Data Science & Machine Learning

Machine Learning Engineering Internship

Master machine learning algorithms, model training, and deployment. Build intelligent systems that learn from data.

6 Months On-site Intermediate to Advanced
ML AlgorithmsModel TrainingProduction MLReal Projects
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Machine Learning
AI/ML ExpertBuild Intelligent Systems
What You'll Learn

Comprehensive Curriculum

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 ML Systems

Predictive Analytics
Predictive Analytics System

Build an ML system for sales forecasting with model deployment using FastAPI and Docker.

PythonScikit-learnFastAPIDocker
Image Classification
Image Classification App

Create a deep learning model for image recognition with a 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

Industry Standard Tools

Jupyter
Notebook
Python
ML Language
TensorFlow
Deep Learning
Scikit-learn
ML Library
Docker
Containers
Git
Version Control
📜

Machine Learning Engineering Certificate

Stipend for exceptional work from registered Pvt. Ltd. company

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