Data Science with AI / ML
Project
Internship Program
Duration – 15 Weeks
Project Training – Offline / Online – 5 WEEKS
Project Development – Offline/ Online – 10 WEEKS
PROGRAM HIGHLIGHTS:
- Provides hands-on training in Python programming and applied machine learning.
- Covers essential Python libraries such as NumPy, Pandas, Matplotlib, and Seaborn.
- Exploring data cleaning, preprocessing, visualization, and exploratory data analysis (EDA).
- Deep insights on machine learning algorithms for regression, classification, and clustering.
- Emphasizes model evaluation techniques and performance optimization.
- Includes a guided capstone project using real-world datasets to apply end-to-end ML workflows.
OUTCOMES:
- Gain proficiency in Python programming and key data science libraries for analysis and visualization.
- Develop the ability to clean, preprocess, and engineer features from real-world datasets.
- Build, evaluate, and optimize machine learning models for classification, regression, and clustering tasks.
- Apply end-to-end ML workflows and deploy models with tools like Streamlit and GitHub.
PROJECT EXAMPLES:
- Apply machine learning algorithms like SVM, Random Forest, and K-Means to real-world datasets.
- Implement complete ML pipelines: preprocessing, modeling, and evaluation.
- Focus on feature engineering, hyperparameter tuning, and model interpretation.
- Deploy final models using Streamlit and version control with GitHub.
TOOLS AND RESOURCES:
- Python 3.x, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
- Jupyter Notebook, VS Code, Google Colab
PROJECT DEVELOPMENT – 10 Weeks
Project Phase-1: Environment Setup & Test bench Development
- Focus: Build foundation and learn ML techniques.
- Data preprocessing, visualization, and feature engineering
- Apply supervised & unsupervised ML models
- Understand evaluation metrics and model deployment basics
- Outcome: Ability to develop and evaluate ML models
Project Phase-2: Implementation, Debug & Final Demo
- Focus: End-to-end ML project execution.
- Dataset exploration and preprocessing
- Model training, tuning, and testing
- Final evaluation, GitHub repo, and project presentation
- Outcome: Complete ML project with deployment-ready code and documentation
