Welcome to my repository for the (Machine Learning Zoomcamp by DataTalksClub)[https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/master]. Here you can find my notes of the course and the assignments I completed. This course is structured to provide a comprehensive understanding of both the theoretical and practical aspects of machine learning.

1. Introduction to Machine Learning: This section introduces the foundational concepts of machine learning, the distinction between ML and rule-based systems, and tools such as NumPy and Pandas.

2. Machine Learning for Regression: Here, the focus is on regression techniques, data preparation, and feature engineering.

3. Machine Learning for Classification: This module covers classification methods, data preparation, and the importance of features.

4. Evaluation Metrics for Classification: This section delves into understanding accuracy, confusion tables, ROC curves, and other evaluation metrics.

5. Deploying Machine Learning Models: Learn about various deployment techniques, including Flask and Docker.

6. Decision Trees and Ensemble Learning: This module covers decision trees, random forests, and gradient boosting.

7. Neural Networks and Deep Learning: Dive deep into learning concepts, TensorFlow, Keras, and convolutional neural networks.

8. Serverless Deep Learning: Understand serverless concepts, AWS Lambda, and TensorFlow Lite.

9. Kubernetes and TensorFlow Serving: This section covers Kubernetes, TensorFlow Serving, and deployment techniques.

10. KServe: An optional module that provides insights into KServe, its applications, and deployment techniques.