Udacity Aws Machine Learning

Udacity Aws Machine Learning – This is a very short description of this course. After I completed it I felt the need to share my experience and help other people who might be considering following the same course. I hope it can help someone decide if they want to take this program, how to complete it successfully, get the most out of it and avoid mistakes I made.

What I write here is my own opinion and personal experience and someone else’s opinion may be different of course.

Udacity Aws Machine Learning

For those who haven’t heard of it, Udacity is an online platform for learning technical skills such as: Data Science, Programming, Artificial Intelligence, Cloud Computing etc. The Machine Learning Engineer Nanodegree at Udacity is a comprehensive program that helps you prepare for the labor market in that area. It is a multidisciplinary program that teaches you many valuable skills such as: Software Engineering, Python Programming, Cloud Computing and Machine Learning.

Aws Sagemaker · Github Topics · Github

This course will help you learn and understand how to run real machine learning projects in conjunction with using Cloud in the Amazon cloud. That is especially valuable for someone fresh out of college who has never worked on the team. Understanding the real-life software development flow is key to cracking the job market and becoming employable. For less experienced developers who may not yet have had the opportunity to practice some of the techniques used in this course, it is a good introduction and starting point to learn more about: software testing, version control, implementation, reviews code, etc.

In addition, in this course you will learn to think like a data scientist, ask the right questions and learn from data. You will learn step by step how to carry out machine learning projects and improve the results of your work. It also teaches you how to do all this in the Amazon cloud by using AWS services and Amazon’s SageMaker Machine learning platform to train, test and deploy your Machine learning models. Check out my Git repository of required projects for this course. My detailed report on the latest capstone project can be found here.

This was one of the biggest revelations for me during the course. I realized that I was already familiar with most of the topics in this course. So obviously I was only getting limited value from it. Then I thought: Who could benefit most from a course like this? If you are in one of the categories below, then this program is for you:

The course website lists an estimated duration of 3 months at 10 hours per week. In my experience, this is a very optimistic estimate. It may only work that way if you can work a few hours each day. Ideally, you would like to work on this course for 6 to 8 hours a day, to complete it as quickly as possible and avoid paying higher fees than necessary.

Your Complete Aws Machine Learning Overview

In my case, it took me over 6 months to finish, and two applications. The mistake I made was underestimating the amount of work required for the final project. The main reason was the data cleaning necessary to perform the modelling. The datasets supplied contain a large number of inconsistent data samples. For example: character values ​​within numeric attributes, mixed decimal and integer values, large number of categorical attributes that look like regular numeric attributes, etc.

Correct data pre-processing was only possible after thorough reading and study of additional metadata documents, which describe the dataset columns in detail. Because the datasets contained a large number of columns, it took a long time to go through them all. This situation was really good to have because it is just like in real life. We rarely, if ever, get very clean and tidy data to work with. It usually boils down to the 80-20 rule, where 80% of the time is spent cleaning and pre-processing the data, and only 20% is spent on model training and development real.

This is not one of the cheapest courses on the market. For that reason, it is wise to make sure one has enough time to commit to it before starting it. There are two regular options with different prices:

There is also a third option, when Udacity has a promotion and gives a discount of around 60-70%. In that case, you may get a “Pay as you go” option for only $99 per month, which is the option I used.

Udacity Aws Machine Learning Foundations Summary.

Short answer: yes, definitely. I learned a lot about some great machine learning use cases and problem areas. The structure and content of the course aligns very well with real life projects and necessary skills. I wish I had taken it more seriously from the start so it didn’t take so long to finish. A big thank you to my employer Airteam for sponsoring my Nanodegree and investing in my education and vocational training.

There are 7 modules in total. Of these, 6 projects must be submitted. After you submit a project, it will be reviewed and you will receive personalized feedback.

It can be quite intense, but nothing in the course is really “hard”. You should expect to put some time and effort into completing each project.

Some of the units (Basics of Machine Learning, Model Evaluation, Supervised Learning, Deep Learning) are some of the best lectures I have ever seen.

Best Udacity Nanodegree Programs (2022)

They are really great, and the video lectures explain concepts in a way that even someone with high school math can understand. The focus is on building intuition, and those units succeeded.

For some units, the quality drops a bit. For example, some video lectures in unsupervised learning, such as PCA, are taken from other Udacity courses, so that could cause some confusion.

The PCAs are actually quite good (they are also offered in the free ML course at Udacity by Sebastian Thrun). But there is a short unit on Independent Component Analysis which is very poorly recorded. The narrator for those ICA videos was clearly unprepared, swallowing key words and generally inaudible. That is a great shame, because that is one of the most interesting parts of the course. That said, the same narrator did the hierarchical clustering video series, which was pretty good.

Reinforcement learning is a bit of a difficult module. Where the earlier units focused on building intuition, the RL units are very mathematical. Sometimes it feels like the videos are telling the comparisons, which is pretty pointless. That said, I’m not sure if there is a better way to teach this material.

Coursera Vs Udacity For Machine Learning

The deep learning division ended in MLPs and CNNs. There are many interesting things that could be included, especially those related to NLP/sequential models, such as regular neural networks (RNNs / LSTM / GRUs), attentional mechanism, word embedding.

Anyway, that material seems to be offered in the Deep Learning Nanodegree (which I didn’t take) and the free Intro to Deep Learning with PyTorch course.

When I signed up it was a 6 month course worth AUD $1250. I think it was worth it at the time, although it was very generously sponsored by my employer Airteam, rather than me paying it out of my own pocket.

Now, however, it’s a two-semester course, at double the price. It is now AUD$1250 for each installment, about AUD$2500 in total. Honestly I would find it hard to recommend this today at that price.

Free Udacity Nanodegree Online Courses With Certificate

I’m also torn, as Udacity courses get more expensive over time, so if you don’t sign up today, it will likely get even more expensive.

If you are considering doing this nanodegree but are concerned about the price, I suggest you quickly go through the syllabus to see if you can learn the material elsewhere. That takes a lot of discipline, especially doing projects yourself.

Either way, in the long run, $2500 AUD is probably not a lot of money to invest in yourself. Of course, you won’t get a job in machine learning if you only do the nano degree, but the material is high quality and the supervised projects and feedback are great. It’s a great starting point to start your career. The company aims to introduce machine learning (ML) to students as part of their higher education and to introduce them to future technology and AI disciplines.

To invite students interested in honing their machine learning skills and expertise, AWS partnered with Udacity for its AWS Machine Learning Engineer Scholarship Program.

Udacity And Aws Collaborate To Offer More Free Courses In Machine Learning

The goal of this program is to raise the level of machine learning expertise among all participants and develop the next generation of ML leaders worldwide, with an emphasis on underrepresented groups. AWS works with professional groups at the forefront of increasing the skills and diversity of engineering professions through the We Power Tech program, including groups such as Girls In Tech and the National Society of Black Engineers.

Principles of machine learning, the associated procedures

Machine learning engineer udacity, udacity machine learning course, udacity machine learning nanodegree, aws machine learning services, udacity machine learning review, udacity machine learning python, udacity google machine learning, udacity machine learning github, aws machine learning udacity, udacity machine learning, udacity aws, aws machine learning pricing