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2020 AWS SageMaker, AI and Machine Learning - With Python

Link : 2020 AWS SageMaker, AI and Machine Learning - With Python 
2020 AWS SageMaker, AI and Machine Learning - With Python
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The author of this exam, Frank Kane, is a popular machine learning instructor on Udemy who passed the AWS Certified Machine Learning exam himself on the first try - as well as the AWS Certified Big Data Specialty exam, which the Machine Learning exam builds upon.

Bestseller
by Chandra Lingam

What you'll learn
  • Learn AWS Machine Learning algorithms, Predictive Quality assessment, Model Optimization
  • Integrate predictive models with your application using simple and secure APIs
  • Convert your ideas into highly scalable products in days
  • Practice test and resources to gain AWS Certified Machine Learning - Specialty Certification
 Description

Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep

*** UPDATE JAN-2020 Timed Practice Test and additional lectures for Exam Preparation added

For  Practice Test, look for the section: 2020 Practice Exam - AWS Certified Machine Learning Specialty

For exam overview, gap analysis and preparation strategy, look for 2020 - Overview - AWS Machine Learning Specialty Exam

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*** UPDATE DEC-2019  Third update for this month!!! AWS Certified Machine Learning Specialty Exam Overview and Preparation Strategies lectures added to the course!  Timed Practice Exam is coming soon!

Also added, two new lectures that gives an overview of all SageMaker Built-in Algorithms, Frameworks and Bring-Your-Own Algorithm Supports

Look for lectures starting with 2020

***

*** UPDATE DEC-2019.  In the  Neural Network and Deep Learning section, we will look at  the core concepts behind neural networks, why deep learning is popular these days, different network architectures and hands-on labs to build models using Keras, TensorFlow, Apache MxNet: 2020 Deep Learning and Neural Networks

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*** UPDATE DEC-2019.  New reference architecture section with hands-on lab that demonstrates how to build a data lake solution using AWS Services and the best practices: 2020 AWS S3 Data Lake Architecture. This topic covers essential services and how they work together for a cohesive solution.  Covers critical topics like S3, Athena, Glue, Kinesis, Security, Optimization, Monitoring and more.

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*** UPDATE NOV-2019. AWS Artificial Intelligence material is now live!

Within a few minutes, you will learn about algorithms for sophisticated facial recognition systems, sentiment analysis, conversational interfaces with speech and text and much more.

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*** UPDATE OCT-2019. New XGBoost Lectures, Labs, do-it-yourself exercises, quizzes, Autoscaling, high availability,  Monitoring, security, and lots of good stuff

*** UPDATE MAY-2019.  1. Model endpoint integration with hands-on-labs for (Direct Client, Microservice, API Gateway).  2. Hyperparameter Tuning - Learn how to automatically tune hyperparameters ***

*** UPDATE MARCH-12-2019.  I came to know that new accounts are not able to use AWSML Service.  AWS is asking new users to use SageMaker Service.

I have restructured the course to start with SageMaker Lectures First.  Machine Learning Service Lectures are still available in the later parts of the course.  Newly updated sections start with 2019 prefix.

All source code for SageMaker Course is now available on Github

The new house keeping lectures cover all the steps for setting up code from GitHub.

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*** SageMaker Lectures -  DeepAR - Time Series Forecasting, XGBoost - Gradient Boosted Tree algorithm in-depth with hands-on.  XGBoost has won several competitions and is a very popular Regression and Classification Algorithm, Factorization Machine based Recommender Systems and PCA for dimensionality reduction ***
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