Fundamentals Of Supervised Machine Learning Training Ppt

Slide 1

This slide gives an overview of supervised learning which is one of the most basic types of Machine Learning. In this case, the Machine Learning algorithm is trained on labeled data. Even though the data must be appropriately marked for this method to work, supervised learning is powerful when utilized in the right situations.

Slide 2

This slide demonstrates that regression analysis is the fundamental approach used in Machine Learning to address regression issues, using data modeling. It entails establishing the best fit line, which goes through all data points while minimizing the distance between the bar and each data point. The regression approach is mainly used to identify predictor strength, forecast trend, time series, and in the event of a cause and effect relationship.

Slide 3

This slide describes that linear and logistic regression are two regression analysis approaches used to address problems using Machine Learning; these are the most popular regression approaches. However, there are many types of regression analysis approaches in Machine Learning, and their use varies depending on the nature of the data.

Slide 4

This slide states that regression analysis have many types, and the application of each approach is dependent on the number of components. These variables include the kind of target variable, the form of the regression line, and the number of independent variables.

Slide 5

This slide describes that linear regression is one of the most fundamental forms of regression in Machine Learning. The linear regression model links a predictor variable and a dependent variable linearly.

Slide 6

This slide states that Polynomial Regression is a type of regression analysis approach in Machine Learning. It is similar to Multiple Linear Regression but with a few differences. As an estimator, a linear model is used. The Least Mean Squared Method is also used in Polynomial Regression.

Slide 7

This slide states that this sort of regression in Machine Learning is employed when the independent variables have a strong correlation. This is because, in multicollinear data, the least square estimates produce unbiased results. However, if the collinearity is too high, there may be some bias value.

Slide 8

This slide lists that lasso regression is a sort of regression in Machine Learning that includes regularisation and feature selection. It forbids the regression coefficients’ absolute size; and as a result, the coefficient value approaches 0, which is not the case with Ridge Regression.

Slide 9

This slide showcases that Bayesian Regression is a type of Machine Learning regression that uses the Bayes Theorem to determine the value of regression coefficients. Instead of calculating the least-squares, this regression approach determines the posterior distribution of the features.

Slide 10

This slide states that decision trees are a handy tool and has many applications. Decision trees can be used to solve classification and regression issues. The name indicates that it displays the predictions coming from a series of feature-based splits using a flowchart-like tree structure. It all starts with a root node and ends with a leaf choice.

Slide 11

This slide gives an overview of random forest algorithm. A Random Forest is a cluster of decision trees. Each tree is classed, and the tree "votes" for that class to classify a new item based on its properties. The forest chooses the categorization with the highest number of votes (over all the trees in the forest).

Slide 12

This slide gives an overview of logistic regression which is a sort of regression analysis approach employed when the dependent variable is discontinuous: For example, 0 or 1, true or false, and so on. The Logit function is used in Logistic Regression to assess the connection between the target variable and the independent variables.

Slide 13

This slide demonstrates that KNN is a simple algorithm that keeps all existing instances, and classifies new cases based on a majority vote of its k neighbors.

Instructor’s Notes:

KNN may be understood with an analogy from real life. For example, if you want to learn more about someone, chat with their friends and coworkers.

Consider the following before settling on the K Nearest Neighbors Algorithm:

• KNN is costly to compute & arrive at
• Variables should be normalized, or greater range variables will cause the algorithm to be biased
• Data must still be pre-processed

Slide 14

This slide states that Naive Bayes is a probabilistic Machine Learning technique based on the Bayes Theorem and is used for a wide range of classification problems. A Naive Bayesian model is straightforward to build and works well with massive datasets. It is simple to use and outperforms even the most sophisticated classification algorithms.

Slide 15

This slide showcases that the SVM algorithm is a classification process in which raw data is shown as points in an n-dimensional space (n being the number of features you have). The value of each characteristic is then assigned to a specific location, making it simple to categorize the data. Classifier lines can divide data and plot it on a graph.