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- Introduction to Python programming
- Data types and structures
- Control Flow and Functions
- Object-Oriented Programming
- File Input/Output
- Error and Exception Handling
- Modules and Packages
- Numpy for numerical computing
- Pandas for data processing and analysis
- Data Visualization with Matplotlib and Seaborn
- Working with Time Series Data
- Introduction to Machine Learning
- Supervised Learning (Linear Regression, Logistic Regression, etc.)
- Unsupervised Learning (K-means, PCA, etc.)
- Deep Learning with TensorFlow and Keras
- Natural Language Processing (NLP)
- Reinforcement Learning
- Applications of Python in Data and Machine Learning (Examples, Case Studies)
- Best Practices and Tips for Effective Data and Machine Learning
- Advanced topics in Python for Data and Machine Learning (Ensemble methods, Feature Engineering, etc.)