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Practical Data Science with Amazon SageMaker Certification Training
This 1-day Practical Data Science with Amazon SageMaker course equips you with essential skills for leveraging Amazon's powerful SageMaker platform. You will learn through hands-on exercises tailored to real-world data challenges, mastering the tools and techniques used by in
Practical Data Science with Amazon SageMaker Certification Training
• Comprehensive post-training support for continued success
• Hands-on training to refine your skills
• Benefit from instruction by AWS Certified Instructors
Course Overview
This Practical Data Science with Amazon SageMaker course is designed to provide participants with hands-on experience and practical skills in leveraging Amazon SageMaker for data science projects. Through a comprehensive curriculum, participants will learn how to utilize SageMaker's suite of tools and services to tackle real-world data science challenges efficiently and effectively.
The course begins with an introduction to Amazon SageMaker, covering its key features, capabilities, and how it fits into the broader data science ecosystem. Participants will then delve into data preprocessing techniques, exploring methods for cleaning, transforming, and preparing datasets for analysis.
As the course progresses, participants will learn advanced machine learning concepts and algorithms, including supervised and unsupervised learning techniques, model evaluation, and hyperparameter tuning. Practical exercises and projects will allow participants to apply these concepts to real datasets, gaining valuable experience in building and deploying machine learning models with SageMaker.
Additionally, the course will cover topics such as model interpretation and explainability, ensuring participants develop a comprehensive understanding of their models' behavior and performance.
By the end of the course, participants will have the skills and knowledge needed to confidently undertake data science projects using Amazon SageMaker. Whether beginners or experienced practitioners, participants will leave equipped with practical techniques and best practices for leveraging SageMaker to drive insights and value from their data.
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Course Objectives
Upon completing the course, you will be able to:
• Understand the core concepts and principles of Amazon SageMaker.
• Gain proficiency in preprocessing and cleaning datasets for machine learning tasks.
• Learn various supervised and unsupervised machine learning algorithms available in SageMaker.
• Master techniques for model evaluation, selection, and hyperparameter tuning.
• Develop skills in building, training, and deploying machine learning models using SageMaker.
• Explore advanced topics such as model interpretation and explainability.
• Acquire knowledge of best practices for managing data science workflows on SageMaker.
• Learn how to integrate SageMaker with other AWS services for scalable and efficient data processing.
• Gain hands-on experience through practical exercises and real-world projects.
• Obtain the skills necessary to confidently undertake end-to-end data science projects using Amazon SageMaker.
Skills You Will Acquire:
• Familiarity with Python programming language.
• Basic understanding of machine learning concepts.
• Ability to preprocess data using Amazon SageMaker.
• Competence in model evaluation and tuning.
• Proficiency in deploying models with SageMaker
Audience
• Data scientists
• Machine learning engineers
• Software developers
• Data analysts
• Business analysts
• IT professionals interested in machine learning and data science
Prerequisite
• Proficiency in Python programming language
• Foundational knowledge of Machine Learning
Course Outline
Introduction to machine learning
- Categories of ML
- Occupations in ML
- Stages in the ML pipeline
Introduction to data prep and SageMaker
- Varieties of ML
- Careers in ML
- Phases in the ML pipeline
Problem formulation and dataset preparation
- Business Challenge: Customer Attrition
- Examine Customer Churn Dataset
Data analysis and visualization
- Demonstration: Loading and Exploring the Dataset
- Exercise 1: Correlating Features with Target Variables
- Exercise 2: Analyzing Relationships Among Attributes
- Demonstration: Data Cleaning Process
Training and evaluating a model
- Types of Algorithms
- XGBoost and Amazon SageMaker
- Demonstration: Data Training
- Exercise 3: Completing Estimator Definition
- Exercise 4: Configuring Hyperparameters
- Exercise 5: Model Deployment
- Demonstration: Hyperparameter Tuning with SageMaker
- Demonstration: Assessing Model Performance
Automatically tune a model
- Automatic Hyperparameter Tuning with SageMaker
- Exercises 6-9: Hyperparameter Tuning Jobs
Deployment / production readiness
- Deploying a Model to an Endpoint
- A/B Deployment for Testing
- Auto Scaling Configuration
- Demonstration: Auto Scaling Setup and Testing
- Demonstration: Monitoring Hyperparameter Tuning Job
- Demonstration: Utilizing AWS Auto Scaling
- Exercises 10-11: Implementing AWS Auto Scaling
Relative cost of errors
- Cost Analysis of Different Error Types
- Demo: Determining Binary Classification Cutoff
Amazon SageMaker architecture and features
- Accessing Amazon SageMaker Notebooks within a VPC
- Amazon SageMaker Batch Transforms
- Amazon SageMaker Ground Truth
- Amazon SageMaker Neo
Choose Your Preferred Mode
Online Training
- 1 days Instructor-led Online Training
- Experienced Subject Matter Experts
- Approved and Quality Ensured training Material
- 24*7 leaner assistance and support
Corporate Training
- Blended Learning Delivery Model (Self-Paced E-Learning And/Or Instructor-Led Options)
- Course, Category, And All-Access Pricing
- Enterprise-Class Learning Management System (LMS)
- Enhanced Reporting For Individuals And Teams
- 24x7 Teaching Assistance And Support
FAQ’s
What is Amazon SageMaker, and why is it important for data science?
Amazon SageMaker is a fully managed service that enables developers and data scientists to build, train, and deploy machine learning models quickly and efficiently. It's important for data science because it streamlines the entire machine learning workflow, from data preparation to model deployment, making it easier to scale and accelerate the development of machine learning projects.
Do I need prior experience in data science to enroll in this course?
While prior experience in data science can be beneficial, it's not a requirement for this course. The course is designed to cater to individuals with varying levels of experience, from beginners to intermediate learners. Our instructors will guide you through the fundamentals and provide support as you progress through the course.
What topics are covered in the course curriculum?
The course covers a wide range of topics essential for practical data science with Amazon SageMaker, including data preprocessing, model training and tuning, deployment strategies, and best practices for machine learning workflows. Additionally, you'll learn how to use specific SageMaker features and algorithms to solve real-world data science problems.
Are there any prerequisites for enrolling in this course?
While there are no strict prerequisites, a basic understanding of machine learning concepts and familiarity with Python programming language would be beneficial. If you're new to these concepts, we recommend completing introductory courses or tutorials beforehand to ensure a smoother learning experience.
Will I receive a certificate upon completing the course?
Yes, upon successful completion of the course, you will receive a certificate of completion from Vinsys. This certificate can be a valuable addition to your resume and demonstrate your proficiency in practical data science with Amazon SageMaker.
Is the course entirely online, or are there in-person components?
Currently, the course is offered entirely online, allowing you to learn at your own pace from anywhere with an internet connection. However, we also offer customized corporate training options for organizations looking to provide in-person training for their teams.
How long does it take to complete the course?
The duration of the course is 1-day.
Will I have access to course materials after completing the course?
Yes, you will have access to the course materials, including lecture recordings, slides, code samples, and additional resources, for a specified period after completing the course. This allows you to review the material and refresh your knowledge as needed.
Are there any opportunities for hands-on practice or real-world projects?
Absolutely! The course includes hands-on exercises and practical assignments designed to reinforce your learning and apply the concepts covered in real-world scenarios. You'll have the opportunity to work on projects that simulate common data science challenges, allowing you to build a portfolio of practical experience.
How can I enroll in the Practical Data Science with Amazon SageMaker course?
To enroll in the course, simply visit the Vinsys website or contact our enrollment team directly. You'll be guided through the registration process and provided with all the necessary information to get started on your journey to mastering practical data science with Amazon SageMaker
Why Vinsys
Seasoned Instructors
Official Vendor Partnerships
Authorized Courseware
3,000+ Courses & 2,000+ Modules
In Synch with Tech-advancements
Customizable Blended Learning Options
Reviews
The Practical Data Science with Amazon SageMaker course at Vinsys provided me with a comprehensive understanding of how to leverage SageMaker for real-world data science projects. The hands-on exercises and projects were particularly valuable as they allowed me to apply the concepts learned in the course to actual datasets. The instructors were highly knowledgeable and provided excellent support throughout the course. I would highly recommend this course to anyone looking to enhance their skills in data science using Amazon SageMaker.
Shradha ChahalData Scientist
I recently completed the Practical Data Science with Amazon SageMaker course at Vinsys, and I am highly satisfied with the experience. The course materials were well-designed and easy to follow, even for someone like me who was relatively new to data science. The instructors were incredibly knowledgeable and supportive, always willing to answer questions and provide guidance. I especially enjoyed the interactive nature of the course, with plenty of opportunities for hands-on learning. Thanks to this course, I now feel equipped to tackle data science projects using Amazon SageMaker effectively. I highly recommend this course to anyone looking to upskill in the field of data science.
Urvashi KhareData Base Enginner
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