Machine Learning (ML) is a branch of artificial intelligence that involves the development of algorithms that are adept at learning and improving independently. This technology has been around for a while, but it is only recently that it has started to gain mainstream attention. In this article, we will give you a brief introduction to Machine Learning and its potential applications.
Machine Learning algorithms are often used in conjunction with statistical methods and other data analytics tools to extract meaningful insights from large amounts of information. This kind of analysis helps us make decisions based on past events or predict future outcomes, but it requires an enormous amount of computing power.
Our sister site, eWeek, has a great guide to data analytics if you want to learn more.
What is Machine Learning?
Machine Learning is a field of artificial intelligence that emphasizes providing computers the flair to learn from their past experiences and improve themselves due to those learnings. Face recognition, spam filtering, prediction, and other applications rely on Machine Learning algorithms.
In its simplest form, Machine Learning may be defined as a branch of artificial intelligence that automates the development of analytical models and involves giving computers the ability to learn from their past experiences and then improve themselves from those experiences.
It is a branch of artificial intelligence built on the concept that a computer can learn from data, be able to determine and recognize patterns, and then make decisions with hardly any human involvement.
You can use Machine Learning for the following:
- Classification: identifying what category an item belongs in (for example, whether an email message is spam or not)
- Clustering: grouping similar items together (for example, grouping products by color)
- Prediction: forecasting future behavior based on past behavior (for example, predicting how many people will visit your website on any given day)
Read: Introduction to Machine Learning in Python
Why is Machine Learning Important?
Machine Learning is important because it allows computers to automatically improve their performance on tasks without being explicitly programmed to do so. This can save a lot of time and effort for developers, as they do not need to write code for every little improvement that needs to be made. Additionally, Machine Learning can lead to more reliable and accurate systems, as they are constantly improving themselves.
There are many different types of Machine Learning, such as neural networks, decision trees, and k-means clustering. Each algorithm has its own strengths and weaknesses, so it is important to choose the right one for the task at hand.
What are Some Machine Learning Methods?
There are several Machine Learning methods, each with its own pros and cons. The most common Machine Learning methods are supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning can be defined as a form of Machine Learning in which the data is labelled before the computer learns from it. This form of learning is often employed for classification tasks, including face recognition and fraud detection. Supervised learning can be classified into Regression and Classification.
Unsupervised learning is a subset of Machine Learning in which there is no label on the data and the algorithm must learn from it. You can take advantage of unsupervised learning to solve clustering issues, such as categorizing clients based on their purchasing habits.
Reinforcement learning is a type of Machine Learning where the algorithm learns by trial and error, receiving rewards or punishments as it goes.
Read: Is the Google Data Analytics Professional Certificate Worth It?
How does Machine Learning Work?
The following steps summarize how Machine Learning works:
- Collecting data: Collect data from multiple sources, including text, images, audio, sensor data, etc. This data could be structured or unstructured or both. However, ensure that the data collected is reliable since your Machine Learning model needs the correct data to generate the right patterns.
- Preparing the data: In this step, you should prepare the data for consumption by a machine; this may involve extracting features like colour or shape in image classification problems or language models in case of textual data. Additionally, clean the data periodically so that any unused or duplicate data is removed. It also involves preprocessing operations like normalization and transformations on the dataset that are required before starting the training cycle on it.
- Splitting the data: In this step, the data is separated into two sets: a training set and a testing set. You can take advantage of the training set to train the model. The testing set is used to evaluate how accurate the model is.
- Selecting a Model: In this step you should select a model relevant to the task you need to accomplish. You should select the model based on the task at hand such as, image recognition, prediction, speech recognition, etc.
- Training the Model: Next, you should train the model. To do this you would typically provide the prepared data to the selected model so that your Machine Learning algorithm can detect patterns and make predictions accordingly.
- Evaluating the Model: This is the final step in this journey in which you would typically want to verify how your model is performing. To do this, you should test the performance of the model based on not the same data on which it has generated patterns, but on the previously unseen data. Remember, this unseen data is the testing set we created earlier.
Applications of Machine Learning
There are many different applications for Machine Learning. Some of the most popular applications include:
- Automating tasks and workflows
- Predicting consumer behavior
- Fraud detection
- Speech recognition
- Enhancing predictive analytics
- Predicting financial markets
- Recommender systems
Machine Learning vs Artificial Intelligence vs Deep Learning
Deep Learning, Machine Learning, and Artificial Intelligence are all terms often used interchangeably, but there are subtle differences between them.
Artificial intelligence is a broader concept that encompasses all types of computerized decision-making. It can be as simple as a rules-based system that makes decisions based on predetermined criteria.
Machine Learning is a branch of artificial intelligence that emphasizes the development of algorithms that can learn from data and improve its precision over time.
Deep learning is a subset of Machine Learning that takes advantage of artificial neural networks and learn from data much the same way human brain learns.
Final Thoughts on Machine Learning
Machine Learning is a powerful technology, and it’s becoming more important every day. As the amount of data continues to grow exponentially, the need for programs that are capable of analyzing that data is only going to increase.
With Machine Learning, you can create systems that make better decisions, analyze large amounts of data in real time, and adapt to changes in the environment without needing to be reprogrammed. But this technology isn’t a silver bullet; it still requires careful planning and preparation before you can use it effectively.
If you want to begin a career in the artificial intelligence and machine learning field, you can consider become a data analyst or data scientist. We have an article discussing the Top Courses for Data Analysis that can help you learn more about ML and AI.