In this course, you will learn about Azure machine learning and, a great platform MS Azure through which you can create, maintain and distribute real world ML solutions.
In this part of the video, I will introduce Machine learning concept and introduction, how it is different from traditional programming and ML types or methods.
Let’s look at Picture 1… This is a bird, right? How? We have seen many birds which have the same properties, such as feathers, legs, eye, can fly, beak, etc. In actuality, I have never seen this exact bird, however, from my previous experiences, I am sure that this is a bird. Let’s see Picture 2. Is it a bird? Maybe, because most of the properties are matching with a bird. Then again, the beak is a bit weird, or the mouth doesn’t look like a bird. Despite the fact that we are not100% sure about the beak or mouth, we can say that this looks like a bird. Now, let’s observe Picture 3, what is your prediction? Is it a man? Maybe not… Then, is it a bird? Again no. This image is weird and we can’t identify it. Why, because, we have not seen this kind of creature before. We don’t have data on this. (* I took all these images randomly from google search) This is exactly, AI or Machine learning. Machine Learning also does the same thing, predicting the output based on previous data.
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Historical or back data has been primarily used for the following two purposes until a few decades ago:
- As a Record to know what happened.
- To identify the root cause of why it happened.
Despite the fact that the reasons mentioned are valid, we have added a dimension in the last decade where data is being utilized for predicting what could potentially happen in the future. Then comes Machine Learning, which plays a significant role in doing so. Machine learning is a subset/subfield of Artificial Intelligence. Generally, the main aim of Machine learning is to understand the structure of data and apply the best possible models that can be utilized or identify a hidden pattern. Developing a machine learning model is one of the key factors in predicting a future problem which again requires machine learning algorithms. There are numerous machine learning algorithms that have been developed and mature enough to solve various real-world business problems. Although machine learning is a field within computer science, it differs from traditional computational approaches. In traditional computing, algorithms are sets of explicitly programmed instructions used by computers to calculate or problem solve. Machine learning algorithms instead allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range. Because of this, machine learning facilitates computers in building models from sample data in order to automate decision-making processes based on data inputs. Using Machine learning, information is being turned into knowledge. In the last 5-6 decades, enormous data has been recorded or collected which will be of no use if we don’t utilize or analyze to find hidden patterns. In order to find useful and significant patterns with complex data, we have several Machine Learning techniques available to ease our struggle for discovery. Subsequently, those identified hidden patterns and knowledge of the problem can be helpful to perform complex decision making and predict future occurrence.