Section 2: ML/AI Techniques

Course Description

Section 2: ML/AI Techniques

In this section, you will gain hands-on experience with coding in Python to create k-means algorithms and apply functions. You will also learn how to predict outcomes using multiple linear regression models, create visual decision trees, and interpret various kinds of ML/AI decision models.

Module 17: Practical Application IIIuch as learner engagement notifications, assignments, essay questions (and more) are not featured in this course.

Module 6: Clustering and Principal Component Analysis

Module 7: Linear and Multiple Regressions

Module 8: Feature Engineering and Overfitting

Module 9: Model Selection and Regularization

Module 10: Time Series Analysis and Forecasting

Module 11: Practical Application II

Module 12: Classification and k-Nearest Neighbors

Module 13: Logistic Regression

Module 14: Decision Trees

Module 15: Gradient Descent and Optimization

Module 16: Classifying Nonlinear Features

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