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AWS Machine Learning Certification Exam | Complete Guide

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AWS Machine Learning Certification Exam | Complete Guide, available at $99.99, has an average rating of 4.41, with 177 lectures, 127 quizzes, based on 1400 reviews, and has 12260 subscribers.

You will learn about Data Engineering Data types, Python Libraries (pandas, Numpy, scikit Learn, MatplotLib, Seaborn), data distributions, timeseries, Feature Engineering (imputation, binning, encoding, and normalization) AWS Services and Algorithms Amazon SageMaker, Amazon S3 Storage services, AWS Glue AWS Kinesis Services (Kinesis firehose, Kinesis video streams, Kinesis data streams, Kinesis analytics) Redshift, Redshift Spectrum, DynamoDB, Athena, Amazon Quicksight, Elastic Map Reduce (EMR) Rekognition, Lex, Polly, Comprehend, Translate, transcribe, BlazingText Word2Vec, DeepAR, Factorization Machines, Gradient Boosted Trees (XGBoost) Image Classification (ResNet), IP Insights, K-Means Clustering, K-Nearest Neighbor (k-NN) Latent Dirichlet Allocation (LDA), Linear Learner (Classification), Linear Learner (Regression) Neural Topic Modelling (NTM), Object2Vec, Object Detection, Principal Component Analysis (PCA), Random Cut Forest, Semantic Segmentation, and Seqence2Sequence Machine and Deep Learning Basics This course is ideal for individuals who are Developers and data scientists wanting to get certified in AWS Machine Learning It is particularly useful for Developers and data scientists wanting to get certified in AWS Machine Learning.

Enroll now: AWS Machine Learning Certification Exam | Complete Guide

Summary

Title: AWS Machine Learning Certification Exam | Complete Guide

Price: $99.99

Average Rating: 4.41

Number of Lectures: 177

Number of Quizzes: 127

Number of Published Lectures: 173

Number of Published Quizzes: 127

Number of Curriculum Items: 304

Number of Published Curriculum Objects: 300

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Data Engineering
  • Data types, Python Libraries (pandas, Numpy, scikit Learn, MatplotLib, Seaborn), data distributions, timeseries, Feature Engineering (imputation, binning, encoding, and normalization)
  • AWS Services and Algorithms
  • Amazon SageMaker, Amazon S3 Storage services, AWS Glue
  • AWS Kinesis Services (Kinesis firehose, Kinesis video streams, Kinesis data streams, Kinesis analytics)
  • Redshift, Redshift Spectrum, DynamoDB, Athena, Amazon Quicksight, Elastic Map Reduce (EMR)
  • Rekognition, Lex, Polly, Comprehend, Translate, transcribe, BlazingText Word2Vec, DeepAR, Factorization Machines, Gradient Boosted Trees (XGBoost)
  • Image Classification (ResNet), IP Insights, K-Means Clustering, K-Nearest Neighbor (k-NN)
  • Latent Dirichlet Allocation (LDA), Linear Learner (Classification), Linear Learner (Regression)
  • Neural Topic Modelling (NTM), Object2Vec, Object Detection, Principal Component Analysis (PCA), Random Cut Forest, Semantic Segmentation, and Seqence2Sequence
  • Machine and Deep Learning Basics

Who Should Attend

  • Developers and data scientists wanting to get certified in AWS Machine Learning

Target Audiences

  • Developers and data scientists wanting to get certified in AWS Machine Learning

Update 01/02/2020: Section #13 on Machine Learning Implementation and Operations is released.

Machine and Deep Learning are the hottest tech fields to master right now! Machine/Deep Learning techniques are widely adopted in many fields such as banking, healthcare, transportation and technology. Amazon has recently introduced the AWS machine Learning Certification Speciality exam and its quite challenging! AWS Certified Machine Learning Specialty is targeted at data scientists and developers who design, train and deploy AI/ML models to solve real-world challenging problems.

The bad news: this exam is a very challenging AWS exam since it tests the candidate’s knowledge on multiple aspects such as (1) Data Engineering and Feature Engineering, (2) AI/ML Models selection, (3) Appropriate AWS services solution to solve business problem, (4) AI/ML models building, training, and deployment, (5) Model optimization and Hyperparameters tuning. You need to answer these questions in order to pass the exam:

o How to select proper ML technique to solve a given business problem?

o Which AWS service could work best for a given problem?

o How to design, implement and scale secure ML solutions?

o How to choose the most cost-effective solution?

The good news:With over 500+ slides and over 50 practice questions, this course is by far the most comprehensive course on the market that provides students with the foundational knowledge to pass the AWS Machine Learning Certification exam like a pro! This course covers the most important concepts without any fillers or irrelevant information.

Course Curriculum

Chapter 1: INTRODUCTION, DATA/ML LINGO, AWS DATA STORAGE

Lecture 1: What makes this course unique?

Lecture 2: AWS Machine Learning Exam Overview

Lecture 3: Course Outline

Lecture 4: EXTRA: Learning Path

Lecture 5: Guidelines and Best Practices

Lecture 6: Section Introduction

Lecture 7: What is Machine Learning and AI – Part 1

Lecture 8: What is Machine Learning and AI – Part 2

Lecture 9: Amazon Web Services

Lecture 10: AIML Data Lingo – Labeled vs. unlabeled

Lecture 11: AIML Data Lingo – Data Types

Lecture 12: Database vs. datalake vs. warehouse

Lecture 13: AWS Storage S3 DynamoDB RDS

Lecture 14: GET YOUR EXTRA MATERIALS

Lecture 15: Section 1 Slides

Chapter 2: AMAZON S3

Lecture 1: Section Introduction

Lecture 2: Amazon S3 Partitions and Tags

Lecture 3: S3 Storage Tiers and LifeCycle Polices

Lecture 4: S3 Encryption

Lecture 5: S3 Security – Part 1

Lecture 6: S3 Security – Part 2

Lecture 7: Additional Information

Lecture 8: GET YOUR EXTRA MATERIALS

Lecture 9: Section 2 Slides

Chapter 3: AWS DATA MIGRATION, GLUE, PIPELINE, STEP and BATCH

Lecture 1: Section Introduction

Lecture 2: AWS Glue – part #1

Lecture 3: AWS Glue – part #2

Lecture 4: AWS Data Pipeline

Lecture 5: AWS Data Migration Service DMS

Lecture 6: AWS Batch

Lecture 7: AWS Step Function

Lecture 8: GET YOUR EXTRA MATERIALS

Lecture 9: Section 3 Slides

Chapter 4: DATA STREAMING AND KINESIS

Lecture 1: Section Introduction

Lecture 2: Kinesis Overview

Lecture 3: AWS Kinesis Video Streams – Part 1

Lecture 4: AWS Kinesis Video Streams – Part 2

Lecture 5: AWS Kinesis Data Streams – Part 1

Lecture 6: AWS Kinesis Data Streams – Part 2

Lecture 7: AWS Kinesis Firehose

Lecture 8: AWS Kinesis Analytics – Part 1

Lecture 9: AWS Kinesis Analytics – Part 2

Lecture 10: GET YOUR EXTRA MATERIALS

Lecture 11: Section 4 Slides

Chapter 5: JUPYTER NOTEBOOK, SCIKIT-LEARN, PYTHON PACKAGES, AND DISTRIBUTIONS

Lecture 1: Section Introduction

Lecture 2: Jupyter Notebooks and Scikit Learn

Lecture 3: Python Packages (Pandas, Numpy, Matplotlib and Seaborn)

Lecture 4: Data Visualization

Lecture 5: Distributions (Normal, Standard, Poisson, Bernoulli)

Lecture 6: Time Series

Lecture 7: GET YOUR EXTRA MATERIALS

Lecture 8: Section 5 Slides

Chapter 6: ATHENA, QUICKSIGHT, EMR

Lecture 1: Section Introduction

Lecture 2: Athena – Part 1

Lecture 3: Athena – Part 2

Lecture 4: Amazon Quicksight – Part 1

Lecture 5: Amazon Quicksight – Part 2

Instructors

Rating Distribution

  • 1 stars: 33 votes
  • 2 stars: 35 votes
  • 3 stars: 147 votes
  • 4 stars: 449 votes
  • 5 stars: 736 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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