2019 AWS SageMaker and Machine Learning – With Python

*** 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.

***

*** 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 ***

There are several courses on Machine Learning and AI.  What is special about this course?

Here are the top reasons:

1. Cloud based machine learning keeps you focused on the current best practices.
2. In this course, you will Learn most useful algorithms. Don’t waste your time sifting through mountains of techniques that are in the wild
3. Cloud based service is very easy to integrate with your application and has support for wide variety of programming languages.
4. Whether you have small data or big data, elastic nature of the AWS cloud allows you to handle them all.
5. There is also No upfront cost or commitment – Pay only for what you need and use

In this course, you will learn AI and Machine Learning in three different ways:

AWS Machine Learning

AWS Machine Learning Service is designed for complete beginners.

You will learn three popular easy to understand linear algorithms from the ground-up

You will gain hands-on knowledge on complete lifecycle – from model development, measuring quality, tuning, and integration with your application

AWS SageMaker

The next service is AWS SageMaker.

If you are comfortable coding in Python, SageMaker service is for you.

You will learn how to deploy your own Jupyter Notebook instance on the AWS Cloud.

You will gain hands-on model development experience on very powerful and popular machine learning algorithms like

• XGBoost – a gradient boosted tree algorithm that has won several competitions,
• Recurrent Neural Networks for Time Series forecasting,
• Factorization Machines for high dimensional sparse datasets like Click Stream data
• Neural Network based Image Classifiers,
• Dimensionality reduction with Principal Component Analysis
• and much more

Application Services

In Application Services section of this course,

You will learn about a set of pre-trained services that you can directly integrate with your application.

You will gain hands-on experience in ready-to-use Vision service for image and video analysis, Conversation chatbots and Language Services for text translation, Speech recognition, and text to speech and more

I am looking forward to seeing you in the course.