AWS Sagemaker - Hands on Training
Created by Akshay Deep Lamba, Last Updated 22-Jun-2020, Language:English
AWS Sagemaker - Hands on Training
Master AWS Sagemaker by Building Real World Projects
Created by Akshay Deep Lamba, Last Updated 22-Jun-2020, Language:English
What Will I Get ?
- Learn about Different Built-in Algorithm like XgBoost ,Deep AR , Linear Learner , Factorization Machines on SageMaker
- Learn To Deploy custom Machine Learnng Algorithms on SageMaker
- Learn to implement Real world Machine Learning Problem on SageMaker
- Learn to do Hyper Parameter Tuning on SageMaker
- Learn to Deploy Sagemaker Models using Lambda and API Gateway
Requirements
- Basic of Machine Learning
- AWS Account
Description
Master AWS Sagemaker by Building Real World Projects
A well groomed knowledge of the complete AWS Machine Learning ecosystem is required and SageMaker is one of the Most Important component of it.
This course includes real World Projects which enables you to learn and Solidify your concept on Sagemaker.
In this Course, you will learn:-
General Overview of SageMaker
Breast Cancer Classification Using XgBoost
Predicting House Price using Linear Regression
Using KNN for MNIST SIGN Language
Object Detection
Dimension Reductionality using PCA
Recommender System using Factorization Machine
DeepAR for Time Series Forecasting
Blazing Text for Word2Vec
Hosting Custom Model on Credit Card Fraud Detection on Sagemaker
Other Built-in Algorithms
Hyper Parameter Tuning
AWS Lambda Basics
Integration and Deployment Options
We know that you're here because you value your time and Money.By getting this course, you can be assured that the course will explain everything in detail and if there are any doubts in the course, we will answer your doubts in less than 12 hours.
All the project Files are available for you.
So, What are you waiting for? Go Click on the Buy button and let's explore this exciting journey.
I will be waiting for you inside the course...
Cosmic
Course Content
-
Introduction
2 Lectures 00:02:40-
Introduction
Preview00:02:40 -
Course Files
-
-
General Overview of SageMaker
6 Lectures 00:19:21-
Intro to AWS Sagemaker
Preview00:04:36 -
Instance Types
Preview00:02:15 -
Built-in Algorithm
Preview00:06:12 -
Frameworks Offered , AWS Ground Truth and NEO
00:02:45 -
Different API Levels
00:02:21 -
Summary
00:01:12
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-
Prerequisite of Sagemaker
3 Lectures 00:07:23-
Making S3 Bucket
00:01:53 -
Spinning Jupyter Notebook in Sagemaker ( Part 1)
00:03:36 -
Spinning Jupyter Notebook in Sagemaker ( Part 2)
00:01:54
-
-
Basic of Implementing ML Model on Sagemaker
3 Lectures 00:04:47-
Sagemaker ML Model Overview
00:01:23 -
Sagemaker NEO
00:02:20 -
Sagemaker Security
00:01:04
-
-
CLASSIFICATION PROJECT : Breast Cancer Classification
13 Lectures 01:05:37-
General Overview and Business Problem
00:04:19 -
Data importing and Basic Data Cleaning
00:07:09 -
Visualization, Scaling and Encoding Data
00:10:15 -
Model Fitting and getting the Feature Importance
00:12:56 -
Balancing dataset and Feature Selection
00:10:17 -
Setting up everything for AWS Sagemaker
00:03:37 -
Saving Data in S3
00:03:02 -
XgBoost Intro
00:02:07 -
Model Building and Fitting
00:04:35 -
End Point Creation
00:02:49 -
Prediction ( Part 1 )
00:02:15 -
Prediction ( Part 2)
00:01:04 -
Conclusion
00:01:12
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-
Predicting House Price using Linear Regression
9 Lectures 00:30:18-
Intro
00:03:38 -
Data Importing
00:06:30 -
EDA and Visualization
00:07:17 -
Model Building and Prediction
00:01:49 -
Linear Learner Intro
00:03:03 -
Data import and basic Data Cleaning
00:03:07 -
Model Building and Training
00:01:58 -
Endpoint Creation and Predicting
00:01:35 -
Deleting Endpoint
00:01:21
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-
Using KNN in Sagemaker
6 Lectures 00:21:41-
Intro
00:06:24 -
Model Training and Prediction
00:04:12 -
Hyper Paramerter Tuning
00:03:26 -
Data Import
00:02:06 -
Data Preparation and Model Building
00:03:04 -
Endpoint Creation and Testing
00:02:29
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-
Object Detection
6 Lectures 00:18:12-
Intro to Object Detection
00:02:10 -
Downloading Data
00:02:48 -
Preparing the data
00:05:45 -
Uploading the S3
00:01:53 -
Model Building and Training
00:03:30 -
Endpoint Creation
00:02:06
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-
Dimension Reductionality using PCA
7 Lectures 00:25:42-
General Overview to PCA
00:04:56 -
Understanding Components ( Part 1 )
00:04:51 -
Understanding Components ( Part 2 )
00:04:31 -
Getting started and uploading data to S3
00:04:28 -
Model Buidling and Fitting
00:03:24 -
End Point Creation
00:01:36 -
Testing the Endpoint and Getting the Prediction
00:01:56
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-
Blazing Text for Word2Vec
7 Lectures 00:19:48-
Intro to Blazing Text
00:02:55 -
Data Import
00:03:09 -
Basic NLP workflow ( Part 1 )
00:03:08 -
Basic NLP workflow ( Part 2 )
00:01:26 -
Basic NLP workflow ( Part 3 )
00:02:29 -
Model Building and Fitting
00:03:42 -
Evaluating Results
00:02:59
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-
Recommender System using Factorization Machine
5 Lectures 00:15:48-
What is Factorization Machine ?
00:02:14 -
Data Import and Getting Basics Ready
00:05:26 -
Uploading to S3 and Building The Model
00:04:42 -
Model Training and End Point Creation
00:01:44 -
Testing the Endpoint
00:01:42
-
-
DeepAR for TimeSeries Forecasting
1 Lectures 00:02:32-
Intro
00:02:32
-
-
Custom Model on Sagemaker - Fraud Detection
9 Lectures 01:07:01-
Intro and Business Challenge
00:12:51 -
Data Import
00:07:11 -
Feature Engineering and Model Prediction
00:09:03 -
Under Sampling the Data
00:08:40 -
Over Sampling the Data
00:11:33 -
Understanding the Folder Structure AWS Accepts
00:04:13 -
Basic Setup and Model Python File
00:04:20 -
Model Building, Fitting and End Point Creation
00:06:15 -
Conclusion
00:02:55
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-
Other Built-in Algorithms
8 Lectures 00:16:04-
Image Classification
00:02:45 -
Semantic Segmentation
00:02:45 -
Neural Topic Modelling
00:02:34 -
Latent Dirichlet Allocation
00:01:02 -
Seq2Seq
00:02:12 -
Object2Vec
00:02:32 -
Random Cut Forest
00:01:06 -
IP Insights
00:01:08
-
-
Hyper Parameter Tuning
4 Lectures 00:12:36-
Intro
00:03:49 -
Getting Started with creation of Job
00:03:52 -
Setting up the Job
00:02:33 -
Evaluting the Results and Getting the Best Parameteres
00:02:22
-
-
AWS Lambda Basics
4 Lectures 00:24:04-
Intro
00:04:56 -
Getting Started with AWS and Serverless and Adding Configs
00:03:01 -
Creating Serverless and AWS Project using Terminal
00:07:04 -
Deploy the Project on AWS
00:09:03
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-
Integration and Deployment Options with DIABETES PREDICTION
13 Lectures 00:45:25-
General Overview and Business Problem
00:02:50 -
Data Import
00:03:02 -
EDA and Visualization
00:03:30 -
Feature Engineering
00:03:34 -
Model building
00:05:13 -
Balancing the Dataset
00:03:19 -
Refitting the Model
00:04:56 -
Switching everything to AWS Notebook Instance
00:04:06 -
Model Building and Training
00:01:18 -
End Point Creation
00:01:39 -
Making the SLS Project and Coding the YAML File
00:03:11 -
Coding the Handler file, Deployment and Testing
00:07:33 -
Conclusion
00:01:14
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-
Other ML Services
11 Lectures 00:15:21-
Amazon Ground Truth
00:01:51 -
Amazon Comprehend
00:01:05 -
DEMO : Amazon Comprehend
00:02:10 -
Amazon Translate
00:00:52 -
DEMO : Amazon Translate
00:00:52 -
Amazon Transcribe
00:00:51 -
DEMO : Amazon Transcribe
00:01:12 -
Amazon Polly
00:00:53 -
DEMO : Amazon Polly
00:01:12 -
AWS Rekognition
00:01:05 -
Amazon Forecast, Lex and Other Services
00:03:18
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-
Conclusion
1 Lectures 00:00:51-
Conclusion
00:00:51
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Akshay Deep Lamba
Hustler
Hi, We are Cosmic .
Professionally, We are Entrepreneurs with over six years of experience in finance, Tech, Real Estate etc. We are trained at Mobile App Development, Web Development, Artificial Intelligence, Finance etc.
In my courses, you will very well understand how I combine theory and Real Life Case studies to give you the best in-Class Training in any topic I teach.
I am looking forward to sharing my passion and knowledge with you!
Looking forward to working together!
