This is a 4 month practical bootcamp that puts you on the fast-track to becoming a Machine Learning Engineer:
- It starts from the basics of Machine and Deep Learning using a CRISP-DM Methodology
- It looks at internal mechanism of some of the most popular ML algorithms such as Linear Regression, Logistic Regression, Decision Trees, Ensemble Learning such as Random Forest and XGBoost, as well Neural Networks for Deep Learning.
- It then goes on to the packaging and deployment of ML/DL models with Docker, Flask and BentoML to cloud services
- The cloud services used are AWS EC2 and Lambda (for serverless computing)
- Additionally, it covers some essential advanced topics such as working with tflite and TensorFlow serving, as well as Kubernetes and KServe
- Finally, it ties everything together with 2-3 student-led projects that employ the tools and knowledge learned in the bootcamp
The highlights of the course are:
- Focus on collaborative problem solving, getting hands on with git and sharing in public via notes and write-ups
- It also includes weekly homeworks that serve as a guided walk-through of the concepts learned during the week
- Peer-reviewing and evaluation of projects
