Become a Machine Learning Engineer

4.6 (424)
MOOC
Become a Machine Learning Engineer
Payment
Learning paid
Language
English
Duration
3 months
Course by Udacity
Become a Machine Learning Engineer
About the course
Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in industry.
This program is intended for students who already have knowledge of machine learning algorithms.
Requirements
  • At least 40hrs of programming experience
  • Familiarity with data structures like dictionaries and lists
  • Experience with libraries like NumPy and pandas
  • Supervised learning models, such as linear regression
  • Unsupervised models, such as k-means clustering
  • Deep learning models, such as neural networks
Lecturers
CC
Cezanne Camacho
Curriculum Lead
ML
Mat Leonard
Instructor
LS
Luis Serrano
Instructor
DR
Dan Romuald Mbanga
Instructor
JS
Jennifer Staab
Instructor
SC
Sean Carrell
Instructor
JB
Josh Bernhard
Data Scientist at Nerd Wallet
JA
Jay Alammar
Instructor
AP
Andrew Paster
Instructor
Platform
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This platform focuses on technical and business skills. You pay for monthly access to materials for a chosen course. For the majority of classes, trainees apply their knowledge to real cases, are advised by a career coach and get help in the recruitment process.
Comments (424)
I really enjoyed this program! Since I didn't have any background related to data, I first took Udacity's free courses such as Python, Statistics, SQL, Linear Algebra etc beforehand, and after I felt I got prepared I started this ND. I think it worked. Instructions were well organized, and explained with concrete and familiar examples, which helped me grab basic concepts of algorithms. At the end of each section, I had a project, where a lot of works and considerations were required. I searched and read a lot of resources that were sometimes beyond what I had learned in the lecture, which was necessary to complete the projects. I personally loved working on the projects, because reviewers always gave me precious feedback that contains a lot of suggestions and advises for further improvement as well as the words of affirmations and encouragement. It was always inspiring! Keeping up with deadlines was quite tough for me actually. I dedicated all my free time to this ND and managed to graduate on schedule. As a reward, I got skills, knowledge, and confidence that I can always learn something new! I believe this is the beginning of my new career in Data Science.
CN
Chieko N.
It is extremely useful. It could be a little more challenging, since a lot of things you are showing and they don't require much effort. There could me more challenging task, but with specific instructions of output. In the first processing of `test_review` in the first project was not clear how the shape of it should look like before passing to the model it could be derived either by trail and fail, forum or taking a peek into predict.py, which I guess we are not supposed to do at that moment. Some of the things are also outdated, pandas need version specified in 'requirements.txt' since new one is missing some method and even though sagemaker is at version 1.72.0 there are some deprecated lines like `.s3_input` method. The idea of this course is awesome and it would be perfect if I could spent more time on more challenging, well designed tasks instead of fixing some bugs connected to evolving libraries.
KK
Karol K.
Good to know about the machine learning deployment process in production environment. But it would be better if we can have more consideration as below : 1. I know AWS service is good to have, but this course is too dependent on it. Many companys can not envolve AWS service because of data security and IP issue. In this case, what can be a solution to replace AWS? All the methods and details that I learnt in this course is about AWS SageMaker functionality. 2. I know that this course provided the PyTorch and RNN stuffs as extra information as for reference, not in the core curriculum. When I encountered the first project, it needs knowledge or experience about the RNN model implemetation by using the PyTorch. All that I have learnt was XGBoost algorithm and how to deploy it and something like that. Need to include the RNN / PyTorch relevant curriculum in this course.
jp
junil p.
The program is an excellent refresher of ML concepts. I took a ML online class in 2014 (Andrew Ng's course) and this class was a good way to refresh the basic concepts. I am not sure how I would have performed if this was my first exposure to the themes. The exercises and projects were an excellent resource to familiarize myself with sklearn. I always wanted to become familiar with the library, but never found the focus to learn it. Now I feel very confident using it. I also appreciate the very basic refresher on numpy. Maybe a good reference could be provided to get more familiar with numpy and plotting techniques in general. I know this is outside the scope of the class, but I still feel not very knowledgeable about plots.
MB
Mauricio B.
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