What You'll Learn
Comprehensive curriculum designed by industry experts
ML Fundamentals
- Python for ML
- NumPy & Pandas
- Data Preprocessing
- Feature Engineering
- Train-Test Split
- Model Evaluation
Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- SVM
- Gradient Boosting (XGBoost)
Deep Learning
- Neural Networks
- TensorFlow/PyTorch
- CNNs
- RNNs
- Transfer Learning
- Model Optimization
MLOps
- Model Deployment
- FastAPI
- Docker
- Model Monitoring
- CI/CD for ML
- Cloud Deployment
Live Projects
Build real-world applications that look great on your portfolio
Predictive Analytics System
Build an ML system for sales forecasting with model deployment.
PythonScikit-learnFastAPIDocker
Image Classification App
Create a deep learning model for image recognition with web interface.
TensorFlowCNNFlaskReact
Prerequisites
- Strong Python programming
- Mathematics (linear algebra, statistics)
- Basic ML concepts
- Computer with GPU (recommended)
Career Outcomes
- Build ML models from scratch
- Deploy models to production
- Master ML frameworks
- Work on real datasets
- ML Engineering certificate
- Portfolio with ML projects
Tools You'll Master
JupyterPythonTensorFlowScikit-learnDockerGit
Certification
Machine Learning Engineering Certificate
Stipend for exceptional work
Apply for This Internship
Start your journey with Machine Learning Engineering Internship