Udacity Machine Learning Nanodegree Review
Are you interested in machine learning Nanodegree? Do you wish to pursue a career in machine learning?
Then this review article is the perfect place for you to find one of the most reliable online platforms to learn machine learning.
The Udacity Machine Learning Nanodegree Program is one of the most sought-after courses in today’s era due to its rising demand in artificial intelligence and other technological fields.
Aspiring learners from all the world are looking for ways to pursue a career in ML. One of the easiest ways to obtain adequate knowledge is using online platforms at the comfort of our homes.
In this article, I’ll discuss Udacity’s ML course along with its syllabus, duration, cost, and much more.
This way, students can solve their queries and doubts about machine learning and start a new journey of fun learning.
Without wasting any more time, let’s dive right into it !
Udacity Machine Learning nanodegree instructors
The instructors of Udacity Machine Learning Nanodegree Program are highly trained and qualified professionals who have decades of experience in several prominent and reputable companies.
The expert instructors of the machine learning program of Udacity are Cezanne Camacho, Mat Leonard, Luis Serrano, Dan Romuald Mbanga, Jennifer Staab, Sean Carrell, Josh Bernhard and Mat Leonard.
They completed their undergraduate, masters and PhD programs from universities like Stanford University, University of California, University of Michigan, University of Quebec at Montreal, University of Waterloo, and so on.
Additionally, they have worked in eminent companies like Galvanize, RTI International and United Therapeutics, Amazon and more.
Learners will get access to the best of education from leading experts that would prepare them well for their future endeavors as a machine learning expert.
What are the prerequisites for learning the Udacity ML Nanodegree program?
It is preferable that students have some prior knowledge before starting this program.
I have seen students struggling in clearing their doubts and making their concepts clear due to lack of basic knowledge. This makes it difficult to complete their course on time.
One of the major advantages of having prerequisite knowledge is that it saves a lot of time while completing a course, gives practical knowledge in solving projects as well as makes the experience more fun and enjoyable while learning.
Therefore, the prerequisites for learning the Udacity machine learning program are –
- Having intermediate Python programming skills like minimum 40 hours of programming experience, basic knowledge of data structures like dictionaries and lists as well as working with libraries like NumPy and pandas.
- Having intermediate skills of machine learning algorithms like supervised learning models, such as linear regression as well as unsupervised models, such as k-means clustering and deep learning models, such as neural networks.
Even if somebody doesn’t have the required set of skills then there’s no worry.
Udacity has free courses related to the prerequisite skills that can be beneficial in learning and polishing the required knowledge from scratch.
After completing the free courses and getting adequate knowledge, one can choose to enroll themselves for the paid machine learning nanodegree course.
Syllabus for Udacity ML Nanodegree Program:
The Udacity Machine Learning Nanodegree Program has a few ideas that guide the students to seek after an effective vocation in machine learning.
Notwithstanding, it is significant that the learners know about a few variables of machine learning like programming such as intermediate Python and Machine Learning Algorithms
For better arrangement, it is necessitated that the students are knowledgeable in every one of the primary parts of the referenced themes above.
There are a sum of 5 courses that set up the learners for their machine learning endeavors. These courses are separated into 3-5 illustrations or lessons.
These courses are offered based on the real-time advancement of machine learning so the learners get all the information that is best reasonable for their thought process.
Course 1: Software Engineering Fundamentals
This course helps the learners to develop machine learning algorithms for building scalable production systems.
This course is divided into three lessons : Software Engineering Practices, Programming, and Upload a Package to PyPI.
The first lesson teaches about writing code that is clean, modular, and well-documented.
Students will also learn using refactor code for testing efficiency, conduct and receive code reviews as well as logging that track actions and processes.
The second lesson is about understanding and using object-oriented programming as well as developing and using classes.
The third lesson is a portfolio exercise that teaches how to develop a Python package.
Course Project : Build a Python Package
This program is tied in with seeing how to assemble machine learning calculations and set them up for versatile production frameworks.
An initial move towards building these frameworks is to acquire a comprehension of composing production level code.
This enables you to have the choice of doing by composing a Python package according to your desire.
Course 2: Machine Learning in Production
This course teaches how to use Amazon SageMaker in order to deploy machine learning models to a production environment.
This course has five lessons : Introduction to
Deployment, Deploy a Model, Web Hosting, Model Monitoring, and Updating a Model.
I can say that all these lessons are based on understanding and using Amazon SageMaker in machine learning in various workplaces.
From lesson two onwards, the students will get practical exercises such as predicting housing prices in Boston and determining movie review sentiments using XGBoost on SageMaker.
They will also get to know about using API Gateway and Lambda in ML for producing web models.
Additionally, they will know about developing various models, knowing their behaviours, and using sentiment analysis for advanced purposes in later lessons.
Course Project : Deploy a Sentiment Analysis
Model
This project will teach you to deploy a sentiment analysis model. The students will be given a dataset reflecting information gathered from an investigation.
They will need to use factual methods to respond to inquiries regarding the information and note their decisions and suggestions in a report.
Course 3: Machine Learning Case Studies
This course teaches the real world applications of machine learning as well as understanding data and deploying both built-in and custom-made Amazon SageMaker models.
This has four lessons : Population Segmentation with SageMaker, Detecting Credit Card Fraud, Deploying Custom Models, and Time-Series Forecasting.
In lesson one, it teaches you about the basics of AWS SageMaker and its applications in a breadth of algorithms as well as unsupervised algorithms.
It also teaches to deploy an unsupervised model using SageMaker and draw it’s model attribute insights.
Lesson 2, 3 and 4 are all about applying the knowledge of machine learning deployment using AWS SageMaker and building successful models based on the names of the lessons.
This ensures practical experience for future purposes and projects.
Course Project : Deploy a Sentiment Analysis
Model
This project guides you through the process of extracting applicable text provisions and training a model of your own to do plagiarism identification.
Then, at that point, you will send your prepared model with AWS SageMaker.
Course 4: Machine Learning Capstone
The final course is about choosing any of the four electives and developing a model based on the task given in the chosen elective.
The four electives are :
- Starbucks
In this elective, you need to understand the behavior of customers to retain them.
You need to observe their buying tendencies and focus on those who are keen on rebates.
Next, you need to think of discount measures to gain and retain customers.
- Arvato Financial Services
This elective lets you work through real-time datasets and challenges given by Arvato Financial Services.
Best performers are given an interview opportunity at Arvato or another Bertelsmann company.
- Convolutional Neural Network
This elective is based on understanding and identifying dog breeds based on visual representation.
- Your Choice
This is a free elective. It means that you can develop any model or project based on your likings and preferences.
Course Project : Capstone Proposal and
Project
This capstone project will finally let you use what you’ve understood all through the program to assemble a machine learning model fitting your personal preference.
You will characterize the issue you need to settle, examine, distinguish and investigate the information.
Finally, you will showcase your reports and foster a bunch of analysis. Creating a blog in GitHub repository is the perfect way to show your reports and analysis.
This undertaking will fill in as a way to show your capacity as a machine learning engineer, and will play a significant role in your work portfolio.
Is Udacity Machine Learning Worth it in 2024?
The previous decade experienced some great advancements in science and technology fields which changed the entire game of many established companies and startups.
People from all around the world witnessed some mobile applications and technologies that one couldn’t previously think about a few years back, all thanks to the brains of creative developers, engineers, entrepreneurs and so on.
2021 is no exception as this year is also witnessing many more new technologies and systems.
In these continuous growing years of science and technology, it is essential that people get the best of knowledge in the required science fields and Udacity is a nice way to kick-start it.
The Udacity ML nanodegree program is still relevant as ever it was.
I have observed that many people choose to enroll in this program over other available courses because of its reliable contents and dedicated professionals that makes the learning experience worthy.
Udacity machine learning program is powered by Kaggle and Amazon Web Services (AWS) which makes it obvious that the content delivered is top notch and up to date.
Learners from all levels can easily understand the course and enjoy the process thoroughly.
Previous or past learners gave positive feedback to this ML course and its instructors that reflects their efforts and sincerity.
So I can definitely say this ML nanodegree program is definitely worth it in 2024 !
Is Udacity good for Machine Learning?
Udacity is definitely good for machine learning as the interface is easy to understand and is not complex in nature.
This helps novice learners to easily learn concepts and techniques to quickly make a positive move in understanding ML.
Hands-on practical exercises, real-time projects, feedback, interactive student hub, workspaces, quizzes as well as custom study plans, progress tracker and flexible learning structures are the perfect tools that one can use to measure their knowledge and progress.
Additionally, the students get the best of facilities like experienced project reviewers that give feedback for the projects created by students as well as technical mentor support and personal career services like LinkedIn profile optimization, GitHub portfolio review and resume support for future endeavors and career opportunities.
Although there are several other big players in the market that are also giving machine learning lessons, I think all of these qualities and packages along with other elements makes the Udacity nanodegree ideal for learning and understanding machine learning.
Is the Udacity Machine Learning Nanodegree Program worth the money ?
I agree that the cost for all Udacity nanodegree programs are on the higher side compared to its competitors but this machine learning nanodegree program is definitely worth the money.
Enrolling yourself to this course gives you access to multiple opportunities that can open a wide range of career options for you.
Along with learning with some of the best instructors of machine learning, the students get access to many tools and services like
- CLASS CONTENT
- Content co-created with Kaggle
- Real-world projects
- Project reviews
- Project feedback from experienced reviewers
- STUDENT SERVICES
- Technical mentor support
- Student community
- CAREER SERVICES
- Resume support
- Github review
- Linkedin profile optimization
Additionally, this program is a joint collaboration between Udacity, Kaggle and Amazon Web Services (AWS).
They are committed to delivering content that is fully up-to-date and covers all recent developments and advancements in technology.
This way, it ensures that the learners get a high chance to secure a job offer in one of the top most companies like Amazon, Microsoft, Google and so on.
Udacity Machine Learning nanodegree cost:
The cost for the Udacity machine learning nanodegree program is quite expensive compared to its competitors.
However, I think the cost can be justified on the basis of the quality of the courses and its approach in shaping the careers of the students.
Students can choose to enroll themselves for the course on a monthly basis or they can purchase the 3 months program.
They need to pay $303 if they choose the monthly program or $773 for the 3 months course program.
In both of these programs, students are entitled to a flexible learning schedule at their own comfort and satisfaction.
They can also choose to cancel their program anytime they want.
One bonus factor for choosing the 3 month program is that they can save 15% if they make the payment upfront.
Also, the average course duration for machine learning is 3 months so this plan is more suitable in saving money.
If you are still having some doubts or issues related to the cost of the course, then I do have something that may assist you with the cost.
Reader further to know more about personalized discount offers.
How to get Amazing offers or discount for Udacity ML Nanodegree Program:
I’ve seen many students finding and asking for ways to reduce the overall expense of this course as it’s on the higher side of cost.
Numerous Quora and Reddit questions prompted me to find a reliable solution for this issue. Thankfully, I got a solution that everyone can find useful.
You are eligible to get upto 75% or more percentage of discount based on the application you submit.
All you need to do is go to Udacity’s website and search for the ML nanodegree program. Click on the enrollment form and sign up.
Once you sign up, it asks you to fill in some basic information like phone number, country, current working status, company size, personal income and so on.
After completing these steps, you can select your preferred course for enrollment available from the options given (in this case, machine learning).
You would need to state your motive and reasons for choosing this nanodegree for better experience.
Double check all the information provided for clarity and click on Save & Submit option.
Upon receiving your application, Udacity will go through the enrollment form and analyze it thoroughly.
Based on that, they will provide personalized discounts to you that can be 75% or more.
Udacity Machine Learning nanodegree program scholarships:
Amazon Web Services (AWS) and Udacity have initiated a scholarship called The AWS Machine Learning Scholarship program that aims to promote skills and knowledge related to machine learning to anyone who is 18 and above.
The objective for this program is to up-level AI abilities to all, and to develop the advancement of ML pioneers across the world, with an emphasis on underrepresented gatherings.
As part of the program, it initiated the We Power Tech Program in AWS works together with proficient associations that are driving drives to build the variety and ability in specialized jobs, incorporating associations like Girls In Tech and the National Society of Black Engineers.
The scholarship for the current year was open from May 26, 2021. However, it is closed now.
The applicants get 3.5 months to learn all the topics and get hands-on experience on machine learning.
They will get to learn ML using AWS AI Devices like AWS DeepLens, AWS DeepRacer, and AWS DeepComposer.
Along with this, learners get to prepare their machine learning projects using Amazon SageMaker.
At the end of the program, top performers will be selected for fully paid enrollment in the The AWS Machine Learning Engineer Nanodegree program.
Conclusion
This is the end of review for Udacity’s machine learning (ML) nanodegree program.
Based on the article, it can be concluded that this nanodegree program has more pros to offer to the students than cons.
The biggest issue while purchasing this can be the cost of the course.
However, as there’s availability of personalized discounts through offers and promo codes, this issue can be easily tackled to get premium ML education.
Would you like to share your experience of learning ML at Udacity or any other information regarding this course? We’d love to hear from you.
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