Practical Data Science with Amazon SageMaker Certification Training Course
Practical Data Science with Amazon SageMaker Certification Training Course
The Practical Data Science with Amazon SageMaker is an instructor-led, hands-on certification training course. This course introduces the steps of a typical data science process in machine learning, from dataset analysis and visualisation to data preparation and feature engineering. People will also learn practical aspects of building, training, tuning, and deploying models using Amazon SageMaker. A real use case includes customer retention analysis to inform customer loyalty programs.
- Knowledge of the Python programming language
- Basic understanding of machine learning
Candidates can achieve this certification by passing the following exam(s).
Exam Number: Practical Data Science with Amazon SageMaker
The certification exam can be registered and attempted within 3 months of course/module completion at Logitrain training centre on weekdays during normal business hours (excludes public holidays)
Logitrain course material for Practical Data Science with Amazon SageMaker provided
- Prepare a dataset for training
- Train and evaluate a Machine Learning model
- Automatically tune a Machine Learning model
- Prepare a Machine Learning model for production
- Think critically about Machine Learning model results
- Developers
- Data Scientists
- Students
- IT professionals
- People interested in Practical Data Science with Amazon SageMaker
- Types of ML
- Job Roles in ML
- Steps in the ML pipeline
- Training and test dataset defined
- Introduction to SageMaker
- Demonstration: SageMaker console
- Demonstration: Launching a Jupyter notebook
- Business challenge: Customer churn
- Review customer churn dataset
- Demonstration: Loading and visualizing your dataset
- Relating features to target variables
- Relationships between attributes
- Demonstration: Cleaning the data
- Types of algorithms
- XGBoost and SageMaker
- Demonstration: Training the data
- Finishing the estimator definition
- Setting hyper parameters
- Deploying the model
- Demonstration: hyper parameter tuning with SageMaker
- Demonstration: Evaluating model performance
- Automatically tune a model
- Automatic hyper parameter tuning with SageMaker
- Tuning jobs
- Deploying a model to an endpoint
- A/B deployment for testing
- Auto Scaling
- Configure and test auto scaling
- Check hyper parameter tuning job
- AWS Auto Scaling
- Set up AWS Auto Scaling
- Cost of various error types
- Binary classification cutoff
- Accessing Amazon SageMaker notebooks in a VPC
- Amazon SageMaker batch transforms
- Amazon SageMaker Ground Truth
- Amazon SageMaker Neo
Get a Certificate of Attendance to prove your commitment to learning
Take the official certification exam at Logitrain, a local VUE test centre or online
Course material in digital format is included for flexibility and ease of use
Practise questions are provided for better understanding of the key concepts
Attend the course with an instructor at our training centre or from anywhere
Relax, we will beat competitor’s advertised price. Our course has no extra costs
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The supply of this course/package/program is governed by our terms and conditions. Please read them carefully before enrolling, as enrolment is conditional on acceptance of these terms and conditions. Proposed course dates are given, course runs subject to availability and minimum registrations.
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