This is a 4 months long program to get started with machine learning engineering.
The course consists of two parts.
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Part 1 of the course covers machine learning algorithms implemented in Python, including Linear Regression, Classification, Decision Trees, Ensemble Learning, and Neural Networks.
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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.
- Linear Regression
- Classification
- Decision Trees and Ensemble Learning
- Python and Jupyter notebooks
- Numpy and Pandas
- Matplotlib and Seaborn
- Tensorflow and Keras
- Flask, Pipenv and Docker
- AWS Lambda and TensorFlow Lite
- Kubernetes and TensorFlow Serving
- Kserve
https://datatalks.club/blog/machine-learning-zoomcamp.html
https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/master/