Introduction to Python and its data ecosystem
Variables, data types, and operators
Control flow: loops and conditional statements
Functions and modular programming
File I/O and working with data sources

Introduction to NumPy arrays and vectorized operations
Pandas Series and DataFrame objects
Data cleaning, transformation, and merging
Grouping, aggregation, and pivot tables
Time series data analysis with Pandas

Principles of effective data visualization
Creating various plot types with Matplotlib
Statistical data visualization with Seaborn
Customizing plots for presentations

Introduction to machine learning concepts
Supervised learning: regression and classification
Unsupervised learning: clustering and dimensionality reduction
Model evaluation and cross-validation
Building a complete machine learning pipeline

Core concepts of neural networks
Introduction to TensorFlow and Keras (or PyTorch)
Building a simple neural network for classification
Understanding activation functions and optimizers

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Mastering Python for Data Science & AI

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Model evaluation and cross-validation

Content for model evaluation.