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Sentdex Machine Learning with Python Review

Straight up, I really enjoyed Sentdex Machine Learning with Python course (by Harrison @Sentdex). You can find the course on Youtube, and on his website. I found it at a great introduction, especially the overall understanding and intuition.

Sentdex Machine Learning with Python

Sentdex Machine Learning with Python

The course starts with an introduction to regression, best fit slopes and then starts to move from one topic to the next such as KNN, SVM and then onto Deep Learning topics with Tensorflow. The difference I feel with this course is how deep Sentdex gets into the subject. Plus, I love his passion when he’s instructing.

In terms of depth, I think a good example is when he is teaching us KNN.

After showing us how to do it the ‘easier’ way (Scikit-Learn), Sentdex starts from scratch and helps us build an understanding of how it all works. I think this really helps in realising what you are actually doing. I also really enjoyed the use of deep learning with Kaggle data such as Cats vs. Dogs and his kernal on Kaggle for Lung Cancer Detection.

Favourite Parts:

  1. In depth analysis of some of the technics, such as coding linear regression and KNN from scratch. I think this helps in building an understanding of what is happening. There are times when he will go through the process without code as well, but in the end I think the code speaks more.
  2. Enthusiasm and really passionate when he explains the process.
  3. He makes mistakes, but shows them and corrects them in the end. In some cases he will insert himself in the future to correct a big mistake just so we don’t go along with it. I prefer seeing these mistakes personally when I’m learning. Although ‘some’ of the code is deprecated – or very soon to be, you can always look at the Youtube comments and someone would have corrected it!
  4. Quick tip – you probably need to know a bit about Python to follow through. I recommend a simple introduction course such as Udacity, Udemy or Edx (MIT).
  5. My GITHUB (just going through sections).

Recommended!

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Jose Portilla Python for Data Science Review

I’ve started my transition in data science, and machine learning/artificial intelligence. There are many available resources available but I felt Jose Portilla’s Udemy Python for Data Science was a great overall starter. Course link here.

Python for Data Science

There is roughly 25 sections. Although the first 4 are more about set up (assuming no python background). Here are the things I enjoyed:

  1. The overall ‘introduction’ to Python and Data. Jose pretty much goes into each core area, such as numpy, pandas, matplot, seaborn and then jumps into machine learning (I honestly did not expect this part).

    Yep, Jose has convinced me Seaborn is sexy..

  2. The ‘Notebooks‘ – in Python for Data Science, Jose arranges notebooks for you to do exercises. I really like this part of it as it reinforces the lectures. This was also in his other course (Python bootcamp).
  3. Always reminding us to go through the data, explore it, and figure out why we are doing it.
  4. The start to finish of each project. The format of each topic generally boils down to a lecture (understanding) and then some exercises and then a problem set from start to finish. I normally use a blank notebook for each new topic and follow along with him.
  5. My GITHUB link for this course is here.
Completed the Python for Data Science course.

Completed the Python for Data Science course.

Reminder for those thinking of taking it!

This is an overview, and although there is a crash course for Python, I think a little bit of knowledge is good. Although Python for Data Science covers a lot of topics in machine learning (neural nets, deep learning, NLP) it doesn’t go deeply into them. If you are looking for that, I’d suggest a different course.

I think those pushing it, could finish the course in a less than a month – even a fortnight. A great entry point to Data science and although I do not plan to take the R studio equivalent, I highly recommend it!

 

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Moving From Games to AI

I’ve felt I’ve learnt a lot of programming from Crazy Fishing (VR), Pen Island (VR) and Scramble 7 (iOS & Android) and I want to progreinto Artificial Intelligence (particularly Computer Vision and Machine Learning). I’ve learnt a lot of programming and Managing teams & VR Gaming studios – and those lessons I can’t forget. 

vr gaming crazy fishing vr

VR Gaming as a Business

I think VR gaming will always be around, but the market is certainly not ready yet for a sustainable VR gaming studio, and it’s a damn hard industry to make it big on the mobile. Instead, I feel it’s better to start with something you genuinely love – your little pet game, such as QuiVR (a game that started as a project and grew to this! Stardew Valley was a game made by a programmer to improve his CV and ended up becoming a hit sensation! Undertale – a game that was made by Toby Fox which was made over years in his own time, before it had a successful Kickstarter. These games weren’t made for the money, instead they were made for themselves, but grew to hit sensations.

Out of all the games I’ve been apart of, Crazy Fishing has evolved to a game I enjoy. I can show friends, and I’ve learnt ALOT about virtual reality as a whole. I will write a post about the things we changed and what we discovered after we take Crazy Fishing out of Early Access (which surprisingly wont be too long from now).

Crazy Fishing screenshot

Gaming as a Side Business while working on AI/ML

I’ve been working doing dentistry and surgery during the day, and programming and working on games at night. Instead of doing only games, I’m going to start getting into AI as I think it’s an exciting field and could be a a huge thing in the future. The thought of a what is possible is fucking exciting (excuse my language) and can’t wait to be apart of it.

I won’t quit making games, but I won’t expect them to go big either. I believe the VR gaming market (At the moment) is not ready, but still highly interests me. The platform hasn’t even gone to its full potential, but the limitations with a small team or without a ridiculous budget are highly restricting.

Way forward:

  • I’m going to start learning Machine Learning and Python (I knew a bit, but now go full throttle) with courses I’ll list soon.
  • Working on at my surgery and teeth.
  • Managing some game projects with an active but not so active role (Got to keep the gaming programming skills up!)
  • Mastering computer vision.

Hope you will enjoy the ride to come, it’s going to be an exciting future!

Some Quick Shout Outs:

I’ve also met some amazing people, and some of the Freelancers I do want to shout out such as – Jun (super talented with logos and 2D work) and Dion (an amazing 3D artists).