AI/ML and Data Engineering guided learning sessions

This is a comprehensive 16-week facilitated introduction to all the subjects necessary to master AI, Machine Learning, and Data Engineering using AWS platforms. These are guided 2-hour sessions on Monday and Wednesday evenings (GMT) and will complement university courses and structured training courses.
The program is as follows:

| Topic: |
| ------ |
| Amazon Kinesis |
| Confusion matrices and gradients |
| AWS Glue |
| Data pipelines |
| Amazon S3 |
| Precision, recall, F1, AUC/ROC |
| Introduction to Neural Networks |
| Data distributions |
| More on CNNs, RNNs and LLMs |
| Types of Data |
| Amazon Athena |
| Algorithms - introduction |
| Amazon EMR |
| Cleaning and prepping data |
| Quicksight |
| Sagemaker Data Wrangler |
| Regularization (including L1 and L2) |
| Feature Engineering |
| Convolutional Neural Networks |
| Sagemaker ground truth |
| Jupyter Notebooks |
| Sagemaker Studio |
| Feature Engineering |
| Sagemaker XGBoost |
| Generative AI |
| ML models - linear regression |
| ML models - KNN and Random Forest |
| ML models - Clustering and Naive Bayes |
| ML models - Decision tree and SVM |
| ML models - Dimensionality reduction algorithm and gradient boosting algorithms |
| ML models - K-means and logistic regression |