Machine Learning/Data Science Exam/Interview Preparation With Practice Tests, Examples, and Solution
This video is intended to prepare you for your next machine learning midterm or final exam by reviewing key topics (in a tutorial format) and providing several examples and their solutions. Four key components are explained:
1- The data that we can learn from
2- A model of how to transform the data
3- An objective function that quantifies how well the model is doing
4- An algorithm to adjust the model’s parameters
Covered background and machine learning methods/techniques include:
Linear regression
Polynomial regression
Regularization (Ridge)
Logistic regression
Underfitting and overfitting
Bias and variance
Decision boundary
Confusion matrix, accuracy, precision, recall, and f1 score
ROC curve (TPR vs. FPR)
Cross-entropy loss
Review of logarithm and exponential function
First derivative test and chain rule
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