Udacity

Overall, I was happy with what I learned at Udacity Deep Learning course. The curriculum and the instructors were great.

Now, about the "not challenging enough" part. I have to say, the projects are fun, close to reality, and helpful. However, close to the end of the program, some projects have a lot of copy and paste solution from the lessons. For example, in the last project, which is deploying a deep learning model on Amazon Sage Maker, I found myself doing a lot of copying code from the lessons.

Some might say I should not copy the code, instead, I should type it down. Well, these are configuration lines for deployment. Nobody will try to remember them, that's impractical.

Make no mistake, I love the program, that's why I gave it 5/5 stars. Furthermore, I would love the program to improve even more!

If you're an experienced coder and are curious about taking Udacity's AI nanodegree, there were five things that helped me make a decision. 

I wish future me would have sent me this info back when I was trying to decide. I could have saved lots of time and fear of wasting my money.

Well, future me is here now. I wrote this review in hopes it will help you make your decision to go forward with the Udacity AI nanodegree or not.

[ to see the entire post goto [http://www.thedevpreneur.com/2017/11/26/udacity-ai-nano-degree-review-coders/]

 

1) Self learning AI is not working for you

How the Udacity AI nanodegree is better than self learning:

1. Provided a clear definition of "artificial Intelligence" beyond the trivial wikipedia version
2.  A structured list or map of all the areas of AI and how they relate.
3. Created clarity on how to clarify problems to identify how to solve them with our without AI.
4. Because I had to drop big $$$, I got a sense of urgency and co
5. mmitment. This drove me to carve out time in my week and get it done.
6. Focused in on the key areas of AI. There is way too much information out there, I got guidance on whats hot and making the biggest impacts in the field of AI. 

As trivial as these points seemed, the nanodegree gave me a new perspective. It provided me understanding beyond what I could price together on my own. It also gave me focus and forced me to commit to learning in a finite time frame. 

2) You hate reading white papers Here are some ways Udacity structured the information:

1. Simple animated illustrations for complex topics. This was one of the best parts of the content.
2.  Ease in beginners to reading white papers and only require some reading to complete some projects.
3. Introductory lessons on math notations and core CS concepts needed to understand  AI white papers.
4. The instructor team acknowledged that white papers are currently the best source for latest on AI advancements. The cool thing is that material only referenced white papers as additional reading in most lessons.

Bottom line the Udacity Material is way more beginner friendly than anything I have come across for free on the web .

3) You keep looking for the AI hello world example How Udacity was not too academic:

1. Although the general structure was still academically oriented and bottom up, they broke theory up into small consumable pieces.
2.  Lessons were supplemented with practical examples to reinforce what was taught.
3. Many of the examples provided, were based on real problems being solved in industry.

The AI nanodegree still feels academic in nature but had enough practical examples to keep me interested.

4) You have a hard time justifying the price How Udacity was more than an online course:

1. The team at Udacity will assign a  personal mentor to give you 1 on 1 answers and guidance.
2.  A career prep supplement with resume writing and networking tips and best practices.
3. Very active Slack community covering industry news, events, project discussion and contests.
4. Multiple required projects with code reviews and automated tests with scoring rubrics.
5. Life time access to all content with future updates. 

Even with all these add-ons, the cost felt a little on the higher end of what I'm comfortable with. Add-ons like the career advice is more targeted to junior candidates. On the other hand, the 1 on 1 mentor was very valuable and something you don't get with typical online courses. 

5) You don't feel you meet the prerequisites The prerequisites your really need  before you take the Udacity AI nanodegree:

1. Python basics with focus on looping, recursion and data structures
2. Using jupyter notebooks with python
3.  Condition and non-conditional probability basics. 
4. Some calculus with focus on derivatives if you really want to get into the theory. 
5. Very basic linear algebra with focus on matrics multiplication.  

The area that slowed me down the most was not the math. The Ai nanodegree covered most of the math  in refreshers and intros. I did struggle with figuring out efficient way to manipulate data using python and data libraries like numpy. 

Don't get intimidated by the math  prerequisites. If you're a good coder you shouldn't have too much difficulty getting through it.

I still would highly recommend doing as much pre learning in all the math areas mentioned. If you want to get the most for your $$$, you need to understand the theory and that requires understanding the math. 

So was it worth it?

I had a positive experience with the AI nano-degree and would recommend it to any developer willing to drop the $$$. Go for it if you're looking for the fastest way to learn a whole boat load of AI in a relatively short time.  Just like any paid education, you get out of it what you put in. So make sure you prep well and slice out enough time in your week to maximize your learning.

I've finished the Frontend Developer Nanodegree in August 2018, thanks to a scholarship provided by Google. I've never coded in my life before, but the course gave me all the basic tools needed to understand and learn more about web development. In other words, they did a great job teaching me how to ride a nice bike :)

The mentorship and the support provided by Udacity is overall very good. I've found extremely useful the project reviews, where a real developer was reviewing my code, providing extremely useful feedback. All the projects are then showed in your GitHub account, and this means that you can use them as a personal portfolio.

After the end of the course, I've also decided to use their "career support". It consists of quite many projects, whose goal is to improve your CV, your LinkedIn profile and to learn how to behave during a technical interview.
In November 2018 (2 months after the "graduation"), I've received my first traineeship offer as a frontend developer and today I'm a happy junior developer. 

I can only warmly recommend this course cause it re-pays itself after few months ;)