5 Courses - Master AWS, Analytics, Machine Learning, Bigdata

Cloud Compute, Database, Security, Containers, Migration, data science, Database, Kinesis, Glue, Hadoop, Spark

Created byKaushik Vadali

Last updated 4/2020

English

What you'll learn

Underastand Amazon Web Services, Migrate to Amazon Cloud, Machine learning and Data science

Course content

5 sections • 68 lectures • 12h 11m total length

Embrace Cloud computing12:03
AWS Products and Solutions6:27
AWS-Pricing - Simple Monthly calculator - TCO Calculator17:24
AWS Compute products and services9:21
Learn Amazon Web Services compute products and services, including scalable instances, elastic containers, and Lambda, with auto scaling, pay-for-what-you-use pricing, and elastic load balancing for reliable deployments.
Amazon Elastic Compute Cloud EC211:11
Amazon EC2 - Dashboard15:55
Amazon EC2 - Launch Linux VM18:13
Amazon EC2 - Access Linux VM12:42
Launch and configure an Amazon EC2 Linux VM, set storage and security group, create a key pair, and connect via SSH or PuTTY using the public DNS.
Amazon EC2 - Launch and access Windows server VM18:27
Amazon EC2 - Launch Wordpress website9:33
Launch a WordPress website on Amazon EC2 using a pre-configured image from the marketplace, configure security groups, and access the WordPress admin to customize and publish content.
AWS Elastic Beanstalk11:03
Master AWS Elastic Beanstalk to deploy and scale applications with automatic capacity provisioning and auto scaling. Create environments, upload applications, monitor via the dashboard, and pay only for resources used.
Amazon EC2 Autoscaling12:35
Elastic Load Balancing (ELB)10:36
Explore elastic load balancing to distribute traffic across healthy instances, achieving high availability, security, health checks, and automatic scaling with application, network, and classic load balancers.
Amazon Lightsail5:44
AWS Lamda12:11
AWS ECS using Fargate15:13
Explore AWS ECS using Fargate to run and scale containers without managing servers, utilizing task definitions, services, and load balancing for secure, scalable deployments.
Storage - S3, EBS, EFS, Glacier, Snow Family, Gateway18:00
Discover AWS storage services—S3, EBS, EFS, Glacier, Snow Family, and AWS Storage Gateway—and how they deliver reliable, scalable, secure cloud storage with lifecycle migration and disaster recovery.
Storage S3 - create bucket, store retrieve files11:45
Master creating S3 buckets, uploading and retrieving files, and managing permissions, versioning, and lifecycle policies for scalable, secure backups and static hosting.
Access S3 through AWS - CLI with IAM user credentials10:04
Route 53, Domain name, DNS8:10
Explore how Route 53 handles domain names and DNS to route traffic, perform health checks, and monitor availability for content delivery and production services.
AWS Databases7:48
AWS - RDS MySQL - Create, connect and operate12:28
Amazon Dynamo DB10:24
Migration Hub14:10
Security, Identity, & Compliance15:32
Explore how security, identity, and compliance are managed across organizations with policy-based controls, centralized access to accounts, and policies governing users and applications.
AWS Step Functions18:09
Explore how AWS Step Functions state machines orchestrate Lambda functions into resilient workflows, cover state transitions, branching, timeouts, pricing, and IAM roles for secure deployment.
Amazon CloudWatch monitoring and management service12:54

AWS Analytics and Data Lakes10:47
Amazon Athena - Interactive query service7:10
Amazon Athena enables interactive query service to analyze data directly in storage with ad hoc queries and fast results, paying only for the queries you run, without complex extract-transform-load steps.
Amazon CloudSearch - Managed search service16:20
Amazon Elasticsearch Service11:36
Amazon Kinesis - Data Streams - Visualizing Web Traffic Using Amazon Kinesis Dat23:56
Explore Amazon Kinesis data streams to collect, process, and visualize real-time web traffic for insights. Build stream processing applications with producers and consumers, using analytics, Firehose, and downstream data stores.
Amazon Kinesis - Data Streams using AWS CLI8:40
Learn to set up Amazon Kinesis data streams using the AWS CLI and console. Create streams, configure access, monitor data, and decode streamed content to understand real-time processing.
Amazon Redshift - Data warehousing23:46
Amazon QuickSight - Business Analytics Intelligence Service14:51
Amazon Data Pipeline - Automate data movement18:36
AWS Glue – Managed ETL Service22:26
Explore how AWS Glue provides a managed, serverless ETL service that extracts and transforms data using crawlers, a data catalog, and pay-as-you-go pricing.

1.1 Big data introduction5:33
1.2 Big data history5:33
1.3 Big data technologies
Explore how the big data ecosystem blends analytics technologies to enable better decision making. See how big data powers IT operations, IoT, and industries with data storage and real-time insights.
1.4 Big data characteristics5:33
1.5 Big data Applications5:46
1.6 Data Lake4:43
Explore how a data lake acts as a centralized, scalable store for structured and unstructured data, enabling analytics, visualization, and machine learning across platforms using Hadoop and Spark.
1.7 Data science and Data scientist7:39
Explore data science as an interdisciplinary field uniting statistics, data analysis, and machine learning to extract insights from structured and unstructured data.
2.1 - Hadoop introduction6:17
Discover how Hadoop, an open source framework, stores and processes big data across distributed clusters with core components like HDFS, MapReduce, and YARN.
2.2 - HDFS-Overview6:20
Discover hdfs, the open source hadoop distributed file system inspired by Google file system, designed for high-throughput access to large data sets on commodity hardware with replication and security.
2.3 - Hadoop Architecture8:07
2.3a - Hadoop Architecture - assumptions and goals3:34
2.4 - Demo-Hadoop install - sw download verify integrity5:23
2.5 - Demo-Hadoop install - Java ssh configure5:56
Install and configure Hadoop and Java, set up SSH keys for passwordless login, and run in standalone and pseudo-distributed modes, then access DFS via browser and troubleshoot.
2.6 Demo hadoop access by browser6:35

3.1 Machine learning introduction7:11
Discover how machine learning enables computers to learn from data and build predictive models. Explore supervised and unsupervised learning, algorithms, and real-world applications like spam filtering and handwriting recognition.
3.2 Machine learning algorithms10:25
3.3 Machine learning softwares14:43
Explore machine learning softwares and open source tools, featuring Microsoft Cognitive Toolkit CNTK and deep learning frameworks, with Apache incubating projects and cloud platforms enabling scalable training and deployment.

5.1 AWS and Machine learning7:33
Explore AWS on-demand cloud computing, analytics, and ML services. Learn to train and deploy models with SageMaker and use EMR, Athena, and Redshift for analytics.
5.2 Bigdata and AWS8:30
5.3 Hadoop on Amazon Elastic Map Reduce - EMR8:30
5.4 Amazon Elastic MapReduce -EMR11:05
discover how amazon emr simplifies processing large data sets with hadoop and spark. learn about cluster lifecycles, master and task nodes, and steps that run on demand.
5.5 Amazon EMR Architecutre4:19
Explore the Amazon EMR architecture across storage, resource management, and data processing. See how EMRFS enables S3 as a file system, and how YARN, MapReduce, and Spark run on EMR.
5.6 Amazon EMR benefits7:44
5.7 Launch Amazon EMR cluster9:30
5.8 TensorFlow - Open source Machine Learning framework16:15
Explore TensorFlow, the open source data-flow machine learning framework, and learn how it powers neural networks across research and production, with hands-on setup via Google notebooks and Jupyter across platforms.
5.9 Amazon SageMaker - TensorFlow Part 114:28
Explore Amazon SageMaker as a fully managed platform to train, tune, and deploy machine learning models using TensorFlow, with built-in notebook instances, scalable hosting, and end-to-end workflow integration.
5.9 Amazon SageMaker - TensorFlow Part 214:29
5.10 AWS Deep Learning AMIs8:22
5.11 AWS Translate - Natual language translation4:23
Explore AWS Translate, a deep learning driven, pay-as-you-go natural language translation service that supports 21 languages, enabling real-time translations and easy integration for localization and sentiment analysis.
5.13 Amazon Polly - turn text to speech5:07
Explore Amazon Polly text-to-speech: natural-sounding voices across languages, real-time streaming, and lexicon customization with pay-per-character pricing for content creation.
5.14 Apache MXNet - Deep learning framework5:36

Requirements

Just basic computer knowledge to unleash the power of Cloud to rollout Awesome Amazon Web Services

Description

5 courses pack including below topics.

# Course Lectures Duration (hh:mm:ss)

1 AWS - Cloud Services 27 05:38:02

Elastic Beanstalk, ELB, ECS, EKS, Dynamo DB, Migration Hub

2 AWS - Data Analytics 10 02:38:08

AWS Analytics and Data Lakes, Amazon Athena - Interactive query service, Amazon CloudSearch - Managed search service, Amazon Elasticsearch Service, Amazon Kinesis - Data Streams, Amazon Redshift - Data warehousing

Amazon QuickSight - Business Analytics Intelligence Service, Amazon Data Pipeline - Automate data movement, AWS Glue – Managed ETL Service

3 BigData and Hadoop framework 14 01:20:13

Big data introduction, history, technologies, characteristics and Applications

Data Lake, Data science and Data scientist

Hadoop introduction, HDFS-Overview, Hadoop Architecture, assumptions and goals

Demo-Hadoop install - sw download verify integrity, Java ssh configure, Hadoop access by browser

4 Machine Learning 00:32:19

Introduction, Algorithms, Softwares

5 AWS Machine Learning 14 02:06:00

Bigdata and AWS, Hadoop on Amazon Elastic Map Reduce - EMR, Amazon EMR, Amazon EMR Architecutre, TensorFlow - Open source Machine Learning framework, Amazon SageMaker - TensorFlow Part 1 & 2, AWS Deep Learning AMIs, AWS Translate - Natual language translation, Amazon Polly - turn text to speech, Apache MXNet - Deep learning framework

TOTAL < 68 Lectures > 12hours 15min

Who this course is for:

For all those who is interested in learning about cloud Infrastructure, want to reap the benefits of migrating to AWS

5 Courses - Master AWS, Analytics, Machine Learning, Bigdata

What you'll learn

Explore related topics

Course content

AWS Cloud Services27 lectures • 5hr 38min

AWS Data Analytics10 lectures • 2hr 38min

Bigdata and Hadoop framework14 lectures • 1hr 17min

Machine Learning3 lectures • 32min

AWS Machine Learning14 lectures • 2hr 6min

Requirements

Description

Who this course is for: