Mastering Data Science with Python

◉ Introduction to Data Science

◉ Setting Up Your Environment

◉ Python Basics for Data Science

◉ Data Collection

◉ Data Cleaning and Preparation

◉ Exploratory Data Analysis (EDA)

◉ Introduction to Data Visualization

◉ Matplotlib

◉ Seaborn

◉ Plotly and Interactive Visualizations

◉ Introduction to Machine Learning

◉ Data Preparation for Machine Learning

◉ Supervised Learning

◉ Unsupervised Learning

◉ Model Evaluation and Tuning

◉ Introduction to Deep Learning

◉ Neural Networks

◉ Convolutional Neural Networks (CNNs)

◉ Recurrent Neural Networks (RNNs)

◉ Transfer Learning

◉ Generative Adversarial Networks (GANs)

◉ Reinforcement Learning

◉ Introduction to NLP

◉ Text Preprocessing

◉ Text Representation

◉ Text Classification

◉ Named Entity Recognition (NER)

◉ Machine Translation

◉ Introduction to Deployment

◉ Model Serialization and Saving

◉ Model Serving with Flask

◉ Model Serving with FastAPI

◉ Monitoring and Maintenance

◉ Case Study: Deploying a Real-World NLP Model

◉ Predictive Maintenance in Manufacturing

◉ Customer Segmentation in Retail

◉ Fraud Detection in Finance

◉ Image Classification in Healthcare

◉ Sentiment Analysis in Social Media

◉ Python for Data Science Cheat Sheet

◉ Scikit-Learn Cheat Sheet

◉ TensorFlow and Keras Cheat Sheet

◉ Matplotlib and Seaborn Cheat Sheet

◉ Deployment Cheat Sheet

◉ Mathematical Foundations

◉ Python Reference

◉ Data Science Resources

◉ Glossary of Terms