Machine Learning 101
Machine learning is a buzzword everyone sees floating around the news, but do you actually know what it is? It’s not something you should know but rather something worth knowing. Ever thought about the movie recommendations you get on Netflix or Amazon Prime? That’s machine learning right there. Machine learning is like having a super-smart assistant that learns from data to make predictions or decisions. It’s the technology behind personalized movie recommendations, voice assistants, and self-driving cars. Even if you aren’t a tech expert, knowing about machine learning could be interesting as it provides insights into the cutting-edge technologies shaping our world.
So, what is machine learning? Machines don’t learn the same way humans do. Machines learn through algorithms that analyze data, whereas humans learn through cognition, allowing us to generalize and apply knowledge to new situations. Machines, on the other hand, are sensitive to variations in inputs that differ from what they were trained on, which can lead to inaccurate outcomes since machines lack the broader contextual understanding humans possess.
Machine learning encompasses three types of learning: supervised, unsupervised, and reinforcement learning. For now, let’s dive into supervised and unsupervised learning. But in case you’re curious, reinforcement learning is like teaching a computer using a reward system; it learns by taking actions, receiving feedback, and adjusting its behavior to maximize rewards over time.
1. Supervised learning: Supervised learning algorithms learn from labeled examples. What do we mean by this? Suppose you have a collection of different fruits and want a computer to recognize each fruit. In that case, you would train the system with pictures of these fruits together with their corresponding labels, e.g., “apple”, “orange”, “kiwi”, etc. By repeatedly exposing the model to many labeled examples, it learns to identify patterns and relationships between pictures and their labels. Eventually, it can correctly classify new, unseen images of fruits on its own, even without explicitly being told of the labels. Supervised learning problems are of two types: regression and classification. Regression algorithms are used when the target (output) is a real value, such as housing prices in a pricing prediction problem. Classification algorithms are used when the target is a categorical value, such as “spam” or “not spam”, in email spam filtering.
2. Unsupervised learning: These learning algorithms are used to discover underlying patterns in the input data without being provided with explicit labels. Unsupervised learning algorithms have no prior knowledge about the expected output and try to identify patterns in the data independently; hence the name “unsupervised”. For example, in Google News, the algorithm automatically groups related news articles based on the topics they cover, without anyone telling it which words or issues to look for. Examples of unsupervised learning algorithms are clustering (groups similar data points together), anomaly detection (finding unusual data points, which is helpful in fraud detection), and dimensionality reduction (lets you take an extensive dataset and compress it to a much smaller dataset by reducing the number of features in the dataset while preserving the essential information)
If this sounds exciting to you, why not get in on the action yourself? Here are some helpful resources to get you started:
1. https://www.deeplearning.ai/courses/machine-learning-specialization/
2. https://codewithmosh.com/p/python-programming-course-beginners
