7 Questions
Which of the following is NOT a common task associated with the preparation of a dataset for machine learning?
Generating synthetic data
Which of the following is the primary purpose of using linear regression?
To predict a continuous target variable
Which of the following is a key step in the process of improving a linear regression model?
Analyzing the residuals and identifying potential improvements
Which of the following is a common evaluation metric used to assess the performance of a logistic regression model for binary classification?
Confusion Matrix
Which of the following is a key difference between the Perceptron algorithm and logistic regression?
The Perceptron algorithm uses a linear activation function, while logistic regression uses a sigmoid activation function.
Which of the following is a common use case for automatic classification in machine learning?
Identifying the sentiment of customer reviews
Which of the following is a common challenge in the context of ethical issues related to AI?
All of the above
Study Notes
AI History and Applications
- Tracing the history of AI
- Identifying AI application areas
- Identifying AI players and proposed solutions
AI Ethical Issues
- Detecting ethical issues linked to AI in a given context
Data Preparation
- Manipulating a dataset in a development environment
- Preparing a dataset (detecting missing values, outliers)
- Producing an exploitable dataset for Machine Learning
Linear Regression
- Explaining how linear regression works
- Identifying a use case for linear regression in relation to a need
- Calculating linear regression on a dataset
- Analysing the results obtained by regression
- Improving the regression model according to the results obtained
- Measuring the results obtained (identifying the appropriate evaluation metric: RMSE, MSE, MAE, Risge, Lasso)
Perceptron and Classification
- Explaining how the Perceptron algorithm works
- Identifying a use case for automatic classification in relation to a need
- Calculating logistic regression on a dataset for binary classification
- Analysing the results obtained by the classification (interpreting a confusion matrix; AUC, ROC curve, performance metrics: TPR, TFR, F1-score, precision, recall...)
- Improving the classification model according to the results obtained
- Measuring the results obtained (identifying the appropriate evaluation metric: cross-entropy)
Test your knowledge on the history and applications of AI, identifying key players and ethical issues, as well as understanding linear regression concepts and application in real-life scenarios.
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