Machine Learning zoomcamp

This is a 4 months long program to get started with machine learning engineering.

The course consists of two parts.

1. Part 1 of the course covers machine learning algorithms implemented in Python, including Linear Regression, Classification, Decision Trees, Ensemble Learning, and Neural Networks.

2. Part 2 focuses on deploying models using frameworks like Flask, TensorFlow, and Kubernetes, enabling practical application of machine learning in real-world scenarios.

To Receive a certificate, one needs to finalize and submit two projects.

• Midterm Project
  • A Machine learning project that aims to predict the rain. The model is trained with Gradient Boosting algorithm in a dataset composed of 145460 observations. The final model is containerized with Docker and deployed in AWS with Elastic Beanstalk.
• Capstone Project
  • A deep learning project that classifies satellite images. The model is a Convolutional Neural Network and was containerized with Docker.

Topics covered:

Part 1

1. Linear Regression
2. Classification
3. Decision Trees and Ensemble Learning
4. Python and Jupyter notebooks
5. Numpy and Pandas
6. Matplotlib and Seaborn
7. Tensorflow and Keras

Part 2

1. Flask, Pipenv and Docker
2. AWS Lambda and TensorFlow Lite
3. Kubernetes and TensorFlow Serving
4. Kserve

Tools/tech used:

https://datatalks.club/blog/machine-learning-zoomcamp.html

https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/master/