Welcome to the exciting world of machine learning (ML)! If you are curious about how machines can learn from data, this post is for you. This post will introduce you to the basics of machine learning and its key concepts.
What is Machine Learning?
Machine learning is a branch of artificial intelligence (AI) that focuses on building systems capable of learning from data. Machine learning differs from traditional artificial intelligence because it actually evolves by learning from data.
According to an article by IBM, Machine learning (ML) is a subset of AI and computer science that emphasizes the use of data and algorithms to mimic the way that humans learn, thereby gradually improving its accuracy. Click here to read the entire article.
According to an MIT article, machine learning powers chatbots, predictive text, language translation apps, the shows Netflix suggests to you, and how your social media feeds are organized. It also enables autonomous vehicles and machines that can diagnose medical conditions based on images. Read here for more information.
Types of Machine Learning:
There are 3 different types of machine learning-
- Supervised Learning: The model learns from labeled training data, attempting to predict the output for new data based on the provided input and output data. Supervised learning employs two types of techniques: regression and classification. Regression algorithms work with continuous categorical data, which has unlimited values. On the other hand, classification uses discrete categorical data, which has a limited number of values.
- Unsupervised Learning: The model identifies patterns in unlabeled or uncategorized data, meaning the data are not defined by cause/effect or input/output labels. The two techniques used in this kind of learning are clustering and dimensionality reduction. Clustering groups similar data points together, while dimensionality reduction decreases the number of variables studied to simplify the data analysis.
- Reinforcement Learning: The model learns to make decisions through a process of rewarding desired behaviors and punishing undesired ones. This process is similar to the process of training dogs. When teaching dogs a trick, you give them a treat for correctly doing the trick (reward) but don’t give them a treat when they fail to execute the trick (punishment). As the dog realizes it receives a treat for performing the trick, it continues to do so. Similarly, machines try to maximize potential rewards by pursuing the most optimal solutions.
Why is Machine Learning Important?
Machine learning is important as machines can do what humans used to do in this technological world. It is reshaping how we live and work, powering everything from search engines and fraud detection systems to self-driving cars and personalized recommendations on streaming platforms.
Conclusion:
Machine learning is a vast and fascinating field. It is a branch of artificial intelligence in which the machine learns from the data and gradually improves its accuracy in achieving optimal solutions. I hope you found this article useful and learned a thing or two about what exactly machine learning is!