Switching to Freelancing has given me much more free time. After hanging out with my family, I like to invest a lot of that time into educating myself and building new skills that would allow me to yield a greater return for the time I spend working on projects for clients – all in hopes of gaining more free time.
Here are some of the Machine Learning resources & blogs that I use when I want to get up to date.
Course Resources
Coursera
Whenever I am seeking to upskill my Machine Learning skills, Coursera is typically the first place I look for courses. It helps all the more that the Founder of Coursera is none other than Andrew Ng, the creator of possibly the most famous Machine Learning MOOC that exists.
Udemy
Udemy has also proven itself to be a great way to learn machine learning. Many of the courses I was taking regarding Machine Learning, always left out the deployment phase of the ML lifecycle. I really wanted to learn this and at the time the only course I could find was on Udemy – many other courses have since flooded the market. There is plenty of noise on Udemy from my experience, and after becoming familiar with how Coursera quizzes and tests you, I felt that the Udemy courses are a lot more passive – this may be the case for only the courses I’ve taken.
Udacity
I’ve only ever taken one Machine Learning course on Udacity, it was a nanodegree. From discussing with other learners and my one experience, I feel it is fair to say that Udacties nanodegrees are extremely comprehensive and you can be sure to feel a lot more competent. An argument I’d make against this resource is that it’s expensive, I paid £329 a month for my course. However, the support team is willing to negotiate something more affordable and there are pretty much sales all the time.
Blogs
Towards Data Science
Towards Data Science is the largest publication on Medium for Data Science. Since I spend quite a lot of time blogging on the platform, it makes sense why I also spend the majority of my time reading articles by other writers. If the work is an opinion, be sure to take whatever is being said with a pinch of salt and assess whether or not it’s something you agree with. On the other hand, if the work is more about technical concepts, ensure you cross-check with original sources.
Google AI
I recently came across Google AI and I was shocked to see how up-to-date their blog was. Most large companies allow their blog to go for months on end without new content but that doesn’t seem to be the case for Google AI blog. With Google being a major company, it’s likely that whatever they publish is going to be well-vetted before being released, this doesn’t mean everything published is necessarily correct, it merely means posts will be less opinionated.
KDNuggets
KDNuggets is probably the longest-standing online platform related to big data/data mining – please correct me if I’m wrong. It was probably the first platform when I decided to learn about Machine Learning and it was pivotal to my learning. While I’ve never taken any, the platform also hosts many webinars, tutorials, courses, and Datasets which may be valuable (if you’ve taken some, leave a comment sharing your experience).
Kaggle
I know what you’re thinking – "Kaggle is not a blog website". Technically, you’re correct. However, a flurry of practitioners posts extremely practical tips and tricks on the website so it’s worth going on there to read some of their ideas. For example, there is no harm in searching for the top kernels to read through and assess what made this kernel so good. After, then visit a less popular kernel and analyze it in the same way you did for the top kernel and determine why that kernel may not have been as popular as the top-ranked kernel.
Video Resources
The High ROI Data Scientists
I came in contact with Vin, not too long ago. But ever since our first interaction, I have not stopped consuming his content. Vin offers a very different perspective on the Data industry, one that isn’t abiding by the norms that have been put in place by the community and are somewhat pushing the boundaries of what it truly means to be a data professional. He shares extremely thought work on social media and what makes it all better is that he is now on YouTube making video content.
I highly recommend his content.
Wrap Up
Consuming content is only beneficial when the knowledge is being put to use. It’s very easy to fall into the trap of being a serial consumer, therefore, I highly recommend you’re only consuming content as and when needed for 1) inspiration of ideas 2) to tackle a current problem. The most growth you’ll see as a practitioner will generally be from the application of what you’ve learned and your failures so don’t try to avoid this by thinking you can consume your way out.
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