Now let us discuss the classification of Machine Learning, Machine learning is broadly classified into three major tasks, which are supervised, unsupervised, and reinforcement learning. The simplest form of machine learning requires learning and it is the one where you have input variables like X and output variable Y. we use an algorithm to learn the mapping function from the input to the output. So in simple terms, it implies Y equals F effects. Now the goal is to approximate the mapping function so well that whenever you get some new input data X, the machine can easily break the output variables Y for the data. Now let me rephrase this in simple terms. In a supervised machine learning algorithm, every instance of the training data set consists of input attributes and expected outputs. The training data that can take any kind of data as input like values of data set roles, the pixel off an image, or even audio frequency histogram.
Let me tell you why this category of machine learning is supervised learning. Now, this is because the process of an algorithm learning from the training data set can be thought of as a teacher teaching his students. The algorithm continuously predicts the result on the basis of the training data and is continuously corrected by the teacher. The learning continues until the algorithm achieves an acceptable level of performance. Now in speech recognition or any speech automated system on your mobile phone to raise your voice and then starts working based on the training data. This is an application of supervised learning biometric attendance. You can train the machine with inputs of your biometric identity. It can be a thumb at the iris or your face. For that matter of fact, once the machine is trained, it can validate your future input and can easily identify you. Nowadays, this is being implemented in all the smartphones that we have, but sometimes the data is unstructured and unlabeled, so it becomes very difficult to classify that data into different categories. So unsupervised learning helps to solve this problem. Now, this learning is used to cluster the input data into classes on the basis of the statistical properties that the training data is a collection of information without any label here, the mathematically unsupervised learning is where you only have the input data, which is the X and no corresponding output variables.
Classification Of Machine Learning-Unsupervised Learning
Now the goal of unsupervised learning is to model the underlying structure of the distribution in the data in order to learn more about the data. So we came across an important point here, which is clustering. So what exactly is clustering? So clustering models focus on identifying groups of similar records and labeling the records according to the group to which they belong. This is done without the benefit of prior knowledge about the groups and their characteristics. In fact, we may not even know exactly. How many groups to look for that the models are often referred to as unsupervised learning models since there is no external standard by which to judge the modest classification performance. There are no right or wrong answers to these models. No market basket analysis is one of the key techniques used by large retailers to uncover an association between items and walks, all unsupervised learning. It walks by looking for combinations of items that occur together frequently in the transaction. Now, to put it another way, it allows retailers to identify the relationships between the items that people buy. For example, people who buy bread also tend to buy butter. Now the marketing teams at the retail stores should target customers who buy bread and butter and provide an offer to them so that they buy the third item like an egg. So if a customer buys bread and butter and sees a discount on or an offer on an egg, he will be encouraged to spend more money and buy the eggs.
Classification Of Machine Learning-Reinforcement Learning
And this is what market basket analysis is all about. Reinforcement learning is a part of machine learning, where an agent is put in an environment and he learns to behave in this environment by performing certain actions and observing the rewards which it gets from those actions. This reinforcement learning is all about taking appropriate action in order to maximize the reward in a particular situation in supervised learning, the training data comprises of the input, and so the model is trained with the expected output itself. But when it comes to reinforcement learning, there is no expected output. Their enforcement agent decides what action to take in order to perform a given task. In the absence of a training data set, it is bound to learn from its own experience. Now let’s understand this reinforcement learning with an analogy to consider a scenario where a baby is learning how to walk. Now, this scenario can go in two ways. The first is that the baby starts walking and makes it to the candy. Since the candy is the end quote, the baby is happy. Its positive reward now coming to the second scenario. The baby starts walking but feels due to some hurdles in between. The baby gets hurt and does not get to the candy. It’s negative.
The baby is sad. That implies a negative reward that just like how we humans learn from our mistakes by trial and error, reinforcement learning is also similar. We have an agent which is here, the baby, and we have a reward, which is the candy. With many hurdles in between, the agent is supposed to find the best possible path to reach the reward. Now, another application of reinforcement learning is also the games. It is used to solve different games and sometimes achieve superhuman performance. The most famous one must be the alpha goal and the Alpha will zero. It trained from the scratch and a researcher let the new agent, Alpha will zero play with itself and finally beat the Alpha go one hundred to zero. Now, this was a major breakthrough in the reinforcement learning process and also help a lot of people in the deep learning process as well, and also the data scientists to make new robots and create the artificial bots which are there in the games. So, guys, this will come to an end of the article. I hope you understood the basics of machine learning, what it is, what are the basic types of machine learning, how it is difficult for us to perform all of these scenarios by hand, and write an algorithm by ourselves.