ML Engineering Course

This course covers various concepts and techniques in machine learning engineering. It includes library notebooks, math notebooks, model notebooks, time-series forecasting notebooks, and text generation notebooks.

Concepts Covered

• Data preprocessing
• Feature engineering
• Model evaluation
• Hyperparameter tuning
• Model deployment

Library Notebooks

• numpy_detailed.py
• numpy_ultraquick_tutorial.ipynb
• pandas_dataframe_ultraquick_tutorial.ipynb

Math Notebooks

• basics/arrays.ipynb
• basics/plotting.ipynb

Model Notebooks

• convoluted_neural_networks/cd_image_classification_with_image_augmentation.ipynb
• convoluted_neural_networks/cd_image_classification.ipynb
• convoluted_neural_networks/flowers_classification_transfer_learning.py
• convoluted_neural_networks/flowers_classification.py
• dense_networks/celsius_farenheit_model.py
• dense_networks/clothing_image_classification.ipynb
• dense_networks/clothing_image_classification.py

Time-Series Forecasting Notebooks

• common_patterns.ipynb
• moving_average_forecasting.ipynb
• naive_forecasting.ipynb
• using_time_windows.ipynb

Text Generation Notebooks

• text_generation_rnn.py