AI - ML Engineer
Create a free account to apply in seconds
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
Learn Complete AI & ML Skillsets
Build strong foundations in Python, data preprocessing, ML algorithms, deep learning, and model deployment — all in one internship.
Build Real AI/ML Projects
Work on practical projects such as prediction models, neural networks, image classification, NLP-based tools, and end-to-end ML pipelines.
Mentorship from Industry AI Professionals
Learn directly from experienced ML engineers and data scientists working on production-grade AI systems.
Beginner-Friendly with Progressive Advancement
Start with the basics and move toward advanced ML & DL concepts, making it suitable for students from all engineering branches.
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