Machine Learning | 3 rd | week 2 |.
Machine Learning, a subset of AI, uses computer algorithms to analyze data and make intelligent decisions based on what it has learned. Instead of following rules-based algorithms, machine learning builds models to classify and make predictions from data. Let's understand this by exploring a problem we may be able to tackle with Machine Learning. What if we want to determine whether a heart can fail, is this something we can solve with Machine Learning? The answer is, Yes. Let's say we are given data such as beats per minute, body mass index, age, sex, and the result whether the heart has failed or not. With Machine Learning given this dataset, we are able to learn and create a model that given inputs, will predict results. So what is the difference between this and using statistical analysis to create an algorithm? An algorithm is a mathematical technique. With traditional programming, we take data and rules, and use these to develop an algorithm that will give us an answer. In the previous example, if we were using a traditional algorithm, we would take the data such as beats per minute and BMI, and use this data to create an algorithm that will determine whether the heart will fail or not. Essentially, it would be an if-then-else statement. When we submit inputs, we get answers based on what the algorithm we determined is, and this algorithm will not change. Machine Learning, on the other hand, takes data and answers and creates the algorithm. Instead of getting answers in the end, we already have the answers. What we get is a set of rules that determine what the machine learning model will be.