Certificate Course in
Machine Learning on AWS Cloud
- Get Trained by Trainers from ISB, IIT & IIM
- 24 Hours of Intensive Classroom & Online Sessions
- 60+ Hours of Practical Assignments
- 2 Capstone Live Projects
- AWS Certified Speciality Machine Learning Certification
- 100% Job Placement Assistance
"Companies that adopt cloud services experience a 20.5% average improvement in time to market. 80% of all enterprises will move to the cloud by 2025." - (Source). Amazon Web Services is a cloud service platform that offers flexibility and scalability to deploy services and manage data for organizations of all sizes. AWS provides the broadest and deepest set of machine learning services that fit your business needs and help unlock new insights and value. It also provides visualization tools and services that help developers build, train, and deploy machine learning models without having to learn complex machine learning algorithms and technology. In this course, learn to use AWS Machine Learning tools and services to make smart business decisions.
ML on AWS Cloud
- Computer Skills.
- Basic Mathematical Knowledge.
- Basic Data Science Concepts.
AWS Machine Learning Programme Overview
Learn to use the AWS Cloud platform to scale your business growth. Employ AWS EC2, AWS S3, and AWS RDS to seamlessly store and transfer organization data to and from AWS Cloud. Build, train, and deploy AWS Deep Learning models with Machine Learning on AWS Cloud. This program begins with an introduction to cloud computing and the evolution of Amazon Web Services(AWS). The rudiments of Elastic Cloud Compute (EC2), features of EC2, and types of instances of AWS EC2 are imparted to the student. Data Storage with Simple Storage Services (S3), concepts of creating S3 bucket, storage classes, versioning, static website hosting, and cross-region replication of data through S3 are elaborated in detail. Learn about AWS Relational Database Service (RDS), deploying RDS instances, and much more. Apprehend Machine Learning using Amazon Sage maker and NLP and Text Mining using Amazon Comprehend. Build Prediction Models using Machine Learning Services.
Amazon Machine Learning allows a developer to discover hidden patterns in the data through algorithms, construct models, and implement predictive applications based on these patterns. AWS allows developers to build models according to the specified needs of the organization and helps make better business decisions. These models make a prediction based on probability and allow us to test thousands of potential product designs, improve health care outcomes, and enhance customer service responses. AWS provides many benefits like Security, where data is encrypted to provide end-to-end security. Flexibility, where developers can select the operating system language and database. Usability, where it quickly deploys applications, builds new apps and migrates existing ones. Last but least Scalability, where developers can scale up or down as needed.
Machine Learning on AWS Learning Outcomes
Machine Learning is about making predictions using simple statistical methods, algorithms, and modern computing power. AWS is designed to securely host your applications and enables you to select the operating system, programming language, and other services you need and pay only for the computing power, storage, and services you use. With Amazon ML one can build data from large data sets, make predictions that are used to solve real-time problems. This course introduces you to the Machine Learning concepts and terminologies, how to create and use machine learning models, how to evaluate that model's performance, and what problems can machine learning solve. Students will learn to build, train, tune, and deploy ML models using the AWS Cloud. Using the Machine Learning web service offered by Amazon you will learn to work with data sources and generate accurate predictions. Explore real-world use cases with Machine Learning (ML) and using Amazon Sage Maker which enables Data Scientists and developers to easily deploy your ML use cases and removes the complexity from each step of the ML workflow also discover common neural network frameworks with Amazon Sage Maker.
Block Your Time
Who Should Sign Up?
- Data scientists, technology heads, decision-makers.
- Professionals with analytics knowledge.
- Professionals with industry domain experience in various areas (banking, finance, insurance, mechanical, IoT etc.).
Machine Learning on Cloud Modules
AWS Machine Learning algorithm quickly helps to build smart applications that are used to detect fraud, predict demand, and synchronizes the previous data to provide vital information to the user. The module on Machine Learning on AWS Cloud fulfills the objective of getting familiar with Amazon services and machine learning. Each of the modules will take you through several ML concepts, AWS services, and the challenges Machine Learning can address and ultimately help solve. The first module introduces you to cloud computing and its advantages and then you will be given a brief introduction to AWS and its features like storage, security, flexibility, and scalability. You will also learn how to make use of Amazon Sage Maker which is used to easily integrate Machine Learning into your applications.
Introduction to Cloud Computing and its concepts. Understand various advantages of Cloud Computing, the various Cloud deployment Models, and the various Service Models.
Introduction to Amazon Web Services, its history, AWS milestones, Amazon Web Services standing in the Cloud market, AWS Global Infrastructure, Regions, Availability Zones, and Edge Locations.
Introduction to EC2 and its services. Creating an EC2 Instance. Classification of EC2 Instances based on Configuration, Performance, and Memory. Learn about On-Demand Instances, Reserved Instances, Scheduled Instances, and Spot Instances. In this module, you will study the use of Load Balancers, Elastic Block Storage - Volumes and Snapshots, dealing with AMI and creating Custom AMI.
Introduction to various types of Storage Services - EBS, S3 and EFS, differences between these storage services. In this module, you will learn to use S3 services for Machine Learning. You will learn about the properties of S3 and storage classes in S3.
AWS best practices in securing your Amazon Web Services Account, controlling other Users, Groups, in AWS Account through AWS Policies. Study the importance of the IAM Role. Learn to create Custom Policies and enable Multi-Factor Authentication.
Studying Amazon RDS Services, creating an RDS Instance and its characteristics, creating MYSQL RDS service and establishing a connection with Remote EC2 Instance.
Introduction to Machine Learning on Cloud, Amazon SageMaker and its characteristics, various services under Amazon SageMaker, creating a Notebook Instance, and launching Jupyter Notebook.
Introduction to Text Mining and Natural Language Processing, Uploading the extracted data in Amazon Comprehend and performing Sentiment Analysis.
Introduction to Amazon Machine Learning Service. Learn how Machine Learning Service builds a Machine Learning Model based on the inputs provided.
Machine Learning On AWS Cloud Trends in Malaysia
Machine Learning has accelerated innovation and unlocked new possibilities in healthcare, customer service, fraud detection, etc. It has provided insights into making more accurate predictions, enabled new efficiencies, and that is why many customers choose to use AWS for Machine Learning. The global cloud computing market is forecast to go up to $700 by 2025 and as many as 85% of enterprises will be running on a multi-cloud strategy. Machine Learning is among the top trends of AWS. Developers create machine learning models by using large datasets and certain algorithms for which they need the Cloud. 50% of enterprises have decided to use public cloud storage for high computing performance. Private and Hybrid cloud computing is the future of cloud and 85% of the business leaders will move to it for storage, computing, and data analysis.
Whether it is predicting repeat purchases of customers, facilitating new product development, or creating real-time recommendations, Machine Learning Technologies are accelerating to innovate faster for customers. Mobile cloud computing will also catch up to reach 120.70 billion by the end of 2025. It’s a platform that combines to create a new infrastructure using mobile devices and cloud computing to bring rich computational resources to mobile users to create, organize files, folders, music, and photos to cloud computing models. Its features like redundancy, stability, and security contribute to the popularity of the cloud. It avoids the problem of buying and maintaining hardware and offers the facility to access content from basically anywhere.
How We Prepare You
Additional Assignments of over 60+ hours
Live Free Webinars
Resume and LinkedIn Review Sessions
Lifetime LMS Access
Job Placements in Machine Learning on AWS Fields
Unlimited Mock Interview and Quiz Session
Hands-on Experience in Live Projects
Life Time Free Access to Industry Webinars
Call us Today!
Cloud Machine Learning Panel of Coaches
Bharani Kumar Depuru
- Areas of expertise: Data Analytics, Digital Transformation, Industrial Revolution 4.0.
- Over 14+ years of professional experience.
- Trained over 2,500 professionals from eight countries.
- Corporate clients include Hewlett Packard Enterprise, Computer Science Corporation, Akamai, IBS Software, Litmus7, Personiv, Ebreeze, Alshaya, Synchrony Financials, Deloitte.
- Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, AgilePM (DSDM Atern).
- Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business.
Sharat Chandra Kumar
- Areas of expertise: Data Science, Machine Learning, Business Intelligence and Data Visualisation.
- Trained over 1,500 professional across 12 countries.
- Worked as a Data Scientist for 14+ years across several industry domains.
- Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology, Infrastructure Library.
- Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, R, RStudio, Python, Tableau, Cognos.
- Corporate clients include DuPont, All-Scripts, Girnarsoft (College-dekho, Car-dekho) and many more.
- Areas of expertise: Data Science, Machine Learning, Business Intelligence and Data Visualisation.
- Over 20+ years of industry experience in Data Science and Business Intelligence.
- Trained professionals from Fortune 500 companies and students from prestigious colleges.
- Experienced in Cognos, Tableau, Big Data, NoSQL, NewSQL.
- Corporate clients include Time Inc., Hewlett Packard Enterprise, Dell, Metric Fox (Champions Group), TCS and many more.
Get validation of your advanced skills and knowledge with the Machine Learning on AWS Cloud certificate. Join the growing community of developers and data scientists trained on AWS.
FAQs on Cloud Machine Learning Certificate
The easiest way to understand the relationship between AI and deep learning is to visualise them as concentric circles. In that, AI is an idea that came first in which machines exhibit human intelligence. Machine learning came next, which is an approach to achieve AI. And finally, deep learning, which is driving today’s AI explosion, is a technique to implement machine learning.
The skills that you will obtain from our Deep Learning course are: You will be able to build AI systems using Deep Learning You will be familiar with deep learning and image processing convolution networks. You will be introduced to text and emotion mining and Natural Language Processing (NLP). You will learn how to build, train and deploy AWS Deep Learning models with Machine Learning on AWS Cloud.
While salaries of AI experts go up to US$ 1 million a year, it could start from US$ 150,000, on an average.
The salary range varies based on experience, industry, domain, geography and various other parameters. However, as a general rule of thumb, we can go with research conducted by job portals. On average, in Malaysia, machine learning professionals draw salaries of RM 165,074 per annum. Refer To
On average, data scientist salaries are around RM 102,000 per annum. Refer To
If you miss a class, we will arrange for a recording of the session. You can then access it through the online Learning Management System.
We assign mentors to each student in this programme. Additionally, during the mentorship session, if the mentor feels that you require additional assistance, you may be referred to another mentor or trainer.
Jobs in the Field of Machine Learning On AWS Cloud in Malaysia
The increasing demand for Machine learning on AWS has given rise to several high paying jobs like Special Solution Architect AI/ML, Machine Learning Engineer, Cloud Developer, AWS Solutions Architect, DevOps Engineer, etc.
Salaries in Malaysia for Machine Learning On AWS Cloud
In Malaysia, the average salary for Machine Learning On AWS Cloud at the entry-level will be RM 40,800, at mid-level RM 73,720, and for experienced RM 97k. It varies with experience and job roles.
Machine Learning On AWS Cloud Projects in Malaysia
You can work on various projects using Amazon Web Services like Hosting an application on a website, building a secure online store, designing a database for a mobile app, creating an audio transcript, and deploying a Python web application to name a few.
Role of Open Source Tools in Machine Learning On AWS Cloud
With Amazon Machine Learning you can build and train predictive models, host an application, design a database for a mobile app, or identify potential customers for a marketing campaign. In this course, we will learn to customize machine learning algorithms using TensorFlow, and PyTorch.
Modes of Training in Machine Learning On AWS Cloud
The course in Malaysia is designed to suit the needs of students as well as working professionals. We at 360DigiTMG give our students the option of both classroom and online learning. We also support e-learning as part of our curriculum.
Industry Applications of Machine Learning On AWS Cloud in Malaysia
Machine Learning applications are used in fraud detection, self- driving cars, virtual personal assistants, product recommendations, traffic alerts, etc.