Decision Trees: Induction & Attributes

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Machine Learning Basics: Supervised and Unsupervised Learning

Covers the basics of machine learning, supervised and unsupervised learning, various techniques like k-nearest neighbors and decision trees, and the challenges of overfitting.

Supervised Learning: k-NN and Decision Trees

Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.

Machine Learning Basics: Supervised Learning

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Decision Trees: Classification

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Supervised Learning: Classification Algorithms

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Image Classification: Decision Trees & Random Forests

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Decision Trees and Random Forests: Concepts and Applications

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.