Cloud Machine Learning Associate Training in Singapore

Module 1 : Introduction to ML and AI tools from AWS

AWS Sagemaker - Overview and features

•  Labs : Deploy  one click Jupyter notebooks(NB)
•  Labs : run sample Pandas programs on cloud jupyter NB

AWS Textract  - overview and Features  

• Labs : Extract text from documents  

AWS Translate - Overview and Features

•  Labs - translate content from English to Chinese language

AWS Transcribe - overview and features

•  Labs - convert speech to text

AWS Rekognition - Overview and features I

• Labs - Object and scene detection 
• Labs - Image Moderation
• Labs - Facial Analysis 
• Labs - Celebrity recognition 
• Labs - Face comparison 
• Labs - Text in Image 
• Labs - Video Analytics 

Amazon Comprehend - NLP 

•   Labs - Analyse unstructured text 

AWS Polly - Overview and features

• Labs - Text to Life like speech conversion 
• AWS Personalize - Overview and Features 
• Amazon DeepLens - Overview and Features 
• Amazon Forecast ( reinforcement learning) - Overview and Features 
• Amazon Lex - overview and features

Module 1: Introduction to Azure Machine Learning

• Azure machine learning overview.
• Introduction to Azure machine learning studio.
• Developing and hosting Azure machine learning applications
• Hands-on lab sessions Lab:
• Using Exercise and Calories dataset
• Explore Azure Machine Learning Studio
• Upload datasets, Create Experiments,
• How to import data from big data sources and define a data workflow in an experiment.

Module 2 : Building Azure machine learning models with ML Studio

• Categorizing your data
• Importing data to Azure machine learning,
• Exploring and transforming data in Azure machine learning
• Hands on labs
• Prepare Azure SQL database, Import data, Visualize data
• Train and evaluate a regression model  and a classification model using exercise and calories data set.

Module 3 : Publish Predictive models as Azure Machine Learning services

• Significance of webservice
• How to publish and test a webservice in ML Studio

Lab

• Publish and test a webservice using ML Studio using exercise and calories dataset
• Publishing and consuming a parameterized webservice                      

Module 4:  Building Azure Machine Learning Models with Azure ML Services

• Introduction to Azure Machine Learning Services
• How to build Azure machine learning models with ML services.

Lab:

• Building Azure machine learning models with ML services introduction
• Electricity demand forecast

Module 1 :  Google Machine Learning AI Solutions Overview

Vision AI  : Overview and Concepts 

• • Analyze images in the cloud or at the edge

Video AI: Overview and Features 

• • Precise video analysis — down to the frame

AI Platform Notebooks: Overview and Features 

• • An enterprise notebook service to launch projects in minutes

AI Platform Deep Learning VM Image :Overview and Features 

• • Preconfigured virtual machines for deep learning applications

Kubeflow: Overview and Features 

• • The machine learning toolkit for Kubernetes

Cloud TPU : Overview and Features 

• • Hardware designed for performance

Natural Language : Overview and Features 

• • Multimedia and multi-language processing

Translation : Overview and Features 

• • Fast, dynamic translation tailored to your content

Cloud Speech-to-Text API : Overview and Features 

• • Speech recognition across 120 languages

Cloud Text-to-Speech API  : Overview and Features 

• • Lifelike text-to-speech interactions

Dialogflow : Overview and Features 

• • Conversational experiences across devices and platform

AutoML Tables : Overview and Features 

• • Build state-of-the-art ML models on structured data

Cloud Inference API : Overview and Features 

• • Run large-scale correlations over typed time-series datasets

Recommendations AI (beta) : Overview and Features 

• • Deliver highly personalized product recommendations at scale

BigQuery ML : Overview and Features 

• • Build models with SQL

Cloud AutoML : Overview and Features

• • Train custom ML models quickly and easily

Module 2 : Google cloud Machine Learning Labs

Lab1 : Implementing an AI Chatbot with Google Dialogflow 

The goal of this lab is to introduce the basics of Google Cloud Dialogflow by building a responsive chat bot, such as those handling support requests on websites. Demonstrates how to utilize this interactive AI in application development.

Lab 2 : Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API

The Cloud Vision API lets you understand the content of an image by encapsulating powerful machine learning models in a simple REST API. In this lab you’ll send an image to the Cloud Vision API and have it identify objects, faces, and landmarks.

Lab 3: Google Cloud - Deploy Jupyter notebook instance with GPU and run sample pandas or classification example program

Lab 4 : User vision API to identify text from image sign board (OCR) which is in  chinese    language and translate the text to english using Google  Translate api.