1. AWS
1.1. Resources
1.1.1. Intro
1.1.1.1. ML General
1.1.1.1.1. MLU-Explain (website)
1.1.1.1.2. Data Science - An Introduction (website)
1.1.1.2. Deep Learning
1.1.1.2.1. Artificial Intelligence (AI) vs. Machine Learning vs. Deep Learning (article)
1.1.1.2.2. Dive into Deep Learning (e-book)
1.1.1.3. Problem Domains - Tabular
1.1.1.3.1. Classification vs Regression (article)
1.1.1.4. Algorithm selection
1.1.1.4.1. Use Amazon SageMaker Built-in Algorithms or Pre-trained Models (Developer Guide)
1.1.1.5. Practical Data Science with Amazon SageMaker Introduction
1.1.1.5.1. Machine Learning & Artificial Intelligence (website)
1.1.1.6. Benefits of machine learning (ML)
1.1.1.6.1. What is Machine Learning? (website)
1.1.1.7. Framing the business problem
1.1.1.7.1. Adult (data set)
1.1.2. Preparing a Dataset
1.1.2.1. Data analysis and preparation
1.1.2.1.1. Data Science Needs Drudges (article)
1.1.2.2. Data Visualization
1.1.2.2.1. 17 Data Visualization Techniques All Professionals Should Know (website)
1.1.2.3. Splitting dataset
1.1.2.3.1. The Importance of Data Splitting (article)
1.1.2.4. Python Libraries
1.1.2.4.1. 9 Best Python Libraries for Machine Learning (website)
1.1.2.5. Data Wrangler
1.1.2.5.1. Welcome to SageMaker Data Wrangler Deep Dive Workshop (workshop and tutorial)
1.1.3. Training a Model
1.1.3.1. Choose an algorithm
1.1.3.1.1. Use Amazon SageMaker Built-In Algorithms or Pre-Trained Models (developer guide)
1.1.3.1.2. How XGBoost Works (developer guide)
1.1.3.1.3. Decision Trees (website)
1.1.3.2. Amazon CodeWhisperer
1.1.3.2.1. Amazon CodeWhisperer (workshop)
1.1.4. Evaluating and Tuning a Model
1.1.4.1. Model evaluation
1.1.4.1.1. Multiclass Model Insights (developer guide)
1.1.4.1.2. ROC & AUC (website)
1.1.5. Deploying a Model
1.1.5.1. Model evaluation
1.1.5.1.1. Deploy models for inference (developer guide)
1.1.5.1.2. Choosing the Right Model Threshold (website)
1.1.6. Operational Challenges
1.1.6.1. Model evaluation
1.1.6.1.1. What Is Fairness and Model Explainability for Machine Learning Predictions? (developer guide)
1.1.6.1.2. Amazon SageMaker Model Deployment (website)
1.1.6.1.3. Blue/Green Deployments (developer guide)
1.1.7. Other Model-Building Tools
1.1.7.1. No-code ML with SageMaker Canvas
1.1.7.1.1. Amazon SageMaker Canvas Announces Quick Build Support for Time-Series Forecast Models (article)
1.1.7.1.2. Amazon SageMaker Canvas Use Cases (website)
1.1.7.1.3. Onboard to Amazon SageMaker Domain (developer guide)
1.1.7.1.4. Export an Amazon SageMaker Studio Lab Environment to Amazon SageMaker Studio (developer guide)