Machine Learning/Data Science Exam/Interview Preparation With Practice Tests, Examples, and Solution скачать в хорошем качестве

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 #MachineLearningExam #DataScience #ProblemSolution