Machine Learning (ML) is being heralded as the next big thing, and it’s not hard to see why. After all, Machine Learning teaches computers how to think for themselves. And while this may sound like something out of a science fiction novel, the fact is that Machine Learning is already revolutionising industries all over the world.
In this blog post, we will explore how Machine Learning will shape the world around us in the years to come.
What is Machine Learning?
Machine Learning is a subset of AI similar to Deep Learning, Neural Networks, Natural Language Processing (NLP), and more.
It is the process of teaching computers how to make predictions based on data. It is done by creating algorithms, or sets of rules, that can identify patterns in data. Once the computer identifies these patterns, it can use them to predict new data.
In the past few years, Machine Learning has made great strides in accuracy and efficiency, thanks to advances in both hardware and software. And as data sets continue to grow larger and more complex, Machine Learning will become more important.
It is a hot topic in academia and industry and has grown in popularity in recent years. It’s already being used in several industries, from retail to healthcare, and its applications are only getting more and more diverse.
Machine Learning aims to enable computers to learn automatically without human intervention and adjust actions.
There are three main categories of Machine Learning:
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement Machine Learning
Supervised Machine Learning
Supervised machine learning involves creating models that can learn from data to make predictions.
A classic example of supervised machine learning is spam filtering. Here, we train the machine on a dataset of emails labelled as spam or not spam. The machine then learns to identify patterns in the email content that indicate spam and can then flag new emails accordingly.
Other examples of supervised machine learning include facial recognition, handwriting recognition, and medical diagnosis.
There are many types of supervised machine learning, but some of the most common are:
Classification is a technique where the algorithm learns to assign a class label to input data. Some examples of classification tasks are:
– Determining whether an email is spam or not
– Deciding whether a given image contains a dog or a cat
Regression is a technique where the algorithm learns to predict a continuous value for input data. Some examples of regression tasks are:
– Predicting the price of a house based on its specifications like size, location, and others.
– Predicting how many likes a YouTube video will get
Advantage: It can be used to create highly accurate predictions.
Disadvantage: It requires a large amount of training data to create accurate models.
Unsupervised Machine Learning
Unsupervised learning is where the computer is given a set of data that is not labelled or categorised. This means that the algorithm must find some way to learn from the data without the guidance of humans. The algorithm does it in a couple of ways.
Clustering: It groups together similar data points. For instance, clustering can be used to group customers with similar buying habits.
Dimensionality reduction: This algorithm simplifies complex data by reducing the number of features (dimensions) the data has. This can help visualise data or make predictions on new data points.
Anomaly detection: This is where data is analysed to find outliers or unusual cases. For example, anomaly detection could be used to identify fraudulent credit card transactions.
There are many unsupervised machine learning algorithms, each with advantages and disadvantages. The choice of algorithm will depend on the problem being solved and the desired result.
Advantage: It can find hidden patterns in data, make predictions about new data, and cluster data for further analysis
Disadvantage: It needs large amounts of data and lacks control over results.
Reinforcement Machine Learning
Reinforcement machine learning uses feedback data to improve the performance of a model. This feedback data can be either from a human or another machine learning algorithm.
The goal of reinforcement machine learning is to learn how to map input data to actionable outputs so that the system can get better over time at performing the task it is given.
Reinforcement learning has been used to solve a variety of tasks, including:
-Optimising routes for delivery trucks
-Playing games such as Go and chess
-Controlling robotic arms to perform a task
One of the earliest and most famous examples of reinforcement learning is the Markov decision process, which mathematician Andrey Markov first formalised in the early 1900s. In a Markov decision process, an agent interacts with its environment in a sequence of discrete time steps. At each step, the agent observes the environment’s state and chooses an action from a set of possible actions. The agent’s goal is to find a policy—a mapping from states to actions—that maximises the expected sum of future rewards.
There are two different types of reinforcement learning:
- Positive reinforcement learning
- Negative reinforcement learning
Positive reinforcement learning occurs when the desired behaviour is reinforced by adding a positive reinforcer. The most common positive reinforcer is a reward.
Negative reinforcement learning occurs when an undesired behaviour is prevented or stopped by adding a negative reinforcer. The most common negative reinforcer is punishment.
Advantage: Reinforcement learning can learn from past experiences and use that knowledge to improve future performance.
It can also solve complex problems that are difficult for humans to understand.
It can adapt to changes in the environment and adjust its behaviour accordingly.
Disadvantage: It can take a long time for the machine to converge on an optimal solution.
It can be computationally intensive, making it impractical for some applications.
It can sometimes result in sub-optimal solutions because the machine may only be able to explore some possible options.
What are the main differences?
Supervised learning requires a marked dataset for training.
Unsupervised learning recognises hidden data patterns from an unlabeled dataset.
Reinforcement learning is not given explicit feedback about what is right or wrong but must learn from its own actions and experiences. This makes Reinforcement learning more similar to real-world learning situations, where we often need access to a complete set of correct answers.
Reinforcement learning algorithms are often used when the input and output mapping is non-linear or non-stationary, making them more flexible than supervised learning algorithms.
What’s the future of machine learning?
Machine learning is still in its infancy! We’ve only just begun to scratch the surface of what’s possible with this technology.
In the coming years, we’re likely to see exponential growth in both the capabilities of machine learning algorithms and the amount of data they have to work with.
Whatever the future holds, it is sure to be exciting!
Videos on Machine Learning
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