1. Familiarity with the Python environment and installation of data processing and machine learning libraries
2. Working with data, representing data structures and basic statistical indicators
3. Analyzing numerical and mixed data using the Pandas library
4. Basic functions from the Scikit-learn library for linear regression
5. Example problems and solutions using nonlinear regression
6. Multilayer neural networks
7. Applications of deep neural networks using the PyTorch library suitable for dynamic problem-solving
8. Solving a practical image classification problem using the presented numerical tools
9. An introductory description of the TensorFlow library used for deep learning and visualization in the private domain
2. Working with data, representing data structures and basic statistical indicators
3. Analyzing numerical and mixed data using the Pandas library
4. Basic functions from the Scikit-learn library for linear regression
5. Example problems and solutions using nonlinear regression
6. Multilayer neural networks
7. Applications of deep neural networks using the PyTorch library suitable for dynamic problem-solving
8. Solving a practical image classification problem using the presented numerical tools
9. An introductory description of the TensorFlow library used for deep learning and visualization in the private domain