Certificate Program in
Machine Learning on AWS SageMaker
- 24 Hours Classroom & Online Sessions
- 80 Hours Assignments & Real-Time Projects
- Complementary Machine Leaning Concepts
- Complementary Python Programming
- Complimentary DevOps for Beginners
- Aligned with AWS Certified Machine Learning Speciality
Academic Partners & International Accreditations
Machine learning (ML) is widely emerging creating ample opportunities in the market. As per the reports of The World Economic Forum, the growth of Artificial intelligence (AI) could create 57 million new jobs in the coming years, but there are only 300,000 Machine Learning and AI engineers. So there is a lack of ML talent in the job market. This implies that there is a need and demand for professionals who have substantial knowledge of ML on cloud applications. This is the right time for the job aspirants to develop the required ML skills and advance their career.
ML on Cloud
INR 40,460 28,320/-
ML on AWS SageMaker Course Overview
360DigiTMG delivers the best training in Machine Learning on AWS. This course focuses on the basics of AWS Machine Learning. This course emphasizes the key concepts that include Natural Language Processing, Cloud computing, Data preprocessing, and building models. Professionals will also be introduced to advanced tools like TensorFlow, SCikit, Apache, etc, and their applications. ML on AWS training program is designed exclusively for the professionals who want to achieve a top position in the data-driven world. This course enhances the required skills and helps professionals to accomplish their goals.
ML on AWS SageMaker Learning Outcomes
ML on AWS training program is formulated to introduce the key concepts of Machine Learning and how the Machine Learning models are trained and deployed with algorithms. This program is designed to address the problems and generate innovative solutions. From this training program, students will learn the applications of SageMaker, and be able to build models. Will learn the important tools and techniques and learn the concepts like Scaling the Model, Managing Cost & Automatic Tuning. The main objective of the ML on AWS training program is to introduce the key concepts of ML and make professionals the future-ready workforce.
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Who Should Sign Up?
- IT Engineers
- Data and Analytics Manager
- Business Analysts
- Data scientists, technology heads, decision-makers
- Professionals with analytics knowledge
- Professionals with industry domain experience in various areas (banking, finance, insurance, mechanical, IoT etc.)
Modules for ML on AWS SageMaker Course
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 ML on AWS
Amazon web services are the most used Cloud provider in the whole world. Data Scientists are required to have knowledge of cloud services as they are supposed to build, train, and deploy Machine Learning models on the cloud. Let us know the latest trends or advantages of AWS in Machine Learning. You can access it with just a click of a button from 2 core and 4GB Machines to 96 core and 768GB RAM machines and notebooks. Interestingly, no IP addresses, or security groups, or AMI’s are needed to manage the machines. We can link Github accounts to these notebooks, it is used during building the models, furthermore, downloading, and uploading of files is not required. AWS Sagemaker has an excellent feature called Hyperparameter tuning which tunes the model overnight. Another trend is that Amazon’s pre-built models have been highly optimized that can run on AWS services.
As these models are prebuilt, it is not required to build, deploy, and check the models. The prebuild model XGBoost is quite effective which works with Python SDK. SageMaker Autopilot is the industry’s foremost automated machine learning capability that gives you complete control and visibility into your ML models. It automatically identifies the raw data, applies feature processors, trains multiple models, it notifies the performance and ranks the models based on their performance with just a few clicks. This SageMaker autopilot can be easily used by people who don’t have knowledge of Machine Learning and they can build and deploy models. Sagemaker Studio is a new innovation which has many interesting features.
How We Prepare You
Additional Assignments of over 80+ hours
Live Free Webinars
Resume and LinkedIn Review Sessions
Lifetime LMS Access
Job Placements in Data Science Fields
Unlimited Mock Interview and Quiz Session
Hands-on Experience in Live Projects
Life Time Free Access to Industry Webinars
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