Fundamentals and Methods of Machine and Deep Learning. Pradeep Singh
iteratively that separates the classes in a most ideal manner.
• Then, it picks the hyperplane that splits the classes accurately.
For example, let us consider two tags that are blue and black with data features p and q. The classifier is specified with a pair of coordinates (p, q) which outputs either blue or black. SVM considers the data points which yield the hyperplane that separates the labels. This line is termed as a decision boundary. Whatever tumbles aside of the line, will arrange as blue, and anything that tumbles to the next as black.
The major terms in SVM are as follows:
• Support Vectors: Datapoints that are nearby to the hyperplane are called support vectors. With the help of the data points, the separating line can be defined.
• Hyperplane: Concerning Figure 1.4, it is a decision plane that is parted among a set of entities having several classes.
• Margin: It might be categorized as the gap between two lines on data points of various classes. The distance between the line and support vector, the margin can be calculated as the perpendicular distance.
There are two types of SVMs:
• Simple SVM: Normally used in linear regression and classification issues.
• Kernel SVM: Has more elasticity for non-linear data as more features can be added to fit a hyperplane as an alternative to a 2D space.
SVMs are utilized in ML since they can discover complex connections between the information without the need to do a lot of changes. It is an incredible choice when you are working with more modest datasets that have tens to a huge number of highlights. They normally discover more precise outcomes when contrasted with different calculations in light of their capacity to deal with little, complex datasets.
Figure 1.4 shows the hyper-plane that categorizes two classes.
Figure 1.4 SVM [11].
1.7 Decision Tree
Decision tree groups are dependent on the element values. They utilize the strategy for Information Gain and discover which element in the dataset, give the best of data, making it a root node, etc., till they can arrange each case of the dataset. Each branch in the decision tree speaks to an element of the dataset [4, 5]. They are one of the most generally utilized calculations for classification. An analysis of the decision tree, the decision tree is utilized to visually and signify the decision and the process of decision making. As the term suggests it utilizes a tree-like representation of choices. Tree models are the objective variable that can take a discrete arrangement of values termed as classification trees; in this tree model, leaves signify the class labels, and combinations of features of class labels are signified by the branches.
Consider an example of listing the students eligible for the placement drive. Now, the scenario is whether the student can attend the drive or not? There are “n” different deciding factors, which has to be investigated for appropriate decision. The decision factors are whether the student has qualified the grade, what is the cut-off, whether the candidate has cleared the test, and so on. Thus, the decision tree model has the following constituents. Figure 1.5 depicts the decision tree model [2]:
Figure 1.5 Decision tree.
• Root Node: The root node in this example is the “grade”.
• Internal Node: The intermediate nodes with an incoming edge and more than 2 outgoing edge.
• Leaf Node: The node without an out-going edge; also known as a terminal node.
For the currently developed decision tree in this example, initially, the test condition from the root hub is tested and consigns the control to one of the active edges; thus, the condition is again tried and a hub is allocated. The tree is supposed to be ended when all the test conditions lead to a leaf hub. The leaf hub consists of class-labels, which vote against or in favor of the choice.
1.8 Machine Learning Applications in Daily Life
Some of the main areas where we use ML algorithms are in traffic alert systems in Google maps, social media sites like Facebook, in transportation and commuting services like Uber, Product recommendation systems, virtual personal assistant systems, self-driving cars, Google translators, online video streaming services, fraud detection, etc [13].
1.8.1 Traffic Alerts (Maps)
Nowadays, when we decide to go out and in need of assistance for directions and traffic situations on the road we have decided to travel, we usually take the help of Google maps. If in case you decided to travel to a city and decide to take the highway, and the Google traffic alert system suggested that “Even though there is heavy traffic, you are on the fastest route to your destination”, how does the system know all these things? In short, it is a combined data of people actively using the service, the previous data of the route collected over the years, and also involves some own tricks which are acquired by the company to efficiently calculate the traffic. Most of the people who are currently using the Google maps service is indirectly providing their location, speed, and the routes they are going to take in which they are traveling, which helps Google collect data about the traffic, which will help the Google map algorithm predict the traffic and recommend the best routes for future users.
1.8.2 Social Media (Facebook)
Social media applications like Facebook use ML to detect and recognize faces that are used for automatic friend tagging suggestions. The algorithm compares the detected faces with the database of pictures it already has and gives users suggestions. Facebook’s DeepFace algorithm which uses deep learning runs behind the Facebook application to recognize faces and identify the person in the picture. It also provides alternative tags to images already uploaded on Facebook.
1.8.3 Transportation and Commuting (Uber)
Transportation and commuting apps like Uber use ML to provide good services to their clients. It provides a personalized application that is unique to you, for example, it automatically detects your location and gives options either to go home or office or any other frequent places which will be purely based on your search history and patterns. The application uses a ML algorithm on top of historic data on trips to make accurate ETA predictions. There was an increase of 26% in the accuracy of delivery and pickup after implementing ML on their application.
1.8.4 Products Recommendations
This tells you how powerful is the ML recommendation systems are these days. Take for example, you liked an item on Amazon, but add it to your wish list because you cannot afford the item at the current price. Surprisingly, the day after, when you are watching videos on YouTube or some other application you encounter an ad for the item which you have wish-listed before. Even when you switch to another app, say, Facebook, you will still see the same ad on that website. This happens because Google tracks your search history and recommend ads depending on the activities you do. About 35% of Amazon’s wealth is generated by using product recommendation systems like these [18].
1.8.5 Virtual Personal Assistants
Here, virtual assistant finds some useful information when the user asks some questions via text or voice. There are many applications of ML which are being in these kinds of applications. Applications involve speech verifi-cation