In this part with the help of many real world examples the explanation is given for Machine Learning.
With some real world examples explains how machine learning is helping us in our daily life and explains how we cannot ignore Machine Learning. Future of Machine Learning is explained and also explains that we should learn machine learning.
As per Arthur Samuel, Machine Learning is the field of study that gives computes that ability to lean without being explicitly programmed.
Main types of machine learning algorithms are supervised and unsupervised learning.
Supervised machine learning or more commonly, supervised learning, refers to algorithms that learn x to y or input to output mappings. The key characteristic of supervised learning is that you give your learning algorithm examples to learn from. That includes the right answers, whereby right answer, I mean, the correct label y for a given input x, and is by seeing correct pairs of input x and desired output label y that the learning algorithm eventually learns to take just the input alone without the output label and gives a reasonably accurate prediction or guess of the output.
Predicting the housing prices based on the size of the house is a an example of regression problem. By regression, we're trying to predict a number from infinitely many possible numbers such as the house prices in our example, which could be 150,000 or 70,000 or 183,000 or any other number in between.
Breast cancer detection as an example of a classification problem. We are figuring out if a tumor that is a lump is malignant meaning cancerous or dangerous. In this case two possible outputs 0 or 1, benign or malignant. What makes classification different from regression when you're interpreting the numbers is that classification predicts a small finite limited set of possible output categories such as 0, 1 and 2 but not all possible numbers in between like 0.5 or 1.7.
The two major types of supervised learning our regression and classification. In a regression application like predicting prices of houses, the learning algorithm has to predict numbers from infinitely many possible output numbers. Whereas in classification the learning algorithm has to make a prediction of a category, all of a small set of possible outputs. So you now know what is supervised learning, including both regression and classification.
We're not asked to diagnose whether the tumor is benign or malignant, because we're not given any labels. Why in the dataset, instead, our job is to find some structure or some pattern or just find something interesting in the data. This is unsupervised learning, we call it unsupervised because we're not trying to supervise the algorithm. To give some quote right answer for every input, instead, we asked the our room to figure out all by yourself what's interesting. Or what patterns or structures that might be in this data, with this particular data set. An unsupervised learning algorithm, might decide that the data can be assigned to two different groups or two different clusters. And so it might decide, that there's one cluster what group over here, and there's another cluster or group over here. This is a particular type of unsupervised learning, called a clustering algorithm.
In supervised learning, the data comes with both inputs x and input labels y, in unsupervised learning, the data comes only with inputs x but not output labels y, and the algorithm has to find some structure or some pattern or something interesting in the data.
Linear regression is one example of a regression model. The dataset that is used to train the model is called a training set. x is the input variable feature, y is output variable target variable and m is the number of training examples. In the housing price prediction examples x is the size of the house and y is the price of the house so it can be shown as (x, y) = (2104, 400) and x superscript in parenthesis, i, y superscript in parentheses i for the ith training set.
We'll write the function as lowercase f, where f stands for function. Historically, this function used to be called a hypothesis, but I'm just going to call it a function f in this class. The job with f is to take a new input x and output and estimate or a prediction, which I'm going to call y-hat, and it's written like the variable y with this little hat symbol on top. In machine learning, the convention is that y-hat is the estimate or the prediction for y. The function f is called the model. X is called the input or the input feature, and the output of the model is the prediction, y-hat. The model's prediction is the estimated value of y.
Function is denoted as below.
$$ f_{w,b}(x) = wx + b $$