Practical Data Science with Amazon SageMaker Course Overview

Certainly! To ensure that students are adequately prepared for the Practical Data Science with Amazon SageMaker course and can get the most out of the training, the following minimum prerequisites are suggested:

• Basic understanding of machine learning concepts, including familiarity with the types of machine learning (supervised, unsupervised, and reinforcement learning).
• Fundamental knowledge of Python programming, as Python is commonly used for scripting in data science tasks and exercises within the SageMaker environment.
• Experience with data handling and manipulation using Python libraries such as pandas and NumPy.
• Some familiarity with basic data visualization techniques and tools, which could include libraries such as matplotlib or seaborn in Python.
• An understanding of the general data science workflow, from data preparation and analysis to model training and evaluation.
• Awareness of AWS cloud services is beneficial, although not strictly necessary, as the course will provide an introduction to Amazon SageMaker.
• No prior experience with Amazon SageMaker is required as the course will cover this from an introductory level.

Students who meet these prerequisites are more likely to successfully grasp the course content and apply the skills learned in the Practical Data Science with Amazon SageMaker training.

Target Audience for Practical Data Science with Amazon SageMaker

Practical Data Science with Amazon SageMaker is a comprehensive course designed for professionals seeking to leverage ML in cloud environments.

• Data Scientists and Analysts
• Machine Learning Engineers
• Software Developers interested in ML deployment
• IT Professionals looking to expand into data science roles
• Business Analysts wanting to understand ML applications
• Technical Project Managers overseeing ML projects
• Cloud Engineers and Architects focusing on AWS services
• Professionals seeking to understand customer churn analytics
• Data Engineers who want to prepare and manage data for ML
• AI/ML Consultants advising on model training and tuning
• Students pursuing careers in data science and machine learning

Learning Objectives - What you will Learn in this Practical Data Science with Amazon SageMaker?

Introduction to the Course's Learning Outcomes and Concepts Covered

In the Practical Data Science with Amazon SageMaker course, students will learn to build, train, tune, and deploy machine learning models using AWS SageMaker.

Learning Objectives and Outcomes

• Understand the different types of machine learning (supervised, unsupervised, and reinforcement learning) and their applications.
• Identify various job roles within the machine learning field and the responsibilities associated with them.
• Gain knowledge of the steps involved in the machine learning pipeline, from data preparation to model deployment.
• Learn to define training and test datasets and get introduced to Amazon SageMaker's capabilities and environment.
• Formulate real-world problems into machine learning tasks using the example of customer churn and prepare datasets for analysis.
• Acquire skills in data analysis and visualization, including cleaning data and understanding the relationships between features.
• Master training and evaluating machine learning models with SageMaker, using algorithms such as XGBoost, and understand how to set hyperparameters effectively.
• Learn to automatically tune models in SageMaker for optimal performance and efficiency.
• Deploy models into production with best practices, including A/B testing and auto-scaling, to handle varying loads.
• Understand the relative cost of errors in machine learning models and learn to manage trade-offs between different types of errors.