Python Edge –Applied ML Internship

Duration: 4 Weeks
Project Training – Offline / Online

Program Summary:

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.

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.

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.

Day	Topic	Details
Days 1–15: Theory + Simulation Labs
Day 1	Python for Data Science	Numpy, Pandas, Matplotlib crash course
Day 2	Data Cleaning & Preprocessing	Handling nulls, outliers, scaling
Day 3	Data Visualization	Matplotlib, Seaborn
Day 4	EDA & Feature Engineering	Encoding, feature selection
Day 5	ML Introduction & Pipeline	ML workflow, problem types
Day 6	Linear Regression	Predict house prices
Day 7	Model Evaluation – Regression	MAE, MSE, R² score
Day 8	Logistic Regression	Binary classification task
Day 9	Model Evaluation – Classification	Accuracy, Precision, Recall, F1-score
Day 10	Decision Trees & Random Forest	Hands-on: Titanic dataset
Day 11	KNN & Naive Bayes	Hands-on classification comparison
Day 12	Support Vector Machines (SVM)	Concept + implementation
Day 13	Unsupervised Learning – K-Means	Customer segmentation
Day 14	Dimensionality Reduction – PCA	Visualizing high-dimensional data
Day 15	Model Deployment Basics	Intro to Streamlit + GitHub integration
Days 16–20: Final ML Project
Day 16	Project Briefing	Problem understanding, dataset exploration
Day 17	Data Preprocessing & EDA	Clean, analyze, and prepare features
Day 18	Model Building	Train-test split, model training
Day 19	Evaluation & Optimization	Hyperparameter tuning, cross-validation
Day 20	Final Demo & Submission	Present results, GitHub repo, certificate issue
End to End Projects
Credit Card Fraud Detection, Sentiment Analysis on Tweets, Customer Churn Prediction