Questions:
(1) Machine learning algorithms and AI are being quickly adopted in diverse fields and there is also a growing concern among people. Discuss at least FIVE (5) ethical concerns of AI.
(2) Find out about edge computing and explain this term in your own words. How do Edge ML works?
(3) Take any picture using your phone and upload it to Google Vision API
(https://cloud.google.com/vision). Show the snapshot of your upload and the results. Guess the type of algorithm being used from the results provided by the Google Vision API.
(4) Load the breast cancer dataset from sklearn package. Perform exploratory data analysis and set up a KNN classifier. Propose an appropriate value for K. Show the relevant performance metrics. Assess whether scaling the data improves the performance.
(5) Use the iris dataset available in sklearn package. Drop the target variable and apply the Kmeans clustering algorithm. Select the appropriate value for K and provide relevant performance metrics.
(6) Use the diabetes dataset from Kaggle
(https://www.kaggle.com/c/diabetesclassification/data). You should use the train.csv dataset for your training and testing. Explain the attributes and the target. Perform exploratory data analysis and set up a decision tree.
Set up an SVM classifier and compare the performance of the decision tree with the SVM classifier. You are required to perform optimization wherever possible.
Deploy your trained and tested algorithms on the dataset in test.csv. Compare if the decision tree and SVM classifiers provide the same results for the dataset in test.csv
