Machine Learning with AWS
Machine Learning with AWS, available at $49.99, has an average rating of 3.3, with 55 lectures, based on 56 reviews, and has 808 subscribers.
You will learn about 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 image and many more Amazon Transcribe for automatic speech recognition Amazon Translate for natural and accurate language translation This course is ideal for individuals who are Anyone who is curious to experiment with Artificial Intelligence and develop applications or simply interested in exploring AWS Machine Learning and its powerful services available on AWS Cloud. It is particularly useful for Anyone who is curious to experiment with Artificial Intelligence and develop applications or simply interested in exploring AWS Machine Learning and its powerful services available on AWS Cloud.
Enroll now: Machine Learning with AWS
Summary
Title: Machine Learning with AWS
Price: $49.99
Average Rating: 3.3
Number of Lectures: 55
Number of Published Lectures: 55
Number of Curriculum Items: 55
Number of Published Curriculum Objects: 55
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- 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 image and many more
- Amazon Transcribe for automatic speech recognition
- Amazon Translate for natural and accurate language translation
Who Should Attend
- Anyone who is curious to experiment with Artificial Intelligence and develop applications or simply interested in exploring AWS Machine Learning and its powerful services available on AWS Cloud.
Target Audiences
- Anyone who is curious to experiment with Artificial Intelligence and develop applications or simply interested in exploring AWS Machine Learning and its powerful services available on AWS Cloud.
Machine learning engineer and data scientist are the two hottest jobs of 2020. To grab this job opportunity one should apply machine learning skills to solve complex problems of the real world.
Here in this course you’ll going to learn various machine learning services provided by Amazon AWS and able to kick start your career. Anyone can enroll this learning path whether you’re fresher or experienced.
This course is technology-driven will help you in seeking a lucrative job in machine learning domain. Enroll this course and showcase your skill set to the world.
In this learning pathway, you’ll able to use Amazon AWS fundamental services and also integrate them in your Web, Android, IoT and Desktop Applications
Machine Learning Services of AWS which you’ll deploy on the AWS cloud are:
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Amazon Sagemaker to build, train, and deploy machine learning models at scale
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Amazon Comprehend for natural Language processing and text analytics
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Amazon Lex for conversational interfaces for your applications powered by the same deep learning technologies as Alexa
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Amazon Polly to turn text into lifelike speech using deep learning
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Amazon Rekognitionhas a wide range of features like Object and scene detection, Image moderation, Facial analysis, Celebrity recognition, Face comparison, Text in image and much more
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Amazon Transcribe for automatic speech recognition
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Amazon Translate for natural and accurate language translation
As mentioned earlier, this course is industry oriented, therefore it has lots of lot projects which helps you in applying machine learning skills. There is a section, completely focuses on AWS AutoML if you think that you’re newbie, don’t know how to ML algorithms. Don’t worry for such scenario, AutoML plays a vital role. It will help you to job done in autopilot mode. Other than this you’ll also learn to develop your chatbot and virtual assistant for your sites, helping your customers 24*7.
For Python Developers, who want to apply their machine learning skills on a higher scale than why to waste money on buying the resources. Use the power of cloud computing and implement all your machine learning skills with the help of Boto3, a python framework for managing AWS cloud services.
All the best !!!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Difference between AI and Machine Learning
Chapter 2: Quick Summary of all AWS Machine Learning services
Lecture 1: AI services [No machine learning skill]
Chapter 3: Quick Recap
Lecture 1: Quick Recap – NUMPY
Lecture 2: Quick Recap – Pandas
Chapter 4: NLP Project
Lecture 1: Build frontend for ML Application
Lecture 2: Build Backend for ML Application
Lecture 3: Add NLP task (translation)
Lecture 4: Demo: Translation ML app
Lecture 5: Creating Sentiment Analysis ML app
Lecture 6: Demo: Sentiment Analysis ML app
Lecture 7: POS tagging ML App
Lecture 8: Detect entity
Chapter 5: Amazon Comprehend
Lecture 1: Amazon Comprehend
Lecture 2: Practical:Amazon Comprehend
Lecture 3: (PythonBoto3)Comprehend 1
Lecture 4: (PythonBoto3)Comprehend 2
Lecture 5: (PythonBoto3)Comprehend 3
Chapter 6: Amazon Lex and Amazon Polly
Lecture 1: Amazon Lex
Lecture 2: Amazon Lex 2
Lecture 3: Amazon Polly
Lecture 4: Practical:Chatbot using Amazon Lex
Lecture 5: Practical:Amazon Polly
Lecture 6: (PythonBoto3)Polly
Chapter 7: Amazon Rekognition
Lecture 1: Amazon Rekognition
Lecture 2: Object and scene detection(Overview)
Lecture 3: Object and scene detection(Practical)
Lecture 4: (PythonBoto3)Detect Label
Lecture 5: Facial analysis(Overview)
Lecture 6: Facial analysis(Practical)
Lecture 7: (PythonBoto3)Detect Face
Lecture 8: Celebrity recognition(Overview)
Lecture 9: Celebrity recognition(Practical)
Lecture 10: (PythonBoto3)Celebrity Recognition
Lecture 11: Face comparison(Overview)
Lecture 12: Face comparison(Practical)
Lecture 13: (PythonBoto3)Face Comparison
Lecture 14: Text in image(Overview)
Lecture 15: Text in image(Practical)
Lecture 16: (PythonBoto3)Detect Text in Image
Lecture 17: Visual Analysis
Chapter 8: Amazon Transcribe and Translate
Lecture 1: Amazon Transcribe
Lecture 2: Amazon Translate
Lecture 3: Practical:Amazon Transcribe
Lecture 4: (PythonBoto3)Transcribe
Lecture 5: Practical:Amazon Translate
Lecture 6: (PythonBoto3)Translate
Chapter 9: Amazon SageMaker and AWS DeepLens
Lecture 1: Amazon SageMaker
Lecture 2: AWS DeepLens
Chapter 10: Machine Learning(Using Amazon ML to Predict Responses to a Marketing Offer
Lecture 1: Amazon Machine Learning(Overview)
Lecture 2: ML 1 -Prepare Your Data
Lecture 3: ML 2-Create a Training Datasource
Lecture 4: ML 3-Create an ML Model
Lecture 5: ML 4-Use the ML Model to Generate Predictions
Lecture 6: ML 5-Clean Up
Instructors
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Pranjal Srivastava
Docker | Kubernetes | AWS | Azure | ML | Linux | Python
Rating Distribution
- 1 stars: 9 votes
- 2 stars: 3 votes
- 3 stars: 5 votes
- 4 stars: 8 votes
- 5 stars: 31 votes
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