Machine Learning Basics

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 1 of 4

Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.

Machine Learning: Features and Model Selection

Delves into the significance of features, model evolution, labeling challenges, and model selection in machine learning.

Applied Machine Learning: Features and Models

Explores data collection, feature selection, model building, and performance evaluation in machine learning, emphasizing feature engineering and model selection.

Model Selection: Evaluation and Generalization

Explores model selection, evaluation, and generalization in machine learning, emphasizing unbiased performance estimation and the risks of over-learning.

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Model Assessment and Hyperparameter Tuning

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

Model Complexity and Overfitting in Machine Learning

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Decision Trees: Classification

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

Model Evaluation

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Model Evaluation: K-Nearest Neighbor

Explores model evaluation with K-Nearest Neighbor, covering optimal k selection, similarity metrics, and performance metrics for classification models.