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AI/ML

AI/ML Engineering Internship

Build and deploy machine learning models, work with neural networks, and create AI-powered applications with real-world datasets.

3+ weeks
Online · Bengaluru

Overview

Build an end-to-end ML pipeline from data collection to model deployment.

Outcomes

  • Deployed ML model in production
  • Deep learning project portfolio
  • Model optimization skills
  • MLOps fundamentals

Weekly plan

1ML Fundamentals Review

Supervised/unsupervised learning, model evaluation

2Feature Engineering

Data preprocessing, feature selection, dimensionality reduction

3Classical ML Algorithms

Linear models, tree-based methods, ensemble techniques

4Neural Network Basics

Perceptrons, backpropagation, activation functions

5Deep Learning with TensorFlow

Building and training neural networks

6CNNs and Computer Vision

Image classification, object detection basics

7NLP and Transformers

Text processing, sentiment analysis, LLM basics

8Model Optimization

Hyperparameter tuning, regularization, performance optimization

9MLOps Introduction

Model versioning, experiment tracking, CI/CD for ML

10Project Development

End-to-end ML project implementation

11Deployment

API development, cloud deployment, monitoring

12Final Showcase

Documentation, demo, portfolio packaging

Tools you'll use

PythonTensorFlowPyTorchJupyterGitDockerAWS/GCP

Eligibility

Students with Python basics and some ML fundamentals

This is a guided internship experience, not paid employment. Every InternAge programme combines industry-led training, a mentor-guided capstone project and a structured, online internship experience.

The programme fee covers training, mentorship, project guidance and delivery — it is not a fee for the internship or the certificate itself. The Internship Completion Certificate is awarded only after all programme requirements are met.

₹7,999
One-time fee
Apply now

Refund available if requested 48 hours before start. View policy

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Skills you'll gain

PythonTensorFlowPyTorchScikit-learnDeep LearningMLOps