Fundamentals and Methods of Machine and Deep Learning - Pradeep Singh

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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.

       • 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].

      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.

      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)

      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

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