ML & DL Algorithms Competency (Intermediate Level)

Topics covered

• compare prominent machine learning algorithms and select the appropriate algorithm for diversified problem spaces
• describe how regression works by finding the best fit straight line to model the relationships in your data
• distinguish between supervised learning techniques such as regression and classification, and unsupervised learning methods such as clustering
• escribe how clustering algorithms are able to find data points containing common attributes and thus create logical groupings of data
• escribe what Support Vector Machines are and how they are used to find a hyperplane to divide data points into categories
• list the characteristics of regression such as simplicity and versatility, which have led to the widespread adoption of this technique in a number of different fields
• recognize the application of a confusion matrix and how it can be used to measure the accuracy, precision, and recall of a classification model
• recognize the different kinds of machine learning algorithms such as regression, classification, and clustering, as well as their specific applications
• recognize the need to reduce large datasets with many features into a handful of principal components using the PCA technique
• recognize the problems associated with a model that is overfitted to training data and how to mitigate the issue