Lesson List

📄 Internship Offer Letter
⚠️ Please generate your Offer Letter before starting Week 1.

WEEK 1 – Python & Statistics Foundation
This week builds a strong foundation in Python programming and essential statistical concepts required for Artificial Intelligence and Machine Learning. Students develop core coding skills while understanding how mathematical principles support data analysis and model building. This foundation prepares learners for advanced AI and machine learning concepts in the upcoming weeks.

WEEK 2 – Data Handling & Visualization
This week equips students with the core tools used by every professional data scientist. Students master NumPy for numerical computing, Pandas for data manipulation, Matplotlib and Seaborn for visualisation, and the complete Exploratory Data Analysis process. By the end of this week, students can take any raw dataset and transform it into clean, model-ready data with meaningful visual insights.

WEEK 3 – Machine Learning
This week students build and evaluate real machine learning models using scikit-learn. Starting from core ML concepts, students progress through supervised learning algorithms, unsupervised clustering techniques, and professional model evaluation methods. By the end of this week, students can independently build, train, and assess ML models on real datasets.

WEEK 4 – Capstone Project & Internship Conclusion
The final week challenges students to apply everything they have learned by building two complete, real-world machine learning projects of their choice. Working independently, students demonstrate mastery of the full ML pipeline — from data collection and EDA through model building, evaluation, and presentation. Successful completion of both projects earns the Synkoc AI/ML Internship certificate.

Theory Capstone Project
Lab Capstone Project
Final Presentation & Review lesson (updated text)
Go to course home
Final Presentation & Review lesson (updated text)

🎤 Capstone Evaluation & Certification

You have completed all four weeks of the Synkoc AI/ML Internship. Now it is time for your instructor evaluation.

What to submit:
• Your completed Jupyter Notebook for both capstone projects
• A PDF project report following the documentation template
• A 10–15 slide presentation summarising your approach, results, and insights

Instructor Evaluation:
After you submit, your Synkoc instructor will review your project and schedule a 1-on-1 evaluation session to discuss your work and ask questions about your methodology.

Evaluation Criteria:
• Technical correctness of the ML pipeline (40%)
• Quality of EDA and feature engineering (25%)
• Clarity of documentation and presentation (20%)
• Code quality and reproducibility (15%)

After successful evaluation — within 24 hours:
✅ Internship Completion Certificate
✅ Training Certificate
✅ Letter of Recommendation (performance-based)
✅ Offer Letter from Synkoc IT Services Private Limited

📧 Submit your project to: support@synkoc.com
Your instructor will contact you within 2 working days to schedule the evaluation.

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