Cezanne Camacho
8.2/10
(Our Score)
Product is rated as #206 in category Machine Learning
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.
Instructor Details
Votes: 0
Courses : 1
Specification: Become a Machine Learning Engineer
|
12 reviews for Become a Machine Learning Engineer
Show all
Most Helpful
Highest Rating
Lowest Rating
Add a review
| Price | $537 |
|---|---|
| Provider | |
| Duration | 120 hours |
| Year | 2020 |
| Level | Expert |
| Language | English |
| Certificate | Yes |
Become a Machine Learning Engineer
$537.00
Chieko N. –
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.
Mauricio B. –
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.
Manuel F. –
The MLND is a great hands–on program with introductions to the key ML/AI techniques. The coding quizzes and projects showcase what’s hot in tech, link out to great sources for deeper understanding, and at the same time familiarize the students with the practical and iterative type of work they can expect in ML jobs. I had started this program to enrich my ML understanding and skills, but have enjoyed working on the Udacity program so much that I decided to restart my career, moving on from consulting to the tech space. Looking forward:)
Parag A. –
The lectures were engaging. The projects helped to gain a better understanding of how to apply the concepts learned. The structured and guided questionnaires help a student learn about how to approach a given problem. The Slack community was very helpful. The reviewers put real thought into providing constructive feedback and also provide additional links to improve the thought process and content of the projects even further. Hope this experience helps me get a job track change into the ML field.
Bhavani C. –
This is , without a doubt, the best online Machine Learning Course. The material is concise and to the point. The projects are engaging and the project reviewers do a terrific job at giving you useful feedback to improve your models. I really enjoyed learning with Udacity and will be back soon for more!
Nuttapong W. –
This program is good at covering all required points to continue the profession as an ML engineer. It laid a good background on software engineering, for example, unit testing, prepare a python package. Also, I was allowed to deploy model several times which is the main element for this task.
Chieko N. –
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.
Mauricio B. –
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.
Manuel F. –
The MLND is a great hands on program with introductions to the key ML/AI techniques. The coding quizzes and projects showcase what’s hot in tech, link out to great sources for deeper understanding, and at the same time familiarize the students with the practical and iterative type of work they can expect in ML jobs. I had started this program to enrich my ML understanding and skills, but have enjoyed working on the Udacity program so much that I decided to restart my career, moving on from consulting to the tech space. Looking forward:)
Parag A. –
The lectures were engaging. The projects helped to gain a better understanding of how to apply the concepts learned. The structured and guided questionnaires help a student learn about how to approach a given problem. The Slack community was very helpful. The reviewers put real thought into providing constructive feedback and also provide additional links to improve the thought process and content of the projects even further. Hope this experience helps me get a job track change into the ML field.
pongsasit t. –
This program are mainly focus on hands on practices. So learn by doing make me really understand the way how to apply to my work. This course is really proper to data scientist who want to spread the vision and skills to be machine learning engineer. I was a data scientist working in R&D team before, but my new job will be in ML engineer role, so this course really help me understand more in engineering stuffs. (Sorry for my grammar)
Chun Yip Timothy N. –
The program is a bit different than what I expect. I was expecting a deeper dive into machine learning algorithms but it is more about the machine learning development cycle. That said, the AI Programming with Python Nanodegree and Intro to ML are almost absolute essential prerequisites for this course.