Python for Data Science and Machine Learning

This repository is a complete hands-on learning path for Data Science and Machine Learning using Python. It covers fundamentals, advanced analytics, visualization, machine learning algorithms, deep learning, NLP, and Big Data with Spark, with daily practice and projects.

📌 Topics Covered in this Course:

1. Python Foundations

• Python Crash Course
• Python for Data Analysis
• NumPy
• Pandas

2. Data Visualization

• Matplotlib
• Seaborn
• Pandas Built-in Visualization
• Plotly & Cufflinks
• Geographical Plotting

3. Exploratory Data Analysis

• Data Cleaning
• Feature Engineering
• Visualization Techniques
• Capstone Data Analysis Project

4. Machine Learning

• Introduction to Machine Learning
• Linear Regression
• Cross Validation
• Bias–Variance Tradeoff
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Decision Trees
• Random Forest
• Support Vector Machines (SVM)
• K-Means Clustering
• Principal Component Analysis (PCA)

5. Advanced Topics

• Recommendation Systems
• Natural Language Processing (NLP)
• Neural Networks & Deep Learning

6. Big Data

• Introduction to Big Data
• Apache Spark with Python (PySpark)

🎯 Objective

To build a strong industry-ready foundation in:

• Data Analysis
• Machine Learning
• Deep Learning
• Big Data Engineering

with real-world datasets, projects, and interview-focused implementations.

🚀 Who This Repo Is For

• Data Science & AI students
• Machine Learning aspirants
• Interview preparation
• Portfolio building
• Hands-on learners

🛠 Tech Stack

• Python
• NumPy, Pandas
• Matplotlib, Seaborn, Plotly
• Scikit-learn
• TensorFlow / PyTorch (Deep Learning)
• PySpark

📂 Structure

Each folder contains:

• Concept notebooks
• Code implementations
• Visualizations
• Mini projects
• Notes and explanations

📌 Maintained by Chirag Bharadwaj — M.Sc. in A.I. & Data Science, P.E.S. University 📈 Daily coding practice to build a strong Data Scientist portfolio