AI - ML Engineer
Master Machine Learning, Deep Learning & AI model development — and become the engineer companies trust to build intelligent systems.
- Learn Complete AI & ML Skillsets
- Build Real AI/ML Projects
- Mentorship from Industry AI Professionals
- Beginner-Friendly with Progressive Advancement
About Program
The AI ML Engineer Internship is designed for final-year VTU students who want to build strong foundations in machine learning and artificial intelligence. Starting with Python programming, data cleaning, and exploratory data analysis, the program progresses into supervised and unsupervised ML, neural networks, image processing, NLP, and model deployment. Students learn to build complete ML pipelines, work with real datasets, and apply industry best practices. With hands-on mentorship and project-driven learning, learners build multiple AI/ML applications and graduate with a portfolio-ready skillset. This internship prepares students for roles in ML engineering, data science, analytics, and AI-powered software development.
Key Features
Program Content
Module 1 – Python Programming for AI & ML
Topics Covered:
- Variables, lists, dicts, loops & functions
- Exception handling
- Working with files
- Using NumPy for numerical computing
- Pandas for data manipulation
- DataFrames: merge, join, filter
- Python virtual environments
- Jupyter Notebook workflows
Module 2 - Math & Statistics Foundations for ML
Topics Covered:
- Linear algebra basics: vectors, matrices
- Probability distributions
- Descriptive statistics
- Hypothesis testing
- Correlation vs causation
- Normalization & standardization
- Cost functions: intuition & math
- Gradient descent basics
Module 3 - Data Preprocessing & Feature Engineering
Topics Covered:
- Handling missing values
- Outlier detection & removal
- Categorical encoding techniques
- Feature scaling
- Feature selection
- Train-test split & cross-validation
- Data balancing (SMOTE)
- Best practices for ML-ready data
Module 4 - Exploratory Data Analysis (EDA) & Visualization
Topics Covered:
- Data understanding & insights
- Visualizing distributions
- Pair plots, histograms, heatmaps
- Univariate vs multivariate analysis
- Using Matplotlib & Seaborn
- Correlation analysis
- Identifying patterns & anomalies
- Building EDA reports
Module 1 - Supervised Machine Learning Algorithms
Topics Covered:
- Linear & Logistic Regression
- Decision Trees & Random Forests
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Naive Bayes
- Model evaluation metrics
- Hyperparameter tuning
- Building end-to-end ML pipelines
Module 2 - Unsupervised Learning & Pattern Recognition
Topics Covered:
- K-Means clustering
- Hierarchical clustering
- Dimensionality reduction (PCA)
- Anomaly detection
- Association rules
- Customer segmentation (use case)
- Evaluating unsupervised models
- Visualization for cluster insights
Module 3 - Deep Learning & Neural Networks (DL Basics)
Topics Covered:
- Understanding neural network architecture
- Forward & backward propagation
- Activation functions
- Loss functions & optimizers
- Creating neural networks with TensorFlow/Keras
- Underfitting vs overfitting
- Model regularization
- DL experimentation workflows
Module 4 - Computer Vision (CV) Fundamentals
Topics Covered:
- Image processing basics
- Convolutional Neural Networks (CNNs)
- Pooling, padding, filters
- Image classification
- Data augmentation
- Transfer learning (VGG, ResNet)
- Building CV projects using Keras
- Model deployment
Module 5 - Natural Language Processing (NLP)
Topics Covered:
- Text preprocessing: tokenization, stemming, lemmatization
- Bag of Words & TF-IDF
- Sentiment analysis
- Intro to word embeddings
- Text classification use cases
- Building NLP models with Scikit-learn
- Deploying NLP models
- Real-world NLP workflows
Module 1 - Product Thinking for AI & ML Solutions
Topics Covered:
- Problem identification using ML
- Understanding business metrics
- Mapping data to product features
- Building user-centric AI solutions
- Figma for AI product UI
- Model lifecycle in products
- Documentation & ML requirements
Module 2 - Startup Innovation Using AI & ML
Topics Covered:
- AI product idea generation
- Competitive analysis
- Building MVPs for ML products
- AI monetization strategies
- Creating pitch decks
- Using AI tools for product research
- Preparing go-to-market (GTM) plans
Module 3 - Professional Development for ML Careers
Topics Covered:
- Resume building for AI/ML profiles
- LinkedIn optimization
- Writing ML case studies
- GitHub portfolio for ML projects
- Communication skills for data storytelling
- Interview preparation (ML + coding)
Mini Projects
- Sales prediction using Regression
- Iris dataset classifier
- Customer segmentation using K-means
- Sentiment analysis on social media data
Concepts Covered:
- EDA
- ML basics
- Clustering
- Classification
Intermediate Projects
- Image classification with CNN
- Spam detection (NLP)
- Credit card fraud detection
- Housing price prediction
Concepts Covered:
- Deep learning
- NLP
- Evaluation metrics
- Optimization
Capstone Projects
- End-to-End ML Pipeline & Deployment
- AI Customer Insights Platform
- NLP-powered Recommendation System
- Deep Learning Visual Recognition System
Concepts Covered:
- Architecture design
- Deployment
- Experimentation
- Documentation
Tools & Softwares
Why Choose This Internship
Salary Scale
Job Roles
- Machine Learning Engineer
- Data Scientist
- AI Engineer
- Data Analyst
- NLP Engineer
- Computer Vision Engineer
- ML Research Assistant
- Business / Data Analyst
- AI Product Engineer
- ML Pipeline Engineer
FAQ's
Yes. You'll receive VTU-compliant certificates and documentation.
No. The program starts from fundamentals and scales gradually.
Prediction models, CNN-based classifiers, NLP applications, and a full ML pipeline.
Yes — resume, LinkedIn, GitHub, mock interviews & job guidance.
Offered in both offline and hybrid formats.
Yes, you will receive a verified completion certificate from Rooman Technologies upon meeting all requirements.
CSE, ISE, AIML, ECE, EEE, Civil, Mechanical and all final-year VTU students.
Python, Scikit-learn, TensorFlow, Pandas, Kaggle workflows, cloud ML basics.
Contact Us
Have questions about our programs or need guidance? Reach out to us and we’ll be happy to help.
Email Us
online@rooman.net
Call Us
080 6945 1000
Send us a Message
Contact Us
Have questions about our programs or need guidance? Reach out to us and we’ll be happy to help.
Email Us
online@rooman.net
Call Us
080 6945 1000