Dive into a comprehensive 4.5-hour course on tabular data analysis and machine learning. Begin with an introduction to machine learning concepts and model evaluation techniques. Explore exploratory data analysis and the K-Nearest Neighbors algorithm. Learn to use Jupyter Notebooks on SageMaker and delve into feature engineering. Master tree-based models and hyperparameter tuning. Gain hands-on experience with AWS SageMaker. Study optimization techniques, regression models, and regularization. Explore ensemble methods, focusing on boosting algorithms. Conclude with an introduction to neural networks and AutoML, equipping you with a robust skill set for tackling real-world tabular data challenges.
Overview
Syllabus
Accelerated Tabular Data 1.1 - Course Introduction.
Accelerated Tabular Data 1.2 - Introduction to Machine Learning.
Accelerated Tabular Data 1.3 - Model Evaluation.
Accelerated Tabular Data 1.4 - Exploratory Data Analysis.
Accelerated Tabular Data 1.5 - K Nearest Neighbors.
Accelerated Tabular Data 1.6 - Looking Ahead.
Using Jupyter Notebooks on Sagemaker.
Accelerated Tabular Data 2.1 - Introduction.
Accelerated Tabular Data 2.2 - Feature Engineering.
Accelerated Tabular Data 2.3 - Tree-based Models.
Accelerated Tabular Data 2.4 - Hyperparameter Tuning.
Accelerated Tabular Data 2.5 - AWS SageMaker.
Accelerated Tabular Data 3.1 - Introduction.
Accelerated Tabular Data 3.2 - Optimization, Regression Models and Regularization.
Accelerated Tabular Data 3.3 - Ensemble Methods: Boosting.
Accelerated Tabular Data 3.4 - Neural Networks and AutoML.
MLU Channel Introduction.
Taught by
Machine Learning University
