It can be daunting at first when you think of machine learning; how is a mechanical piece of machinery, programmed with 1s and 0s, able to learn?
Machine learning has come on in leaps and bounds in recent years and will be central to many industries in the future.
Machine learning is a complex field, but at its core, it is simply a way for computers to learn from data. Instead of a user inputting a variable as per traditional programming, the computer uses data.
A machine learning algorithm will take a dataset as input and try to find patterns within it. The more data, the better, as this will allow the algorithm to find more complex patterns.
The computer can then use these patterns to make predictions about future data. For example, if you fed a machine learning algorithm data about housing prices, it could, in theory, predict how much a house will cost in the future.
There are different machine learning algorithms, but broadly, we can split them into two categories: supervised and unsupervised.
In this article, I will cover the fundamentals of machine learning and explain how machine learning works in very simple terms, summarising the topic without digging deep into the programming. I will cover:
- How do machines learn in machine learning?
- Is machine learning AI?
- Can the machines think for themselves?
- When should you use machine learning?
- What are the ethical concerns with machine learning?
- How can you get started with machine learning?
- What are some real-world applications of machine learning?
How Do Machines Learn In Machine Learning?
Machine learning works by feeding a computer some data, which it then uses to learn and improve. The more data that is fed into the machine, the better it becomes at learning and understanding patterns. Feeding data into a machine and letting it learn is called ‘training’.
We train machine learning algorithms using a dataset; this dataset contains both input features and corresponding output labels (for supervised learning), or just input features (for unsupervised learning).
The aim is to find a mathematical model that can accurately map inputs X onto correct outputs Y with high precision and recall. We then use this model on new unseen data to make predictions about outputs Y′ for those inputs X′.
There are two main types of machine learning: supervised and unsupervised.
1. Supervised Learning
Supervised learning is where we give the computer a set of training data, which includes the correct answers. The machine can then use this data to learn and generalise about new situations.
In supervised learning, the aim is to learn a function from labelled training data. The training data comprises a set of input objects (also called features) and a set of corresponding output labels.
There are two main types of supervised learning algorithms: regression and classification.
- Regression techniques predict continuous responses, for example, changes in temperature or algorithmic trading.
- Classification techniques classify input data into categories and are used to predict discrete responses, such as colour recognition.
2. Unsupervised Learning
Unsupervised learning is where we give the computer data with no labels or answers. It will have to find its own patterns and structure in the data.
In unsupervised learning, the aim is to learn a function from unlabelled training data. The training data comprises a set of input objects (also called features) with no corresponding output labels.
The goal is to learn some meaningful structure from the input data so that we can reason about it.
Clustering is the most common unsupervised learning technique.
- Clustering is grouping data together so that it is easier to understand. The computer will look at all the data and group it together based on similar characteristics. This can be helpful when you have a lot of data and don’t know where to start.
Is Machine Learning AI?
Artificial intelligence (AI) is a branch of computer science that deals with the design and development of intelligent computer systems. AI systems can perform tasks that normally require human intelligence, such as understanding natural language and recognising objects in images.
Machine learning is a type of AI. AI is a broad field that covers any algorithm that can learn and make predictions.
Machine learning is a subset of AI that deals with algorithms that learn from data. These algorithms are able to automatically improve given more data.
Can The Machines Think For Themselves?
No, machines cannot think for themselves. They can only learn from the data that is given to them. Although machines thinking for themselves is not too far away, with current machine learning techniques, the machines are not thinking for themselves but depend on user input data.
The goal of machine learning is to build algorithms that can automatically learn and improve from experience. This differs from traditional programming, where the programmer has to write explicit code to tell the computer what to do.
We train the machine learning model on a dataset, which is a collection of data that the computer uses to learn. The training dataset is usually different from the dataset that is used to test the accuracy of the model.
The test dataset is used to see how well the model performs on new data. The goal is to have a model that generalises well, which means it can make accurate predictions on unseen data.
This may give the impression that the machine is thinking for itself, but programmers control the machine learning model and optimise it to give the best response.
When Should You Use Machine Learning?
You should use machine learning when you have a lot of data that you want to make automated predictions about.
Machine learning is well suited for tasks where it is difficult or impossible for a person to write a program to do the task.
Some examples of tasks that machine learning is good at are:
- Recognising objects in images
- Translating speech to text
- Predicting whether a person will like a movie based on their past watch history
What Are The Disadvantages Of Machine Learning?
The major disadvantage of machine learning is that it’s difficult to understand how the algorithm is making its predictions.
This is because the algorithm is just looking for patterns in the data and does not have to understand why those patterns exist.
Another disadvantage of machine learning is that it can be slow and require a lot of computing power.
This is because the algorithm has to process a lot of data in order to learn from it.
What Are The Ethical Concerns With Machine Learning?
There are some ethical concerns with machine learning, particularly around personal data and privacy.
Machine learning algorithms can learn a lot about a person from their data. This includes things like their interests, their preferences, and even their identity.
If this data falls into the wrong hands, it could be used to exploit people.
There are also concerns about biased data. Machine learning algorithms can learn from and amplify the biases that exist in training data.
This can lead to things like race or gender based discrimination.
How Can You Get Started With Machine Learning?
There are a few ways to get started with machine learning.
If you’re a programmer, you can start by implementing some of the common machine-learning algorithms yourself. This will help you understand how they work and what they are doing.
You can also use one of the many machine-learning libraries that are available. These libraries will give you access to ready-made algorithms you can use in your own projects.
If you’re not a programmer, you can still get started with machine learning. There are many services that offer pre-trained machine-learning models you can use.
For example, Google has a service called Cloud Vision that offers an API for image recognition.
You can also find many machine learning datasets online that you can use to train your own models.
What Are Some Real-World Applications Of Machine Learning?
Some real-world applications of machine learning include:
- Fraud detection
- Predictive maintenance
- Stock market prediction
- Disease diagnosis
- Spam detection
- Object recognition
- Sentiment analysis
- Recommendation systems
- Customer segmentation
- Driverless cars
Final Thoughts
Machine learning is a process by which computers learn from data to make predictions. We can use it for tasks that are difficult or impossible for a person to write a program to do.
Some disadvantages of machine learning include its slow speed and the need for a lot of computing power.
You can get started with machine learning by implementing some common machine learning algorithms yourself, using one of the many machine learning libraries available, or using a service that offers pre-trained models.
Some real-world applications of machine learning include fraud detection, predictive maintenance, stock market prediction, disease diagnosis, spam detection, object recognition, sentiment analysis, recommendation systems, and customer segmentation.
Machine learning is an amazing advancement in computer AI and will be a big part of all future industries.