Certificate Course in
Machine Learning on AWS Cloud Training
- 24 Hours Classroom & Online Sessions
- 80 Hours Assignments & Real-Time Projects
- Aligned with AWS Certified Machine Learning
- Complimentary Python Programming
- Complimentary Machine Learning Primer
Academic Partners & International Accreditations
"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 Course Fee in India
INR 40,460 28,320/-
AWS Machine Learning Training 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 Sagemaker 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 algorithms, modern computing power and simple statistical methods. 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.
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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
Each module encapsulates the essential tools to impart machine learning skills using the two most popular programming languages Python and R. You will learn how to establish a gateway between different databases with R and Python and also how to connect to external sources of data. One of the major modules of Data Science is Machine Learning. Learn about the various modules that make up Machine Learning using the two most popular tools R and Python. As a data scientist one will be engaged in a multitude of data mining techniques in both supervised and unsupervised learning. One of the major variants of the same is reinforcement learning that enables machines to learn through rewards. Get introduced to all the supervised techniques of prediction and classification; learn about the major unsupervised learning methods and the application of reinforcement learning in Data Mining.
Learn two of the most powerful programming languages used in Data Analytics. Both R and Python are the top two tools used by Data Analytics professionals world over. Start learning from the very basics, right from installation and work your way up through simple commands, writing small functions and programs.
Both R and Python can connect to a wide variety of data sources. Under this module, learn how to establish a gateway between different databases with R and Python. Also, learn how to connect to external sources of data.
One of the major modules of Data Science is Machine Learning. Learn about the various modules that make up Machine Learning using the two most popular tools R and Python. Get introduced to the broad overview of ML and the various quality metrics with the help of R and Python.
In the real world, oftentimes, the datasets cannot be used as such and some amount of preprocessing activity needs to be done. Imbalance in the output classes is one of the common problems where sometimes the proportion may be as lopsided as 95% to 5% or even higher. Learn about the various methods and algorithms to address this problem of imbalanced data sets.
One of the integral parts of learning Data Science and working on Analytics projects is the sound understanding of Statistical tools. In this module learn about the need to know statistical measures and their application in Data Science. Also, learn how to visualize the data in a concise form to derive various meaningful insights.
The essence of analytics is to be able to get the story from the data. And for the data to be able to truly be useful one needs to munge the raw data to make it legible. Using tools like R and Python, learn how to manipulate data from the raw form to make it ready for subsequent ML algorithms. The topic is all the more important in the current context given that a lot of data is moving to the unstructured format.
Both R and Python while being classified under the object-oriented programming languages category, still require some traditional approach to programming whereby the user-defined function needs to be spelled out and the use of conventional program snippets is of utmost importance. In this module, learn how to create simple to complex user-defined functions and hone your programming skills in the context of machine learning.
As a data scientist one will be engaged in a multitude of data mining techniques in both supervised and unsupervised learning. One of the major variants of the same is reinforcement learning that enables machines to learn through rewards. Under this module, get introduced to all the supervised techniques of prediction and classification; learn about the major unsupervised learning methods and the application of reinforcement learning in Data Mining.
Under supervised learning one of the most popular methods of predicting numeric data is linear regression and for classifying categorical data is logistic regression. These two methods will be covered in detail under this module. And the participant will be introduced to multiple examples using R and Python.
Under the classification modeling, decision trees method has a special place even though they are not truly classification modeling techniques but rule-based algorithms. The popularity of Decision trees is in its simplicity, high accuracy, and most important the ability to explain behind- the- scenes working of the algorithm. Under this module, the participant will be introduced to working with Decision trees using R and Python.
Trends in Machine Learning on AWS Cloud
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 80+ 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
Offline Hiring Events
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