Machine Learning and Big Data Analytics with AWS

Machine Learning and Big Data Analytics with AWS, available at $19.99, has an average rating of 2.7, with 58 lectures, based on 40 reviews, and has 727 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 Amazon Athena for querying data instantly and get results in seconds Amazon CloudSearch makes it simple and cost-effective to set up, manage, and scale a search solution for your website or application Amazon Elasticsearch to search, analyze, and visualize data in real-time. Amazon Kinesis for easily collecting, processing, and analyzing video and data streams in real time Amazon QuickSight is a fast, cloud-powered BI service that makes it easy to build visualizations and quickly get business insights from your data. 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 Analytics 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 Analytics and its powerful services available on AWS Cloud.

Enroll now: Machine Learning and Big Data Analytics with AWS

Summary

Title: Machine Learning and Big Data Analytics with AWS

Price: $19.99

Average Rating: 2.7

Number of Lectures: 58

Number of Published Lectures: 58

Number of Curriculum Items: 58

Number of Published Curriculum Objects: 58

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

Amazon Athena for querying data instantly and get results in seconds

Amazon CloudSearch makes it simple and cost-effective to set up, manage, and scale a search solution for your website or application

Amazon Elasticsearch to search, analyze, and visualize data in real-time.

Amazon Kinesis for easily collecting, processing, and analyzing video and data streams in real time

Amazon QuickSight is a fast, cloud-powered BI service that makes it easy to build visualizations and quickly get business insights from your data.

Who Should Attend

Anyone who is curious to experiment with Artificial Intelligence and develop applications or simply interested in exploring AWS Machine Learning and Analytics 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 Analytics and its powerful services available on AWS Cloud.

In today’s world, use of Machine Learning, Artificial intelligence and Big data Analytics is bound to grow from here in our daily business environment. And to combat this, it is essential to have Big data analyst, ML & AI experts in the field. So if you have always been keen on sharpening your skills in this field or simply accelerate your growth in this career choice, then ENROLL this course now.

This learning path offers practical hands on [projects with also covering the theoretical concepts about all the services of Analytics and Machine Learning which is being offered by AWS Cloud.

Why Big data analytics is important?

Big Data is everywhere and to extract meaningful insights is the basic necessity for the business as it aids in improving business, decision makings and providing the biggest edge over the competitors. This applies to organizations as well as professionals in the Analytics domain. For professionals, who are skilled in Big Data Analytics, there is an ocean of opportunities out there.

Why Machine Learning is important?

Machine learning has several very practical applications that drive the kind of real business results – such as timeand money savings– that have the potential to dramatically impact the future of your organization. Machine learning engineer and data scientist are the highest paying jobs.

In this learning path, you will deploy a wide range of services related to ML and analytics like

Analytics tools are –

Amazon Athena (Querying data instantly and get results in seconds)

Amazon CloudSearch (Makes it simple and cost-effective to set up, manage, and scale a search solution for your website or application)

Amazon ElasticSearch (Search, Analyze, and Visualize data in real-time.)

Amazon Kinesis (Easily collecting, processing, and analyzing video and data streams in real time)

Amazon QuickSight (Fast, Cloud-powered BI service that makes it easy to build visualizations and quickly get business insights from your data.)

This course also contains many practicals to groom your analytic skills.

Machine Learning Services are-

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

Amazon Rekognition (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

Course Curriculum

Chapter 1: Machine Learning and Analytics with AWS

Lecture 1: Introduction

Lecture 2: Difference between AI and Machine Learning

Lecture 3: AI services [No machine learning skill]

Chapter 2: Quick Recap

Lecture 1: Quick Recap – NUMPY

Lecture 2: Quick Recap – Pandas

Chapter 3: Project [New Content added]

Lecture 1: Build frontend for ML Application

Lecture 2: 2Build 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

Chapter 4: Analytics(Athena)

Lecture 1: Athena

Lecture 2: Project Athena Part 1

Lecture 3: Project Athena Part 2

Lecture 4: Project Athena Part 3

Lecture 5: Project Athena Part 4

Chapter 5: Analytics(CloudSearch)

Lecture 1: CloudSearch

Lecture 2: Practical: CloudSearch Part 1

Lecture 3: Practical: CloudSearch Part 2

Lecture 4: Practical: CloudSearch Part 3

Chapter 6: Analytics(ElasticSearch)

Lecture 1: ElasticSearch

Chapter 7: Analytics(Kinesis)

Lecture 1: Kinesis

Lecture 2: Practical: Kinesis

Lecture 3: Practical: Kinesis(python boto3)

Chapter 8: Analytics(AWS Quicksight)

Lecture 1: Quicksight

Chapter 9: Introduction(AWS AI)

Lecture 1: Introduction

Chapter 10: ML(Amazon Comprehend)

Lecture 1: Amazon Comprehend

Lecture 2: Practical: Amazon Comprehend

Chapter 11: ML(Amazon Lex and Amazon Polly)

Lecture 1: Amazon Lex Part 1

Lecture 2: Amazon Lex Part 2

Lecture 3: Amazon Polly

Lecture 4: Project ChatBot

Lecture 5: Practical:Amazon Polly

Chapter 12: ML(Amazon Rekognition)

Lecture 1: Amazon Rekognition

Lecture 2: Object and scene detection

Lecture 3: Object and scene detection(Practical)

Lecture 4: Facial analysis

Lecture 5: Facial analysis(Practical)

Lecture 6: Celebrity recognition

Lecture 7: Celebrity recognition(Practical)

Lecture 8: Face comparison

Lecture 9: Face comparison(Practical)

Lecture 10: Text in image

Lecture 11: Text in image(Practical)

Lecture 12: Video analysis

Chapter 13: ML(Amazon Transcribe and Translate)

Lecture 1: Amazon Transcribe

Lecture 2: Amazon Translate

Lecture 3: Practical:Amazon Transcribe

Lecture 4: Practical:Amazon Translate

Chapter 14: ML(Amazon SageMaker and AWS DeepLens)

Lecture 1: Amazon SageMaker

Lecture 2: AWS DeepLens

Chapter 15: 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

Chapter 16: Last Words

Lecture 1: Bonus Lecture

Instructors

Pranjal Srivastava
Docker | Kubernetes | AWS | Azure | ML | Linux | Python

Rating Distribution

1 stars: 6 votes

2 stars: 5 votes

3 stars: 2 votes

4 stars: 5 votes

5 stars: 22 votes

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