Learning Advice to Study Udacity's Nanodegree Machine Learning Program

Sushmita Goswami

08 June 2023

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Course Overview

As Artificial intelligence is booming and is used in sectors ranging from finance to healthcare, udacity nanodegree machine learning course provides detailed insight into the domain of deep learning. You can build and apply your own deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment. You can become ML Engineering Expert in neural networks and learn to implement them using the deep learning framework PyTorch.

Deep Learning Nanodegree Program teaches you to build complex convolutional networks for image recognition, recurrent networks for sequence generation, Generative Adversarial Networks (GANs) for image generation, and learn how to deploy models accessible from a website.

In this course, you can collaborate or join via Slack where you can take the help of your cohorts and have healthy discussions. It is also a good platform for making live open-source contributions in mini Capstones.

"I showed this certification while applying for Masters abroad and got offers from top European universities to study computer science (mainly in AI)."

- Sushmita Goswami

Course Structure

It is a well-curated beginner-level course spread over 4 months. This online course is taught by experienced faculty members. It usually requires an effort of 10 hours per week just to regulate your course pace. This program has been developed exclusively for students interested about machine learning, AI, and deep learning and for those who have a basic working knowledge of Python programming.

I was taught by Alexis Cook and 3 other instructors. The teaching was entirely project-based; this enabled us to get a lot of hands-on practice. This is probably the best way to study deep learning.

The machine learning nanodegree course covers a lot of interesting subjects, with (usually) good explanatory videos and walkthroughs. The videos almost always pique interest about the subjects you are about to learn.

At the beginning of the machine learning engineering course, the content is about introducing Neural Networks, where learners implement gradient descent and backpropagation in Python. Afterward, learners are taught the different error functions and how to convert the conceptual knowledge of implementing gradient descent to practical.

The course also educates the learner on how to train neural networks by preventing overfitting training data and utilizing best practices for minimizing network error. This learning is used in sentiment analysis and image classification.

Insider Tips

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

Technical Exhibitionism from the Course

The course helps learners understand Neural Network Architecture, GANs, Deep Learning, NLP, etc. You can become an ml engineer expert in neural networks, and learn to implement them using the deep learning framework PyTorch. The course teaches you to build complex convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deep learning engineer deploy models accessible from a website.

Assessment and Grading Criteria

The assessment provides learners to work on assignments and projects. There are a total of 5 modules, each including multiple quizzes and projects. The learner can take the assessment numerous times and improve their knowledge with each test.

Capstone Projects

The modules of this course are detailed, comprehensive and practical. The capstone projects help learners to build in-depth knowledge on deep learning models. It also adds more value to their technical skillset.

In 3 months, I was able to develop 5 projects:

Implemented a neural network to predict the sentiment of movie reviews (positive or negative), trained on the AWS SageMaker platform using the IMDB data set, and created an Amazon API gateway for accessing this model as a service.
Implemented a DCGAN to generate realistic images of faces.
Built an RNN and LSTM Network with PyTorch to generate a "fake" TV script that approximates a training set of Seinfeld TV scripts from 9 seasons.
Developed a dog breed classifier app (88% accuracy) to estimate the dog's breed using the VGG16 model with transfer learning.
Built a multi-layer ANN to predict the number of bike-share users on a day, trained on UCI Bike-sharing data set

Final Take

I’m currently a Software Development Engineer (SDE3) at Hyland Software Solutions. My role involved handling an AI-based innovation project as a project lead. The knowledge obtained through this course was very helpful for me while I worked on the above AI project.

Moreover, in the future, if ever want to switch to being an AI engineer, this machine learning nanodegree udacity course would prove very handy.

I showed this certification while applying for Masters abroad and got offers from top European universities to study computer science (mainly in AI). This itself goes to show what a valuable certification it is.