ultimately drive higher sales. We’d love to help you too, so ask us today how we can be a valuable asset for your platform.
You May Also Like
These Related Stories