Book Content
chapters • 11h total length
1. Understanding the AI/ML Landscape
2. Analyzing Open Source Software
3. Using Anaconda Distribution to Manage Packages
4. Working with Jupyter Notebooks and NumPy
5. Cleaning and Visualizing Data
6. Overcoming Bias in AI/ML
7. Choosing the Best AI Algorithm
8. Dealing with Common Data Problems
9. Building a Regression Model with scikit-learn
10. Explainable AI - Using LIME and SHAP
11. Tuning Hyperparameters and Versioning Your Model
