Podcast
Questions and Answers
Which of the following is NOT a common task associated with the preparation of a dataset for machine learning?
Which of the following is NOT a common task associated with the preparation of a dataset for machine learning?
Which of the following is the primary purpose of using linear regression?
Which of the following is the primary purpose of using linear regression?
Which of the following is a key step in the process of improving a linear regression model?
Which of the following is a key step in the process of improving a linear regression model?
Which of the following is a common evaluation metric used to assess the performance of a logistic regression model for binary classification?
Which of the following is a common evaluation metric used to assess the performance of a logistic regression model for binary classification?
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Which of the following is a key difference between the Perceptron algorithm and logistic regression?
Which of the following is a key difference between the Perceptron algorithm and logistic regression?
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Which of the following is a common use case for automatic classification in machine learning?
Which of the following is a common use case for automatic classification in machine learning?
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Which of the following is a common challenge in the context of ethical issues related to AI?
Which of the following is a common challenge in the context of ethical issues related to AI?
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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)
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Description
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.
