📄 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.
Lab Supervised Learning
Train and compare multiple supervised learning models on a real dataset. Tasks include fitting linear and logistic regression, building a decision tree, and tuning a random forest. Students evaluate each model’s performance and learn how to select the best algorithm for a given prediction problem.
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