AI4ALL Ignite Course Notebooks Repository

Welcome to the AI4ALL Ignite Course Notebooks repository! This repository features Jupyter (IPython) notebooks organized to help learners explore various aspects of AI, from statistical models to machine learning techniques and beyond.

---

Folder Structure

The repository is organized into folders, each representing a major topic or module covered in the AI4ALL Ignite course. Below is the detailed structure and contents:

1. Statistical_Probability_Models/

• Overview: Foundations of statistics and probability for AI/ML applications.
• Contents:
  • Statistics.ipynb: A deep dive into statistical concepts and their practical applications.

---

2. Github_Version_Control/

• Overview: Guides learners through version control using Git and GitHub.
• Contents:
  • Github_Version_Control.ipynb: Introduction to using GitHub for collaborative coding and version control.

---

3. Machine_Learning_Methods_I_II/

• Overview: Covers core machine learning methods and their applications.
• Contents:
  • Elbow_&_Silhouette.ipynb: Cluster analysis and choosing the number of clusters.
  • Contrasting_Supervised_and_Unsupervised.ipynb: Highlights differences between supervised and unsupervised learning.
  • Train_Test_Split.ipynb: Introduction to splitting datasets for training and testing.
  • Natural_Language_Processing.ipynb: Applying ML techniques to process and analyze textual data.
  • Computer_Vision.ipynb: Exploring ML methods in image recognition and classification.

---

4. Model_Development_Testing_Training_Evaluation/

• Overview: Methods for developing and evaluating machine learning models.
• Contents:
  • Classification_and_Regression_Evaluation.ipynb: Evaluation metrics for classification and regression tasks.

---

5. Datasets_and_Visualizations/

• Overview: Hands-on examples of dataset manipulation and visualization.
• Contents:
  • DataFrame_Grouping.ipynb: Grouping and summarizing data with pandas.
  • Kaggle_API.ipynb: Accessing datasets directly from Kaggle.
  • Pairplot_Seaborn.ipynb: Creating pair plots with Seaborn for exploratory data analysis.
  • Types_of_Data_Visualizations.ipynb: Exploring various data visualization techniques.

---

6. Data_Preperations_Feature_Engineering/

• Overview: Techniques for preparing data and engineering useful features.
• Contents:
  • Categorical_Encoding_And_One_Hot.ipynb: Encoding categorical variables for ML models.
  • Data_Transformation_Scaling.ipynb: Scaling and transforming data for optimal performance.
  • 4Cs_of_Data_Prep.ipynb: A structured framework for data preparation.
  • Oversampling_and_Undersampling.ipynb: Addressing class imbalance in datasets.

---

Additional Files

• LICENSE: Specifies the repository's licensing terms.
• README.md: Provides an overview of the repository's purpose and structure.

---

How to Use This Repository

1. Clone the repository:

   git clone https://github.com/<your-username>/ai4all-ignite-notebooks.git
   cd ai4all-ignite-notebooks

2. Run the following command to install Python packages:

   pip install -r requirements.txt

3. Launch the notebooks:

   jupyter notebook

4. Navigate through the folders and explore the notebooks.