Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Can I take the course for free?

No, you cannot take this course for free. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you cannot afford the fee, you can apply for financial aid.

Will I earn university credit for completing the Specialization?

This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

Exam Prep MLS-C01: AWS Certified Specialty Machine Learning 专项课程

Launch your career in Machine Learning. Learn the fundamentals of AWS ML tools to prepare for AWS Certified Machine Learning Specialty Certification

位教师：Whizlabs Instructor

3,679 人已注册

包含在 Coursera Plus 中

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5 门课程系列

深入学习学科知识

3.4

（30 条评论）

初级等级

推荐体验

4 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

5 门课程系列

深入学习学科知识

3.4

（30 条评论）

初级等级

推荐体验

4 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Fundamentals of Machine Learning Algorithms
Demonstrate AWS Services for Machine Learning.
Preparation for AWS Certified Machine Learning Specialty Certification

要了解的详细信息

可分享的证书

添加到您的领英档案

授课语言：英语（English）

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培养对关键概念的深入理解
通过 Whizlabs 获得职业证书

专业化 - 5门课程系列

Gain Skills to understand the fundamentals of Machine Learning.
Learn working with various AWS Services necessary for Machine Learning.
Hands on Experience working with AWS Management Console.
Prepare for AWS Certified Machine Learning Specialty Certification

应用的学习项目

Learners will e provided with Lab Demonstrations based on the topics discussed in the courses that are part of this specialization. They will also have the opportunity to practice labs on their AWS Console for hands on demonstrations. However, These hands on labs are optional to be performed by learners.

Data Engineering in AWS

第 1 门课程4小时

您将学到什么

Analyze various data gathering techniques
Analyze techniques to handle missing data
Implement feature extraction and feature selection with Principal Component Analysis and Variance Thresholds

您将获得的技能

类别：AWS SageMaker

类别：Feature Engineering

类别：Data Manipulation

类别：Data Migration

类别：Dimensionality Reduction

类别：Amazon Web Services

类别：Data Cleansing

类别：Data Collection

类别：Jupyter

类别：Unsupervised Learning

类别：Machine Learning

类别：Exploratory Data Analysis

Exploratory Data Analysis in AWS

第 2 门课程6小时

您将学到什么

Analyze and visualize data for machine learning
Demonstrate the implementation of Kinesis Data Streams
Examine AWS Glue service with creation of crawler and transform job

您将获得的技能

类别：Extract, Transform, Load

类别：Amazon Redshift

类别：AWS Kinesis

类别：Data Processing

类别：Real Time Data

类别：Exploratory Data Analysis

类别：Data Sharing

类别：Machine Learning

类别：Amazon S3

类别：Data Management

类别：Data Visualization Software

类别：Applied Machine Learning

类别：Data Analysis

类别：Data Transformation

Modeling in AWS

第 3 门课程4小时

您将学到什么

Analyze Modeling Concepts and train Machine Learning Models
Examine performance of machine learning models
Implement automatic model tuning by training a model

您将获得的技能

类别：AWS SageMaker

类别：Predictive Modeling

类别：Machine Learning

类别：Machine Learning Algorithms

类别：Data Modeling

类别：Applied Machine Learning

类别：Performance Tuning

类别：Amazon Web Services

ML Algorithms

第 4 门课程4小时

您将学到什么

Determine algorithm concepts in ML
Design Regression algorithms and Classification based algorithms
Examine Reinforcement learning algorithms and Forecasting algorithms

您将获得的技能

类别：Regression Analysis

类别：Unsupervised Learning

类别：Machine Learning Algorithms

类别：Forecasting

类别：Supervised Learning

类别：Time Series Analysis and Forecasting

类别：AWS SageMaker

类别：Reinforcement Learning

类别：Text Mining

类别：Algorithms

类别：Classification And Regression Tree (CART)

类别：Image Analysis

类别：Machine Learning

Machine Learning Implementation and Operations in AWS

第 5 门课程6小时

您将学到什么

Design machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance
Implement appropriate machine learning services and features for a given problem
Develop machine learning solutions with lab demonstration

您将获得的技能

类别：AWS SageMaker

类别：Data Security

类别：Data Management

类别：Amazon Elastic Compute Cloud

类别：Cloud Computing

类别：Machine Learning

类别：AWS Identity and Access Management (IAM)

类别：Technical Design

类别：Cloud Security

类别：MLOps (Machine Learning Operations)

类别：Scalability

类别：Amazon Web Services

类别：Disaster Recovery

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

Whizlabs Instructor

Whizlabs

132 门课程86,970 名学生

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自 2020开始学习的学生

''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'

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无限制访问 10,000+ 世界一流的课程、实践项目和就业就绪证书课程 - 所有这些都包含在您的订阅中

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常见问题

10 Weeks

There is no pre-requisite for the AWS Certified Machine Learning - Specialty Specialization. However, Some recommended knowledge and experience for AWS Certified Machine Learning - Specialty Specialization is described below:

Minimum two year of hands-on experience in architecting, building or running ML/deep learning workloads on the AWS Cloud.
Experience in handling ML/deep learning frameworks.
Basic understanding and ability to express the purpose of the Machine learning algorithms.
Ability to perform basic hyperparameter optimization.
Ability to incorporate operational and deployment practices.