Machine Learning Complete Course
Develop Cognitive Application on AWS and Microsoft AZURE
Development ,Data Science,Machine Learning
Lectures -53
Duration -4 hours
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Course Description
In this course, we will learn and practice all the services of AWS Machine Learning that is being offered by AWS Cloud. There will be both theoretical and practical sections of each AWS Machine Learning service. This course is for those who love machine learning and would like to build applications based on cognitive computing, AI, and ML.
You could integrate these services into your Web, Android, IoT, and Desktop Applications like Face Detection, ChatBot, Voice Detection, Text to Custom Speech (with pitch, emotions, etc.), Speech Text, Sentimental Analysis on Social Media, or any textual data.
Machine Learning Services like
Amazon Sagemaker to build, train, and deploy machine learning models at scale
Amazon Comprehend for natural Language processing and text analytics
Amazon Lex for conversational interfaces for your applications, powered by the same deep learning technologies as Alexa
Amazon Polly to turn text into lifelike speech using deep learning
Object and scene detection, Image moderation, Facial analysis, Celebrity recognition, Face comparison, Text in images, and many more
Amazon Transcribe for automatic speech recognition
Amazon Translate for natural and accurate language translation
Machine learning and cloud computing are trending topics and also have a lot of job opportunities
If you have an interest in machine learning as well as cloud computing, then this course is for you. This course will let you use your machine-learning skills to deploy in the cloud. There are various cloud platforms, but only a few are popular, like Azure, AWS, IBM Bluemix, and GCP.
Microsoft Azure is a cloud platform where we will deploy machine learning skills.
In this course, you will learn the following topics:
Concept of Machine Learning
Cognitive Services
Text Analytics
Detecting Language
Analyze images and videos
Recognition handwritten from text
Generate Thumbnail
Content Moderator
Translate
and many more things
ALL THE BEST !!
Goals
- Amazon Sagemaker to build, train, and deploy machine learning models at scale
- Amazon Comprehend for natural Language processing and text analytics
- Amazon Lex for conversational interfaces for your applications, powered by the same deep learning technologies as Alexa
- Amazon Polly to turn text into lifelike speech using deep learning
- Object and scene detection, Image moderation, Facial analysis, Celebrity recognition, Face comparison, Text in images, and many more
- Amazon Transcribe for automatic speech recognition
- Amazon Translate for natural and accurate language translation
- Echo Bot
- Facebook Chatbot
- Question and Answer Maker
- LUIS(Language Understanding)
- Text Analytics
- Detecting Language
- Analyze images and videos
- Recognition handwritten from text
- Generate Thumbnail
- Content Moderator
- Custom Vision
- Translate
- Simple chatbots integrated into HTML websites
Prerequisites
- If you have a background in computer science or development, it would be beneficial.
- You should have an AWS Account or Microsoft Azure account
- You should have a Facebook business page

Curriculum
Check out the detailed breakdown of what’s inside the course
AZURE Machine Learning Overview
1 Lectures
-
Introduction to Azure Machine Learning 11:36 11:36
Computer vision
2 Lectures

Content Moderator
4 Lectures

Custom Vision
4 Lectures

Text Analytics
5 Lectures

Translate
2 Lectures

AWS Machine Learning
1 Lectures

Amazon Comprehend
5 Lectures

Amazon Lex and Amazon Polly
6 Lectures

Amazon Rekognition
9 Lectures

Amazon SageMaker and AWS DeepLens
2 Lectures

Machine Learning (Using Amazon ML to Predict Responses to a Marketing Offer)
5 Lectures

Amazon Transcribe and Translate
6 Lectures

Instructor Details

Pranjal Srivastava
I am an Instructor, Devops engineer, machine learning enthusiast, cloud expert and passionate developer.
I have authored 78 courses with over 115,000 students worldwide across 175+ countries on wide array of technologies like devops, containerization, machine learning, Linux, programming languages and cloud computing platforms like Microsoft Azure, Amazon Web Service and IBM Cloud.
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