Learn to Structure Machine Learning Projects with this Popular Coursera Program

Punit Agrawal

07 June 2023

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Course Overview

Structuring Machine Learning Projects course is an online self-paced certification and is the third course in the 5-course series of the Deep Learning Specialization by deeplearning.ai. This machine learning projects course is taught by Andrew NG, a veteran in the field of Machine Learning (ML) and Deep Learning.

Dr. Andrew is the co-founder and head of Google Brain and was the former Chief Data Scientist at Baidu. He has over 20 years of practical and academic experience in this field, so I think he is a great person to be teaching these difficult concepts.

This is a very popular course and a highly recommended one in most analytics magazines. I happened to read about the course in one such analytics magazine. Through this ml projects course, I was able to learn the various cost functions for ML models, the bias-variance tradeoff and also how to find the optimal point of performance. The project machine learning course also helped me understand transfer learning and how it can be used to simplify model creation. This ml ai projects course will show you how data science projects can be structured end-to-end.

"Through this course, I was able to learn the various cost functions for ML models, the bias-variance tradeoff and also how to find the optimal point of performance."

- Punit Agrawal

Course Structure

This is a 2-week course, and both weeks will be quite similar in intensity. In week 1 of the course, we learned about the various cost functions for ML models and the bias-variance tradeoff. In week 2, we learned about transfer learning and structuring ML projects end-to-end. The quiz, after lessons, could be taken 3 times a day, and the coding assessments could be taken an unlimited number of times until we got the solution.

There was a text transcription of the video lectures, which could be downloaded. The course had a discussion forum where ideas and thoughts from around the world about the course were actively discussed.

This was a great forum to interact with like-minded individuals who were taking the projects in machine learning course and were interested in the world of data and machine learning. The course had 2 weekly assessments, both at the end of each week of the course. There were also quizzes and coding exercises to sharpen one’s knowledge of Python.

Insider Tips

In order to get the best out of this course, I have included some important tips that I think you might find useful.

Make Notes: One should maintain notes which can be actively reviewed even after course completion. This way, you can easily revise the heavy concepts in the projects on machine learning course.
Prerequisites: If you are someone who doesn’t have a background in ML Structures and Deep Learning, I recommend taking the first two courses in the Deep Learning Specialization before you dive into this ML projects one
- Neural Networks and Deep Learning | Coursera: This course will help you build foundational concepts of neural networks and deep learning. It will help you understand topics like Artificial Neural Networks, Backpropagation, and Neural Network Architecture.
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization | Coursera: This course deals with topics like Tensorflow, Mathematical Optimization, and Hyperparameter tuning.
Complete the Specialization: I recommend completing the entire Deep Learning Specialization to avoid any gaps in knowledge. The latest machine learning projects courses recommended after Structuring ML Projects are:
- Convolutional Neural Networks | Coursera
- Sequence Models | Coursera

Final Take

I am currently working as a Decision Scientist at Uber. My current job involves structuring data science projects and using the information gained from such projects to take key decisions to solve decision science problems. This projects on ml course by Andrew NG helped me understand how projects can be structured in the simplest and most efficient manner.

I believe that this machine learning projects course will be relevant as long as data science and ML stay relevant, which I think is quite a long time. I really recommend this course to anyone interested in ML and Structuring Models.