ADVANTAGES TO STUDENTS AFTER INTERNSHIP

1. Hands-on Experience: Interns get practical exposure to applying machine learning algorithms to real datasets, which enhances their understanding and skills.
2. Skill Development: Improves proficiency in Python programming, data manipulation, statistical analysis, and machine learning algorithms.
3. Industry Relevance: Internships provide insights into industry practices, tools, and workflows, making students job-ready.
4. Networking: Opportunities to network with professionals and potential employers in the field.
5. Resume Building: Adds practical experience and projects to their resume, increasing chances of landing a job after graduation.

SCOPE AND JOB PROFILES STUDENTS CAN APPLY FOR

1. Data Scientist: Analysing complex datasets, building predictive models, and interpreting results to drive business decisions.
2. Machine Learning Engineer: Developing and deploying machine learning models into production systems.
3. AI Researcher: Working on advanced algorithms and models, pushing the boundaries of AI capabilities.
4. Data Analyst: Extracting insights from data using statistical tools and techniques.
5. AI Consultant: Advising businesses on AI and machine learning strategies based on data analysis.

SYLLABUS OUTLINE

Module 1: Introduction to Python for Data Science:

• Basics of Python programming
• Libraries: NumPy, Pandas, Matplotlib, etc.

Module 2: Fundamentals of Machine Learning:

• Supervised learning (Regression, Classification)
• Unsupervised learning (Clustering, Dimensionality Reduction)

Module 3: Model Evaluation and Validation:

• Cross-validation
• Performance metrics (Accuracy, Precision, Recall, etc.)

Module 4: Advanced Machine Learning Techniques:

• Ensemble methods (Random Forests, Gradient Boosting)
• Neural networks and Deep Learning basics

Module 5: Data Pre-processing and Feature Engineering:

• Handling missing data
• Scaling and normalization
• Feature selection and extraction

Module 6: Deployment and Scaling:

• Model deployment using Flask or Docker
• Handling large datasets (Big Data tools)

Module 7: Real-world Projects and Case Studies:

• Working on industry-relevant projects
• Solving real-world problems using machine learning techniques