Machine Learning Development Internship
1-6 months remote internship to build real Machine Learning projects with mentorship
About the Program
This internship focuses on applying machine learning techniques to solve real-world problems using data-driven approaches. Interns will gain hands-on experience in data preprocessing, feature engineering, model building, and evaluation. The program covers supervised and unsupervised learning, regression, classification, clustering, and model deployment using Python.
You'll work directly with senior developers, participate in code reviews, and build portfolio-worthy projects that solve actual business problems.
What You'll Do
Develop and train machine learning models using Python.
Build predictive systems for classification, regression, and clustering tasks.
Debug and optimize models to improve accuracy and reduce overfitting.
Learn to identify data quality issues and apply preprocessing techniques.
Integrate ML models into real-world applications via REST APIs.
Deploy trained models on cloud platforms for scalability and performance.
Team Collaboration (Daily Scrum Meeting).
Use Git/GitHub for version control. Work in a simulated Agile team environment.
Daily Schedule
9:30 AM - Daily Standup
15-minute team sync to plan the day's work
10 AM - 12 PM - Project Development
Focused coding time with mentor support
2 PM - 3 PM - Mentor Session
Deep dive into technical concepts and code reviews
Quick Facts
- Format Remote
- Cohort Size 20 interns
- Hours/Week 25-30
- Certificate Yes
- Work Time Flexible working time
Eligibility
Exciting Rewards
Application Deadline
Learning Outcomes
Machine Learning Fundamentals
Understand supervised, unsupervised, and reinforcement learning with real-world use cases.
Data Preprocessing & Feature Engineering
Learn to clean, transform, and prepare raw datasets for accurate model training.
Model Building & Evaluation
Develop ML models for classification, regression, and clustering, and evaluate them using standard metrics.
Deep Learning Concepts
Gain hands-on experience with neural networks, TensorFlow, and PyTorch for advanced ML tasks.
Ability to understand
Monolith, Microservices, API, Refactoring, Technical Debt, Boilerplate Code, POC, Hotfix.
Expertise on
Unit Test, End-to-End Test (E2E), Code Coverage, UAT (User Acceptance Testing), Sprint, Epic, Containerization, Monitoring & Observability.
Mentorship
1:1 Weekly Sessions
Personalized feedback and career guidance from senior developers
Code Reviews
Detailed feedback on your pull requests and architecture
24/7 Support
Dedicated Slack channel for instant help
AI-Powered Spam Email Classifier
Python, NLP, Naive Bayes, TensorFlow
Design a classifier that detects and filters spam emails automatically.
- Preprocess email text using NLP.
- Train Naive Bayes / LSTM models for spam detection.
- Provide probability scores for spam classification.
- Integrate with a mailbox-like interface.
Personalized Music Recommendation System
Python, ML, Spotify API
Build a recommendation engine that suggests songs based on user listening habits.
- Collect user playlists and song features from Spotify API.
- Implement collaborative & content-based filtering.
- Train ML models for personalized recommendations.
- Deploy as a web app with interactive UI.