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You will learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. This course walks through the stages of a typical data science process for Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. Real life use case includes customer retention analysis to inform customer loyalty programs

Activities

This course includes presentations, group exercises, and hands-on labs.

Intended audience

This course is intended for:

Developers
Data Scientists

This course is intended for:

Developers
Data Scientists

In this course, you will:

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

Module 1: Introduction to machine learning

Types of ML
Job Roles in ML
Steps in the ML pipeline

Module 2: Introduction to data prep and SageMaker

Training and test dataset defined
Introduction to SageMaker
Demonstration: SageMaker console
Demonstration: Launching a Jupyter notebook

Module 3: Problem formulation and dataset preparation

Business challenge: Customer churn
Review customer churn dataset

Module 4: Data analysis and visualization

Demonstration: Loading and visualizing your dataset
Exercise 1: Relating features to target variables
Exercise 2: Relationships between attributes
Demonstration: Cleaning the data

Module 5: Training and evaluating a model

Types of algorithms
XGBoost and SageMaker
Demonstration: Training the data
Exercise 3: Finishing the estimator definition
Exercise 4: Setting hyper parameters
Exercise 5: Deploying the model
Demonstration: hyper parameter tuning with SageMaker
Demonstration: Evaluating model performance

Module 6: Automatically tune a model

Automatic hyper parameter tuning with SageMaker
Exercises 6-9: Tuning jobs

Module 7: Deployment / production readiness

Deploying a model to an endpoint
A/B deployment for testing
Auto Scaling
Demonstration: Configure and test auto scaling
Demonstration: Check hyper parameter tuning job
Demonstration: AWS Auto Scaling
Exercise 10-11: Set up AWS Auto Scaling

Module 8: Relative cost of errors

Cost of various error types
Demo: Binary classification cutoff

Module 9: Amazon SageMaker architecture and features

Accessing Amazon SageMaker notebooks in a VPC
Amazon SageMaker batch transforms
Amazon SageMaker Ground Truth
Amazon SageMaker Neo

Practical Data Science with Amazon SageMaker Training Course in Finland

Finland is a country located in northern Europe. Helsinki is the capital and largest city of the country. The majority of the people are Finns but there is also a small Lapp population in Lapland, where the country is famous for the Northern Lights. Finland's national languages are Finnish and Swedish.

Known for its vast forests, lakes, and natural beauty, Finland is one of the world's largest producers of forest products, such as paper, pulp, and lumber. One of the world's largest sea fortresses Suomenlinna, Rovaniemi with the "White Nights", dogsled safaris and of course the Northern Lights are what makes Finland so popular for tourists. Finland is one of the best places in the world to see the Northern Lights and attracts millions of tourists during its seasons.

Finland is home to a thriving technology industry and is widely recognized as one of the world's leading technology hubs. Companies such as Nokia and Rovio (creator of the popular game Angry Birds) are based in Finland. Some of the key factors that have contributed to Finland's success in technology include; strong investment in research and development, a highly educated workforce and fundings.

Finland has a strong educational system, and is widely regarded as one of the world's most literate countries. In fact, Finland's literacy rate is one of the highest in the world, and its students consistently perform well in international tests of math and reading ability.

Also, as a pioneer in environmental sustainability, Finland is known for its efforts to reduce its carbon footprint and promote clean energy. This Nordic country is also famous for its unique and distinctive cultural heritage, including its traditional folk music and its elaborate traditional costumes.

Helsinki, Finland's capital city, is the country's business center. Helsinki is Finland's largest city, and it is home to many of the country's major corporations and organizations, including many of the country's leading technology firms. The city is also a commercial, trade, and financial center, as well as one of the busiest ports in the Nordic region.

Take advantage of our diverse IT course offerings, spanning programming, software development, business skills, data science, cybersecurity, cloud computing and virtualization. Our knowledgeable instructors will provide you with practical training and industry insights, delivered directly to your chosen venue in Finland.

Practical Data Science with Amazon SageMaker Training in Finland

Activities

Intended audience

Upcoming Trainings

Practical Data Science with Amazon SageMaker Training in Finland

Activities

Intended audience

Prerequisites

What You Will Learn

Outline

HOW TO LEARN DATA SCIENCE IN 2024

Upcoming Trainings