Machine Learning Internship
Introduction
Machine Learning (ML) is a subset of Artificial Intelligence that enables systems to learn and improve from experience without being explicitly programmed. It uses algorithms and statistical models to analyze data, recognize patterns, and make predictions or decisions. ML is widely applied in sectors like healthcare, finance, e-commerce, robotics, and automation, making it a critical skill for the future workforce.
Who Can Join
- Engineering & Technology Students
- B.TECH & M.TECH students (all relevant branches)
- Specializations: CSE, IT, ITM, ITC, AI, ML, DS
- B.TECH & M.TECH students (all relevant branches)
- Graduates & Final-Year Students
- Computer Science & IT graduates or final-year students
- MCA, BCA, CA (Application), B.Sc., M.Sc. in CSE/IT
- Computer Science & IT graduates or final-year students
- Others
- Anyone looking to gain practical ML project experience.
How Machine Learning Works at Prasartech
At Prasartech Projects and Solution, ML is more than a subject—it’s a foundation for building intelligent systems in our projects. Students work with real-world datasets, learn algorithm design, and develop models capable of solving industry-level challenges. Our approach combines theory with project-based learning to ensure complete understanding and application.
Industry-Relevant Skills
Interns will gain expertise in:
- Supervised, Unsupervised, and Reinforcement Learning.
- Data Preprocessing and Feature Engineering.
- Model Training, Validation, and Hyperparameter Tuning.
- Libraries and tools such as Scikit-learn, Pandas, NumPy, Matplotlib, TensorFlow, and PyTorch.
- Supervised, Unsupervised, and Reinforcement Learning.
Hands-On Project Work
- Predictive analytics for business and research.
- Image classification and object detection systems.
- Sentiment analysis and text-based machine learning applications.
- Deployment of ML models for real-world use cases.
- Predictive analytics for business and research.
Expert Mentorship
The internship provides mentorship from experienced ML professionals who guide students through data handling, model optimization, and deployment techniques. Collaborative learning and structured project reviews ensure strong conceptual and practical mastery.
Outcome
- Enhanced technical expertise in Machine Learning.
- Readiness for careers in Data Science, AI, and Analytics, as well as academic research.
- Enhanced technical expertise in Machine Learning.
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