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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

Description:

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.

Tabular Data

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#Computer Science #Machine Learning #Data Science #Data Analysis #Jupyter Notebooks #Exploratory Data Analysis #Model Evaluation #Feature Engineering #Hyperparameter Tuning #K-Nearest Neighbors