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Udemy - 2019 AWS SageMaker and Machine Learning – With Python

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Description

 

Description  

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:


 Cloud based machine learning keeps you focused on the current best practices. 
 In this course, you
will Learn most useful algorithms. Don’t waste your time sifting through mountains of techniques that are in the wild

 Cloud based service is very easy to integrate with your application and has support for wide variety of
programming languages. 
 Whether you have small data or big data, elastic nature of the AWS cloud allows you to
handle them all. 
 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. 
Who this course is for:  

 This course is designed for
anyone who is interested in machine learning and data science 
 If you are new to machine learning, this is a
perfect course to upskill yourself and fastest way to learn machine learning 
 If you are an experienced
practitioner, you will gain insight into AWS Machine Learning capability and learn how you can convert your ideas into
highly scalable solution in a matter of days 
 AWS Certification – If you are preparing for certification, you
will learn best practices and gain hands-on experience on securely deploying products using AWS Cloud 

Requirements  

 All materials and software instructions are covered in housekeeping
lecture 
 Familiarity with a programming language 
 AWS Account – if you want to try the hands-on
activities. AWS charges a small amount for model creation and predictions 
 Some basic knowledge of Pandas, Numpy,
Matplotlib would be helpful but not absolutely needed 

Last updated 5/2019 

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