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About The Program



Amazon Machine Learning (Amazon ML) is a cloud-based, robust service that makes it simple for developers of all expertise levels to utilize Machine learning/AI technology. Amazon ML gives tools and wizards that guide you through the way towards making AI (ML) models without learning complex algorithms of Machine learning.

AWS Certified Machine Learning - Specialty exam is for –

  • Data Scientists
  • Business Decision Makers
  • Developers
  • Data Platform Engineers
  • One who is aspired to build a career in ML
  • Try not to stress if you are in all day work, you'll need 15-20 hours for the AWS Certified Machine Learning - Specialty Practice Tests. Get an aggregate of 25-30 hours for exam preparation to take up the AWS Certified Machine Learning - Specialty exam.

According to the report by Paysa, the Amazon Machine Learning Scientist average salary is $214,484.

Some of the important benefits of the AWS Certified Machine Learning - Specialty Certification are as follows:

  • Validates your ability to create, train, and deploy machine learning models using AWS Cloud.
  • Provides you a global recognition for your knowledge, skills, and experience.
  • Listed amongst one of the top-paying info-Tech certifications in the world.

Aws Machine Learning Course Modules


Amazon Machine Learning is an Amazon Web Services product that allows a developer to discover patterns in end-user data through algorithms, construct mathematical models based on these patterns and then create and implement predictive applications.


  • This course introduces the AWS Certified Machine Learning - Specialty learning path which prepares you to take the certification exam.
  • In this course, follow along with AWS certification specialist, Stephen Cole, as he discusses his experience taking the AWS Machine Learning - Specialty Exam.
  • In this course, you'll learn about Machine Learning and where it fits within the wider Artificial Intelligence (AI) field.
  • This course has been expertly created to provide you with a strong foundation in machine learning and deep learning.
  • This course is the first in a two-part series covering the fundamentals of machine learning.
  • This course is part two of the module on machine learning and covers unsupervised learning, the theoretical basis for machine learning, model and linear regression, the semantic gap, and how we approximate the truth.
  • This course covers Distributed Machine Learning, Apache Spark, Amazon Elastic Map Reduce, Spark MLib, and AWS Glue.
  • Develop, visualize, serve, and consume a TensorFlow machine learning model using the Amazon Deep Learning AMI in this Lab.
  • Take control of a p2.xlarge instance equipped with an NVIDIA Tesla K80 GPU to perform CPU vs GPU performance analysis for AWS Machine Learning in this Lab.
  • This course covers the basics of python in machine learning, how to use loops, regressions, and classification, and how to set up machine learning in python.
  • Learn the ways in which data comes in many forms and formats with this course.
  • This course covers the foundations and history of machine learning as well as the principles of memory storage, computing power, and phone/web applications.
  • This course is the first part of the two-part series on the mathematics of machine learning.
  • This course is the second part of the two-part series on the mathematics of machine learning.
  • This course gives an informative introduction to deep learning and introducing neural networks.
  • This course expertly covers the essentials needed to succeed in machine learning.
  • In this course, discover convolutions and the convolutional neural networks involved in Data and Machine Learning.
  • In this course, you'll learn how to use recurrent neural networks to train more complex models.
  • In this course, you'll learn how to improve the performance of your neural networks with this learning path.
  • This course provides a practical understanding of the steps required to build and deploy machine learning models using Amazon SageMaker.
  • Get started with the latest SageMaker Data Wrangler, Data Pipeline and Feature Store services (released at re:invent Dec 2020) and SageMaker Ground Truth
  • The course introduces you to supervised learning and the nearest neighbors algorithm.
  • This course explores hyperparameters, distance functions, similarity measures, logistic regression, the method and workflow of machine learning and evaluation, and the train-test split.
  • In this lab, you'll use a SageMaker notebook to learn how to write Python code to prepare data, train and deploy models, and use them for real-time inference.
  • This playground lab allows you to choose from Amazon's curated library of sample notebooks to learn about what is most important to you.
  • This lab uses Amazon SageMaker to create a machine learning model that forecasts flight delays for US domestic flights.
  • Knowledge Check: Start Modeling Data with Amazon SageMaker
  • Selecting the right machine learning model will help you find success in your projects. In this module, we’ll discuss how to do so, as well the difference between explanatory and associative approaches, before we end on how to use out-sample performance.
  • Knowledge Check: Practical Machine Learning - Module 4
  • This course explores the core concepts of machine learning, the models available, and how to train them.
  • This lab is aimed at machine learning beginners who want to understand how to train custom models.
  • This lab will walk you through building several binary classification models using different model methodologies and then comparing the model predictions using evaluation tools.
  • How do you know that your models will do a good job making predictions on new, unseen data? This lab will discuss the fundamentals.
  • This course explores the core concepts of machine learning, the models available, and how to train them.
  • Regression is a widely used machine learning and statistical tool and it’s important you know how to use it. In this module, we’ll discuss interpreting modes, as well as how to interpret linear classification models.
  • This lab walks you through building several multivariate linear regression models using different prediction variables and evaluating the models' predictions.
  • Knowledge Check: Practical Machine Learning - Module 5
  • This course covers the concept of unsupervised learning within the context of machine learning and how unsupervised learning differs from supervised learning.
  • This course explores the topic of probability and statistics, including various mathematical approaches and some different interpretations of probability.
  • In this course, you'll learn about Amazon Rekognition, a service that enables you to easily and quickly integrate computer vision features directly into your own applications.
  • This lab will walk you through a number of ways to handle missing data including using a default value and building a model to predict the missing data.
  • Learn how to implement object detection on every new image uploaded on Amazon S3.
  • In this course, you'll learn about the key features and components of Amazon Lex, and how to develop, configure, and build an end-to-end Chatbot using the Lex service.
  • Join this Lab and gain experience using an MXNet convolutional neural network to style images and monitor the GPU used for training in Amazon Cloud Watch.
  • This course explains AWS Identity & Access Management (IAM), what it is, and how to implement it.
  • Learn how to manage our organization using IAM Users and Groups and IAM Roles
  • Knowledge Check: Overview of AWS Identity and Access Management (IAM)
  • This course covers the wide range of storage services within AWS, their key features, and when and why you would use them.
  • In this course, you'll learn to recognize and explain what encryption is at a high level as well as the various encryption options provided by AWS.
  • This course will look at some of the management and bucket property features that Amazon S3 has to offer, and how you can use them to maintain and control your data.
  • This course explores two different Amazon S3 features: the replication of data between buckets and bucket key encryption when working with SSE-KMS to protect your data.
  • In this course, you will learn the basics of KMS, what it will cost to implement, how to encrypt data, and more...
  • Knowledge Check: AWS Storage Fundamentals
  • This course introduces AWS Step Functions and its uses, benefits, and limitations.
  • Learn how to use AWS Step Functions.
  • This course explores the AWS Athena service, reviewing fundamental AWS Athena storage and querying concepts.
  • Use Amazon Athena to query encrypted data on S3 and encrypt the query results as well.
  • This course will take you through the fundamentals of AWS Glue to get you started with the service.
  • This course provides an introduction to Amazon Kinesis including what it does and why it's important.
  • In this course, you'll learn about the key features and core components of Kinesis Analytics, and what an end-to-end real-time data streaming example looks like.
  • Preview Exam: Certified Machine Learning - Specialty for AWS

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FAQs

  • What are the AWS Machine Learning - Specialty exam objectives?

    AWS Certified Machine Learning - Specialty exam objectives are: Validate one’s ability to building, implementing, deploying, and maintaining ML solutions for business problems. Select and legitimize the suitable ML approach for a given business issue. Identify the right AWS administrations to implement solutions to ML. Design and actualize versatile, cost-streamlined, dependable, and secure solutions of Machine Learning.

  • Why and who should go for the Aws Mechine Learning course?

    Aws Mechine Learning is one of the top-notch technologies in cloud computing. In this modern competitive market, every small and big organization is moving into this cloud business for application and software development. Absolute beginners and experienced individuals who want to explore the Aws Mechine Learning culture can opt for Aws Mechine Learning training and certification. Even software developers who want to switch their careers as Aws Mechine Learning engineers, Aws Mechine Learning architect can gear up for certified Aws Mechine Learning online training from DeepNeuron.
  • Who is my trainer and what is his selection process?

    Our subject matter experts (SMEs) are experienced with industry expertise in their preferred domain and technologies. Our selection process is intriguing in reference to the course modules where we seek professional experts who have ideas and worked in the same technology for creating and deployment of applications in real-time. Professional experts SMEs will guide you through the entire module and will also make you get exposed to practice-based learning.
  • What can I expect after the Aws Mechine Learning course accomplishment?

    After the completion of the Aws Mechine Learning training course, you will gain expert knowledge to master the Aws Mechine Learning and will be a proficient player to tap Aws Mechine Learning tools at a depth level. Moreover, you will be part of the DeepNeuron community to leverage the knowledge-base shared by the members around the globe. You will also earn an industry-recognized Aws Mechine Learning certification from DeepNeuron.

  • What are the different modes of Aws Mechine Learning training that DeepNeuron provides?

    DeepNeuron offers Instructor-led online training delivered by highly experienced Aws Mechine Learning experts holding more than 10+ years of industry experience. Our separate cherry-picked pool of subject matter experts work with respective courses in curating the best contents and study materials for greater learning experience. Take our FREE Demo sessions to witness our trainer’s expertise.

  • What is the exam format for the AWS Certified Machine Learning - Specialty certification?

    AWS Certified Machine Learning - Specialty Questions includes:

    • Multiple-choice: Has one correct response and three incorrect responses
    • Multiple-response: Has two or more correct responses out of five or more options.

  • *For which all courses will I get certificates from IBM?

    Following are the list of courses for which you will get IBM certificates:

    • R Programming for Data Science
    • Python for Data Science

  • How do I earn the Master’s certificate?

    Upon completion of the following minimum requirements, you will be eligible to receive the Data Scientist Master’s certificate that will testify to your skills as an expert in Data Science.

    Course

    Course completion certificate

    Criteria
    Data Science and Statistics Fundamentals Required 85% of Online Self-paced completion
    Data Science with R Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation in at least 1 project
    Data Science with SAS Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation in at least 1 project
    Data Science with Python Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in course-end assessment and successful evaluation in at least 1 project
    Machine Learning and Tableau Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and successful evaluation in at least 1 project
    Big Data Hadoop and Spark Developer Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation of at least 1 project
    Capstone Project Required Attendance of 1 Live Virtual Classroom and successful completion of the capstone project

  • How do I enroll for the Data Scientist course?

    You can enroll in this Data Science training on our website and make an online payment using any of the following options:

    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal

    Once payment is received you will automatically receive a payment receipt and access information via email.

  • If I need to cancel my enrollment, can I get a refund?

    Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, please read our Refund Policy.

  • I am not able to access the online Data Science courses. Who can help me?

    Yes, we do offer a money-back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support. portal.

  • Who are the instructors and how are they selected?

    All of our highly qualified Data Science trainers are industry experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.

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