December 26th, 2022
Machine Learning is a subfield of Artificial Intelligence. It aims to create systems that can learn and improve from previous experiences and perform tasks without being explicitly programmed to do them.
How does it work?
The first step would be to collect training data. You provide the model with data - images, text, or numbers, like sales reports, time series data, or pictures of smiling children. We call these data training data. They are used to train the computer. The more data, the better the results.
The programmer then selects a machine learning model, provides the computer with the training data, and lets it train itself to analyze the data to find patterns or make decisions.
Machine Learning Types
There are three categories of ML.
Supervised Machine Learning.
Today, the most common type is supervised machine learning. Supervised machine learning models are trained using labeled data sets, allowing the models to learn and improve over time. For example, an algorithm could be trained with images of happy people and other objects that have all been labeled by humans. The machine could then learn how to identify images of happy-looking people on its own.
Unsupervised Machine Learning.
Unsupervised machine learning can detect patterns or trends that we aren't looking for. In unsupervised machine learning, the program searches for patterns in unlabeled data - data that isn't labeled by humans.
Reinforcement Machine Learning.
By setting up a reward system and going through an error and trial process, reinforcement machine learning teaches computers to choose the best action. By letting the machine know when it made the appropriate choices, reinforcement learning can train models to play games or train autonomous vehicles to drive. Over time, the machine will understand what actions to take.
Machine Learning Examples
Machine Learning allows us to solve the same problem with changing variables each time, whether it's driving safely with smart cars or suggesting the best song to match your mood.
For instance, a smart car can observe, identify, and then recognize an object using machine learning. Since there are so many different objects in the world, it would be quite difficult to explicitly code into the car's architecture what each object is or maybe. But if you use machine learning to teach the automobile to recognize objects, it can decide for itself.
Machines can look for patterns in a person's spending or shopping habits to spot possibly fraudulent credit card purchases, login attempts, or spam emails. This helps keep your Amazon purchase info or Gmail account safe.
Machine learning As a Career
If you want to pursue a career in machine learning, Python is argued to be your best choice.
Python has many machine-learning libraries. It is claimed to be the best for data science, sentiment analysis, natural language processing, and data science prototyping, all of which are essential in machine learning.