AI Data Preparation and Visualization Techniques
( 24 Modules )

Module #1
Introduction to AI Data Preparation and Visualization
Overview of the importance of data preparation and visualization in AI and machine learning

Module #2
Data Quality and Cleaning
Techniques for identifying and handling missing data, outliers, and noise

Module #3
Data Transformation and Feature Scaling
Methods for transforming and scaling data for AI model training

Module #4
Data Normalization and Encoding
Techniques for normalizing and encoding categorical and numerical data

Module #5
Handling Imbalanced Datasets
Strategies for dealing with class imbalance in AI model training

Module #6
Data Augmentation for AI
Techniques for generating new data from existing data to improve AI model performance

Module #7
Introduction to Data Visualization
Overview of the importance of data visualization in AI and machine learning

Module #8
Visualization for Data Exploration
Using visualization to understand and explore datasets

Module #9
Visualization for Model Interpretability
Using visualization to understand and interpret AI model performance

Module #10
Matplotlib and Seaborn Fundamentals
Introduction to popular data visualization libraries in Python

Module #11
Plotting and Charting with Matplotlib
Creating various types of plots and charts with Matplotlib

Module #12
Data Visualization with Seaborn
Creating informative and attractive statistical graphics with Seaborn

Module #13
Interactive Visualization with Bokeh
Creating interactive visualizations with Bokeh

Module #14
Geospatial Data Visualization
Visualizing geospatial data with Folium and Plotly

Module #15
Time Series Data Visualization
Visualizing time series data with Pandas and Matplotlib

Module #16
Network Data Visualization
Visualizing network data with NetworkX and Matplotlib

Module #17
AI Model Explainability Techniques
Techniques for explaining and interpreting AI model decisions

Module #18
LIME for Model Interpretability
Using Local Interpretable Model-agnostic Explanations (LIME) for model interpretability

Module #19
SHAP Values for Model Interpretability
Using SHAP values for model interpretability

Module #20
Data Storytelling with Visualization
Using visualization to tell stories with data

Module #21
Best Practices for Data Visualization
Guidelines for creating effective and informative data visualizations

Module #22
Data Visualization Tools and Software
Overview of popular data visualization tools and software

Module #23
Real-World Applications of AI Data Preparation and Visualization
Case studies and examples of AI data preparation and visualization in various industries

Module #24
Course Wrap-Up & Conclusion
Planning next steps in AI Data Preparation and Visualization Techniques career

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AI Data Preparation and Visualization Techniques ( 24 Modules )

AI Data Preparation and Visualization Techniques
( 24 Modules )