MachineLearning&AIEngineering:BuildRealAISystems
Learn to design, train, and deploy machine learning and AI models, and graduate ready for a career as a Machine Learning or AI Engineer.
CourseOverview
Target Audience
- Working Professionals
- Career Switchers
- Students
Prerequisites
- Basic Python Programming
- Basic Mathematics
Career Outcomes
- Machine Learning Engineer
- AI Engineer
- Data Scientist
- ML Ops Engineer
ProgramStructure
Our instructional format is built to translate academic concepts into production engineering. This course spans 6 Months of immersive, hybrid training.
What You Learn
- 14 Learning Modules
- 25+ Practice Quizzes
- 1 Capstone Portfolio Project
What You Build
- 9 Production Projects
- 40+ Hands-on Labs
- 18+ Core Case Studies
Job Assistance
- Guaranteed Internship Phase
- Industry-Recognized Certification
- Dedicated Placement Assistance
Live Help & Support
- 135+ Hours Interactive Lectures
- 1-on-1 Expert Mentorship
- 1:1 Mentor Support
LearningCurriculum
A high-octane roadmap spanning 8 Months. Master the stack through production-grade modules.
Python & Math Foundations for ML
Refresh the Python and mathematical foundations needed for machine learning.
Core Concepts
Build Target
Data Exploration Notebook on a Real Dataset
Questions? Chat on WhatsApp
Technologies&Software
Master the industry standard software ecosystem. Build deep expertise in production-tested developer tools.
Programming Languages
Python
FundamentalsBuild and train machine learning models.
ML Frameworks
TensorFlow
AdvancedDesign and train deep learning models at scale.
PyTorch
AdvancedBuild flexible neural network architectures for research and production.
Scikit-learn
IntermediateApply classical machine learning algorithms efficiently.
Cloud & Deployment
Docker
IntermediatePackage trained models for consistent deployment.
AWS SageMaker
AdvancedTrain and deploy ML models at scale in the cloud.
Developer Tools
Jupyter Notebook
FundamentalsPrototype and evaluate model experiments.
Git
FundamentalsVersion control model code and experiment configs.
AI Tools
Claude Code
AdvancedAccelerate model pipeline development with an AI coding agent.
PortfolioProjects
Build production-grade systems throughout this track. Every project is designed to mirror real business requirements and establish technical authority in your portfolio.
Image Classification with CNNs
Students train a convolutional neural network to classify images into categories, learning core deep learning workflows including data augmentation, training loops, and accuracy evaluation. This mirrors how real teams scope similar work, so students leave with both the artifact and a defensible explanation of their deep learning choices.
- Data Augmentation
- CNN Training
- Accuracy/Loss Curves
- Inference Script
Sentiment Analysis on Product Reviews
An NLP project classifying product reviews as positive, negative, or neutral, introducing text preprocessing, embeddings, and simple transformer-based classifiers. The project is designed to be extended afterward, giving students a natural talking point about text preprocessing during placement interviews.
- Text Preprocessing
- Fine-Tuned Classifier
- Confusion Matrix
- Batch Inference
ML Model Serving API
Students wrap a trained model in a production-style REST API with input validation, logging, and containerization, bridging the gap between notebooks and deployable services. By the end, students walk away with a working, documented build that clearly demonstrates model serving and api design to recruiters and interviewers.
- Prediction Endpoint
- Input Validation
- Request Logging
- Dockerized Service
Computer Vision Defect Detection System
A manufacturing-inspired project detecting product defects from images using transfer learning, giving students hands-on practice with real-world imbalanced and noisy visual data. The finished build is structured for a portfolio or GitHub profile, giving students a concrete example of transfer learning pipeline they can walk through in an interview.
- Transfer Learning Pipeline
- Defect Localization
- Augmented Training Set
- Evaluation Report
End-to-End MLOps Pipeline for Model Retraining
Students build an automated pipeline that retrains, evaluates, and deploys a model when new data arrives, using experiment tracking and versioning tools used in real ML teams. Students finish with a polished, presentable deliverable and a clear narrative of the decisions behind experiment tracking for future employers to evaluate.
- Automated Retraining
- Experiment Tracking
- Model Versioning
- CI/CD Pipeline
Conversational AI Support Assistant
An LLM-powered support assistant that answers product questions using a knowledge base, combining prompt design, retrieval, and safety guardrails into a deployable chat experience. This mirrors how real teams scope similar work, so students leave with both the artifact and a defensible explanation of their llm integration choices.
- Knowledge Base Retrieval
- Conversational Memory
- Guardrails
- Chat API
Real-Time Fraud Detection with Streaming Data
Students build a near-real-time fraud scoring system that consumes streaming transactions, scores them with a deployed model, and flags anomalies for review, mirroring fintech production systems. The project is designed to be extended afterward, giving students a natural talking point about real-time inference during placement interviews.
- Stream Ingestion
- Real-Time Scoring
- Alerting Dashboard
- Model Monitoring
AI Engineering Capstone: Production ML Platform
A capstone platform combining model training, an MLOps pipeline, monitoring, and a user-facing application into one cohesive product, giving students a complete, interview-ready AI engineering showcase. By the end, students walk away with a working, documented build that clearly demonstrates system design and mlops to recruiters and interviewers.
- Training Pipeline
- Model Registry
- Monitoring Dashboard
- Production API
- Kubernetes Deployment
CareerOutcomes
Engineer Intelligent Systems as an AI/ML Engineer. With AI adoption accelerating across industries , companies need engineers who can move models from experimentation into reliable production systems. This course covers machine learning fundamentals, model deployment, and integration practices, preparing learners for engineering roles that sit at the intersection of software development and applied AI, one of the fastest-growing segments of the tech job market.
Target Job Roles
Build and train machine learning models for real-world applications.
Salary Outlook: ₹6–10 LPA
Deploy and integrate ML and generative AI models into production systems.
Salary Outlook: ₹10–18 LPA
Optimize model performance and lead AI infrastructure decisions.
Salary Outlook: ₹18–28 LPA
Define enterprise AI strategy and oversee model governance and architecture.
Salary Outlook: ₹30–45 LPA
In-Demand Recruiter Skills
Industry Credentials
- AWS Machine Learning Specialty
- TensorFlow Developer Certificate
Recruitment Network
FrequentlyAskedQuestions
Have questions about this program? Review our comprehensive breakdown of curriculum, requirements, and logistics.
Still have questions?
If you cannot find the answer to your query in our FAQ database, our academic advisors are available for direct consultations.
Who should take this Machine Learning & AI Engineering course?
This course is designed for developers, data professionals, and graduates who want to specialize in building and deploying machine learning and AI systems, rather than just analyzing data. Whether you're starting fresh or upskilling from an adjacent role, the structure is built to meet you where you currently are.
Do I need prior machine learning experience to join?
Basic Python knowledge is recommended, but prior machine learning experience isn't required. The course builds from foundational ML concepts up to advanced model deployment and AI engineering practices. You can always revisit recorded sessions if a particular fundamental needs extra practice before you move ahead.
Is this course suitable for someone new to AI?
Yes, as long as you're comfortable with basic programming logic, the course guides you through machine learning fundamentals before progressing into more advanced deep learning and deployment topics. We've seen many students with no prior technical background complete this course successfully with consistent weekly effort.
Will I receive a certificate after completing the course?
Yes, you'll receive a certificate after completing all modules, assignments, and your capstone project, which demonstrates your ability to build and deploy machine learning models. This gives you tangible proof of skill that you can reference during technical interviews and portfolio reviews.
Is placement support part of this program?
Yes, placement support includes resume guidance, technical interview preparation focused on ML concepts, and access to hiring partners looking for machine learning and AI engineering talent. Outcomes vary by individual effort, but the structured support meaningfully improves your chances compared to job hunting alone.
Are hands-on projects included in the curriculum?
Yes, you'll build multiple ML models across different problem types, including classification and prediction tasks, before working on a capstone project that involves deploying a model as a usable application. These projects are designed to closely resemble what you'd actually be asked to build in a junior role.
Is there an internship opportunity available?
Yes, eligible students can join an internship track where you apply machine learning skills to realistic problem statements under mentor guidance, adding practical experience to your resume. This experience often becomes a key talking point in interviews when discussing practical, applied skills.
Can working professionals manage this course alongside a job?
Yes, sessions are scheduled for evenings and weekends with recordings available, and the project-driven structure allows you to practice concepts on your own time as needed. You can also reach out to mentors outside live sessions if you get stuck while studying independently.
Which tools and frameworks will I learn?
You'll work with Python, scikit-learn, and deep learning frameworks like TensorFlow or PyTorch, along with model deployment tools, giving you a practical toolkit used across real AI engineering roles. This reflects current industry practice, so the skills you build stay directly relevant to what employers are actually looking for.
Will I learn how to deploy machine learning models?
Yes, model deployment is a key focus of this course. You'll learn to package models into APIs and deploy them so they can be used in real applications, not just notebooks. We keep this part of the curriculum updated regularly to match how the technology is actually used in the field today.
Does the course cover deep learning and neural networks?
Yes, you'll learn the fundamentals of neural networks and deep learning, including how they're applied to tasks like image and text processing, building on your classical machine learning foundation. This is one of the areas where hands-on practice makes the biggest difference in how confident you feel afterward.
Is generative AI or LLM-related content included?
Yes, given how central generative AI has become by 2026, the course includes an introduction to working with large language models and integrating AI capabilities into applications. Understanding this well will also make it easier to pick up related tools and concepts down the line.