When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Introduction to Data Science and Machine Learning with Amazon SageMaker
Skills Gained Understand the basics of data science, ML, and AI Apply data science techniques to solve real-world problems Develop machine learning algorithms Build and deploy AI applications Use...
Read MoreSkills Gained
- Understand the basics of data science, ML, and AI
- Apply data science techniques to solve real-world problems
- Develop machine learning algorithms
- Build and deploy AI applications
- Use Amazon SageMaker to build, train, and deploy machine learning models at scale
Prerequisites
- Familiarity with the Python programming language
- Basic understanding of machine learning
Course Details
Training Materials
All Amazon SageMaker training students receive comprehensive courseware.
Software Requirements
A modern web browser and an Internet connection free of restrictive firewalls, so that the student can connect by SSH or Remote Desktop (RDP) into AWS virtual machines.
Outline
- Introduction to Data Science, Machine Learning, and AI
- What is Data Science?
- Data Science Ecosystem
- What is Machine Learning?
- What is AI?
- Features and Observations
- Representing Observations
- Labels
- Continuous and Categorical Features
- Continuous Features
- Categorical Features
- Common Distance Metrics
- The Euclidean Distance
- What is a Model?
- Model Evaluation
- Bias-Variance (Underfitting vs Overfitting) Trade-off
- The Modeling Error Factors
- One Way to Visualize Bias and Variance
- Underfitting vs. Overfitting Visualization
- Balancing Off the Bias-Variance Ratio
- Types of Machine Learning
- Supervised vs. Unsupervised Machine Learning
- Unsupervised Learning (UL) Type: Clustering
- Clustering Examples
- k-Means Clustering (UL)
- k-Means Clustering in a Nutshell
- XGBoost (Supervised Learning)
- Gradient Boosting
- Which Algorithm to Choose?
- The Typical ML Workflow
- A Better Algorithm or More Data?
- Artificial Neural Networks
- The Basic 3-Layer Neural Network
- Neural Network Terminology
- Model Learning Process in Neural Networks
- The Forward Pass
- The Backpropagation Pass
- When the Learning Process Stops
- Deep Learning vs. Traditional ML
- Amazon SageMaker
- What is SageMaker
- ML with SageMaker
- The ML Phases Diagram
- Supported Systems and Frameworks
- ML Algorithms Supported by SageMaker
- SageMaker in the AWS Management Console
- Ground Truth
- Notebooks
- Training
- Training Options
- The Model Training Flow Diagram
- Inference
- Deployment of Models to the SageMaker Hosting Service
- The SagaMaker Hosting Service Architecture
- Improving Your ML Models
- The AWS Marketplace of ML Algorithms
- EC2 P3 Instances
- SageMaker Pricing
- Conclusion