https://github.com/prakharrathi25/mlops-specialization-coursera

Machine Learning for Production Specialization
https://github.com/prakharrathi25/mlops-specialization-coursera

coursera hacktoberfest machine-learning machine-learning-engineering mlops mooc-solutions production python

Last synced: 1 day ago
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Machine Learning for Production Specialization

• Host: GitHub
• URL: https://github.com/prakharrathi25/mlops-specialization-coursera
• Owner: prakharrathi25
• License: other
• Created: 2022-06-18T08:02:22.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-10-03T17:54:32.000Z (over 2 years ago)
• Last Synced: 2023-03-03T21:37:21.317Z (almost 2 years ago)
• Topics: coursera, hacktoberfest, machine-learning, machine-learning-engineering, mlops, mooc-solutions, production, python
• Homepage: https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops
• Size: 12.7 MB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Machine Learning for Production Specialization (MLOps)



This repo contains all my work for this specialization. All the code base, quiz questions, screenshot, and images, are taken from, unless specified, [Machine Learning Engineering for Production](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops) on Coursera offered by deeplearning.ai. 

**Instructor**: [Prof. Andrew Ng](www.andrewng.org)

## Reminder

As a CS major student and a long-time self-taught learner, I have completed many CS related MOOCs on Coursera, Udacity, Udemy, and Edx. I do understand the hard time you spend on understanding new concepts and debugging your program. The reason I am creating this repository is purely for academic use (in case for my future use). I am really glad if you can use it as a reference and happy to discuss with you about issues related with the course even further deep learning techniques.

Please only use it as a reference. The quiz and assignments are relatively easy to answer, hope you can have fun with the courses. Open to contribution by others.

## Program Structure 

### Course 1: [Introduction to Machine Learning in Production](https://github.com/prakharrathi25/natural-language-processing-coursera/tree/master/Course%201)

#### Week 1: [Overview of the ML Lifecycle and Deployment](https://github.com/prakharrathi25/mlops-specialization-coursera/tree/main/Course1/Week1)

  

1. [Graded Quiz 1](https://github.com/prakharrathi25/mlops-specialization-coursera/blob/main/Course1/Week1/graded_quiz_1.md)

2. [Graded Quiz 2](https://github.com/prakharrathi25/mlops-specialization-coursera/blob/main/Course1/Week1/graded_quiz_2.md)

#### Week 2: [Select and Train a Model](https://github.com/prakharrathi25/mlops-specialization-coursera/tree/main/Course1/Week2)

1. [Graded Quiz 1](https://github.com/prakharrathi25/natural-language-processing-coursera/blob/master/Course%201/Week2-Naive%20Bayes/NLP_C1_W2_lecture_nb_01.ipynb)

2. [Graded Lab Assignment](https://github.com/prakharrathi25/natural-language-processing-coursera/blob/master/Course%201/Week2-Naive%20Bayes/Week2_Graded_Assignment.ipynb)

## Note 

The content that is written inside each Course folder in the `README` has been picked up from the [course website].