Machine learning is a science which deals with algorithms and machines which makes them learn based on their knowledge and data set to make decisions. It is an integral and important part of Artificial Intelligence. It is a two-process method. The first process is using algorithms to find meaning in random and unordered data. The second process is to use learning algorithms to find the relationship between that knowledge to improve the learning process.
Machine Learning requires much more data than a human does because ML optimizes over an artificial hypothetical space which we would consider ridiculous. We don’t need data because we have the idea of what things make sense and what do not, but the computer needs more and more data to implement ML on anything. For example, if we are shown a picture of the butterfly then we can recognize it easily because we have knowledge about it. A computer doesn’t have prior knowledge so it needs a few pictures to recognize.
Advantages of Machine learning
1) Machine learning courses can find the trends and patterns which humans might not find it easily after reviewing the data. It will review the data collected to understand the patterns and trends. For example, Amazon uses ML to understand the purchase and browsing activity so that it can recommend the products according to our interest.
2) The biggest achievement with the help of Machine learning is, it improves the accuracy and efficiency with an increase in data and algorithm’s ability to improve with time. For example weather prediction. The models made for weather prediction are the best example of this situation. The weather forecast is done by looking previous day’s weather pattern; use the data to predict the future scenario. The more data is collected; more is the accuracy of the forecast.
3) Human intervention is not required with Machine learning. It has the ability to learn on its own. Machines can make predictions, take decisions and even improve the algorithms if required. For example, antivirus can identify the viruses and new threats and take actions required.
Subcategories of ML
1) Supervised learning is using labelled data to train the model. The models or machines developed are trained and guided by the dataset. After the models are trained, they make predictions and decisions by themselves with the help of the data set. Supervised learning is further subdivided into\ Regression and classification techniques. Regression is to find the numerical values and classification techniques are used to find the categorical value (logistic regression, decision tree, K nearest neighbour)
2) Unsupervised learning- in this the models don’t require training. They learn through observation to give the desired output. They calculate the output by reviewing the data, performing repeated operations and arriving at the conclusions. It includes image recognition, language generation, etc.
3) Reinforcement learning- It came from supervised learning. It is reward- based learning. For example, we have an agent who acts as an environment. Based on its action either it will get a reward or penalty. Based on this it will make a policy and will perform actions accordingly.
This field has a large scope in the speech recognition machine and the most important robotics. If we have the right models and algorithm then the machine can change our lives. It is the best investment one can make in their professional lives. Students who have a keen interest in studying and knowing mathematical logics are recommended for this course.
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