How does Machine Learning work? 3 Fields that cannot do without it.
Machine learning (ML) is a type of artificial intelligence (AI) that learns and improves on itself over time without explicit programming. And it is more similar to every job than any other technology. How does this work?
Think of it like this — you’re a newly hired waiter at the busy little restaurant around the corner. You know your job title, your role responsibilities, and purpose: get the orders, bring food to the tables, and pick up the dishes for the next guests. It’s enough to get the job done, but guaranteed, two months later, you’ll be working more quickly and efficiently than before. You’ll have learned the customer flow, peak hours, and top menu items— even without noticing yourself. Normally, we call this experience. In machine learning, it is more like “training”.
“Training” a Machine to Learn
In machine learning, “experience” gained is typically called “training” and such training comes with a supervisor, which is the collection of advanced algorithms. These algorithms use historical data to make predictions or make certain decisions without being explicitly programmed later on. In ML, we teach data machines to perform specific tasks and produce accurate results. Similar to real life work training, high-quality data is sent to machines and various algorithms are used to build machine learning models in order to train machines on this data.
Data matters.
Machine learning (ML) algorithms use sample data (also known as “training data”) to automatically create mathematical models that make decisions without special programming. The goal makes good and accurate decisions with little or no human intervention.
Where do we use machine learning?
1. Data Mining
Expert systems and data mining programs are the most common applications for improving algorithms through the use of machine learning. Machine learning and data mining often use the same methods and overlap significantly, but while machine learning focuses on predicting based on known properties derived from training data, data mining focuses on discovering (previously) unknown properties in data
2. Robotics
Machine learning is distinct from, but overlaps with, some aspects of robotics, where robots are the hardware that use machine learning to become fully autonomous and perform more precisely. An ML application to robotics, forms a certain type of artificial intelligence (AI), where robots would not only improve but also collect data from its environment.
3. Natural Language
Natural language processing is a field of machine learning in which machines understand natural language spoken and written by humans, rather than the data and numbers typically used in computer programming. The goal of this is to solve a variety of problems, including computer vision, speech recognition, machine translation, social media filtering, board and video games, and medical diagnosis. Using artificial neural network computing, the study of adaptive networks of nodes, which relate and learn to perform different tasks based on data and past experiences, often without the need for task-specific programming rules.
Of course, machine learning is not so much about automating decisions and predictions as it is about identifying patterns and relationships in data that humans might miss. This is the most important point of machine learning: that a machine’s computing power always outperforms that of the human mind — no matter how great our initial ideas are.
So, why don’t we let the machines do the grunt work?
