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Key Highlights

  • Learning Mode: Blended – Online Instructor-Led + Self-Paced + Virtual Labs
  • Credits: 12 Academic Credits
  • Comprehensive AI Coverage – From ML fundamentals to advanced AI deployment.
  • 110 + Hours of Training, including 60 hours live, 40 hours self-paced, and 10 hours project mentoring.
  • 3 Real-World Projects across NLP, computer vision, and predictive analytics.
  • Access to AI Development Labs with Python, TensorFlow, PyTorch, and cloud ML tools.
  • Placement-Driven Learning with 80% alumni securing roles in AI engineering and data science.

Course Highlights

  • Program Duration: 10 Months
  • Number of Projects: 6 Applied Projects + 1 Capstone
  • Live Sessions: 160 Hours (Instructor-Led)
  • Self-Paced Learning: 80 Hours of structured assignments
  • Credit Load: 18 Academic Credits
  • Mode of Learning: Online ILT + Virtual Labs (Hybrid Optional)
  • Language of Instruction: English

About Program

Duration and Learning Hours

The program is designed to be completed in 12 months (220 days), offering an in-depth curriculum through a balance of live training and structured self-learning.

  • Modules: 7 comprehensive modules covering advanced technical and industry-relevant skills.

  • Live Instructor-Led Sessions: 168 hours of interactive, expert-guided learning.

  • Self-Paced Learning: 100 hours of assignments, lab practice, and curated study materials.

  • Mode of Delivery: Online learning with optional hybrid mode integrating physical labs for hands-on experience.

Project-Based Learning

  • Lab Access: Physical and virtual lab environments included for immersive skill-building.

  • Capstone Project: Integrated project work to apply concepts in real-world contexts.

  • Internship: Available as part of industry-linked learning pathways.

Academic Load and Credits

Credits: 12 Academic Credits

Language of Instruction

All sessions, study materials, assessments, and learner interactions are conducted in English, ensuring professional clarity, global accessibility, and alignment with international learning standards.

 

Who Should Enroll?

This program is best suited for:

  • Engineering graduates from CS, IT, or Electronics
  • Working professionals in DBMS, SQL, or BI tools
  • Aspiring data engineers, pipeline developers, and tech analysts
  • Learners targeting AICTE/NSDC or Govt. project certifications

Course Curriculum

Modules designed to meet current industry standards.

01

Foundations of AI & ML – Concepts, history, and industry applications.

02

Data Engineering for AI – Cleaning, preprocessing, and feature selection.

03

Machine Learning Models – Supervised, unsupervised, and ensemble methods.

04

Natural Language Processing – Sentiment analysis, topic modeling, chatbot development.

05

Computer Vision Applications – Image classification, object detection, face recognition.

06

Capstone Project – AI system solving a real-world business or social problem.

What You’ll Learn

Essential Skills & Tools for Leading Projects in the Digital Age

Core Skills
  • AI and machine learning fundamentals
  • Data preprocessing & feature engineering
  • NLP model building & text analytics
  • Computer vision with deep learning models
  • AI model deployment on cloud services
Tools & Frameworks You’ll Master
  • Python, scikit-learn, TensorFlow, PyTorch
  • AWS Sagemaker, Google Vertex AI, Azure ML
  • NLTK, OpenCV, Hugging Face
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Executive Certification in Network Security & Ethical Hacking
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Need to know more?

Get to know the course in depth by downloading the course brochure
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Executive Certification in Network Security & Ethical Hacking
more

Need to know more?

Get to know the course in depth by downloading the course brochure
Download Brochure

Real People. Real Results​

Real stories of career growth, skill mastery, and success after MSM Grad programs.

30% Salary Hike

Priya M.

Associate Full-Stack Developer Senior QA

I wanted to be in charge of features from start to finish after years of working in manual and automated testing. It was feasible with sprint work because of the 10-month cadence and the 90-hour live sessions. Clean Java basics were promoted by mentors first, followed by REST and Spring Boot. I wired a simple Jenkins pipeline and used React + Spring Boot + MySQL to rebuild a small internal tool. Just clearer ownership and fewer regressions, nothing spectacular. HR screening was aided by the NASSCOM-validated curriculum; the talking was done by the capstone and repository.

40% Salary Hike

Karan S.

Manufacturing Java Developer

I was on a monolith from JSP/Servlets. At last, the sequence—Core Java → Hibernate → Spring Boot → CI/CD—fit into a contemporary workflow. Code reviews compelled me to abandon “quick fixes” in favor of appropriate layers and tests, and the Oracle/MySQL and environment configuration labs were useful. I separated out a service, containerized it, and set up a basic build using Maven + Jenkins, but we didn’t completely revamp our platform in a single day. Handovers are more transparent and deployments are more tranquil.

25% Salary Hike

Sana R.

BCA final-year student

Tutorial projects weren’t enough for me. I could create a CRUD application by the middle of the program using MongoDB for storage, Spring Boot APIs on the back, and React on the front. Git hygiene, meaningful commits, and a README that can be run by another person were important to the faculty. In addition to UI, my capstone demo concentrated on design decisions. Interviews now focus on trade-offs I made—validation, pagination, and auth—rather than just buzzwords, though I’m still learning.

25% Salary Hike

Vivek T.

IT Diploma → Future Full-Stack Java Developer

The combination of 70 hours of self-paced work I could complete after shifts and brief, targeted live classes made the blended format important.” After learning the fundamentals of HTML, CSS, and JS, I moved a small project from JSP/Servlets to Spring Boot and added basic tests and a continuous integration step. I can describe the entire pipeline and have deployed a demo to a cloud free tier. I left with a portfolio that I can continue to grow, but no promises of a job were made.

Real People. Real Results​

Real stories of career growth, skill mastery, and success after MSM Grad programs.

30% Salary Hike

Priya M.

Associate Full-Stack Developer Senior QA

I wanted to be in charge of features from start to finish after years of working in manual and automated testing. It was feasible with sprint work because of the 10-month cadence and the 90-hour live sessions. Clean Java basics were promoted by mentors first, followed by REST and Spring Boot. I wired a simple Jenkins pipeline and used React + Spring Boot + MySQL to rebuild a small internal tool. Just clearer ownership and fewer regressions, nothing spectacular. HR screening was aided by the NASSCOM-validated curriculum; the talking was done by the capstone and repository.

40% Salary Hike

Karan S.

Manufacturing Java Developer

I was on a monolith from JSP/Servlets. At last, the sequence—Core Java → Hibernate → Spring Boot → CI/CD—fit into a contemporary workflow. Code reviews compelled me to abandon “quick fixes” in favor of appropriate layers and tests, and the Oracle/MySQL and environment configuration labs were useful. I separated out a service, containerized it, and set up a basic build using Maven + Jenkins, but we didn’t completely revamp our platform in a single day. Handovers are more transparent and deployments are more tranquil.

25% Salary Hike

Sana R.

BCA final-year student

Tutorial projects weren’t enough for me. I could create a CRUD application by the middle of the program using MongoDB for storage, Spring Boot APIs on the back, and React on the front. Git hygiene, meaningful commits, and a README that can be run by another person were important to the faculty. In addition to UI, my capstone demo concentrated on design decisions. Interviews now focus on trade-offs I made—validation, pagination, and auth—rather than just buzzwords, though I’m still learning.

25% Salary Hike

Vivek T.

IT Diploma → Future Full-Stack Java Developer

The combination of 70 hours of self-paced work I could complete after shifts and brief, targeted live classes made the blended format important.” After learning the fundamentals of HTML, CSS, and JS, I moved a small project from JSP/Servlets to Spring Boot and added basic tests and a continuous integration step. I can describe the entire pipeline and have deployed a demo to a cloud free tier. I left with a portfolio that I can continue to grow, but no promises of a job were made.

Designed for Ambitious Professionals

Roles Offered
  • Data Engineer
  • Big Data Analyst
  • ETL Developer
  • Cloud Data Engineer
  • Data Platform Administrator
Salary Growth Potential

40% Average Hike
Post Course Completion

Top Salary Bracket

Entry Level: ₹8–12 LPA

Mid Level: ₹15–24 LPA

Top Hiring Companies

Designed for Ambitious Professionals

Roles Offered
  • Data Engineer
  • Big Data Analyst
  • ETL Developer
  • Cloud Data Engineer
  • Data Platform Administrator
Salary Growth Potential

40% Average Hike
Post Course Completion

Top Salary Bracket

Entry Level: ₹8–12 LPA

Mid Level: ₹15–24 LPA

Top Hiring Companies

You Asked, We Answered

Is this course beginner-friendly?

Yes, basic Python knowledge is recommended, but prior AI experience is not required.

Will I build AI models myself?

Yes, the program is entirely hands-on with guided labs and projects.

Does the course help in switching careers?

Absolutely—many alumni have moved from software engineering or analytics into AI-specific roles.

Ready to Deep Dive into the Future of AI?

Seats are limited.
Join the next cohort of advanced AI professionals.

Applications Open for May 2026

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