Find the best sources to gain Machine Learning knowledge
Jumping into the field of Machine Learning coming from SEM feels a bit like fighting against the ancient hydra monster from Greek mythology. Once you chop off a head it would regrow two new. There are so many Machine Learning websites and sources outside. For every term you’ve learned you’ll find three or four new on your desk. To avoid monster fighting, in this article I’ll show you my favorite resources I used and still use the most while learning in the field of Machine Learning.
1. Machine Learning Course Stanford/Coursera
This course was such an inspiring blast. Created by Andrew Ng (Professor for Machine Learning in Stanford University, founder of the Google Brain Team and former Chief Scientist of Chinese Tech Giant Baidu), this 12-week-course will guide you through the fundamental basic knowledge of ML, explaining the mathematical basics and providing tons of use cases and applications. The best thing: the enrollment is for free 😉
2. Jim Sterne – Artificial Intelligence for Marketing
This was so helpful when entering the field of AI and Machine Learning. 20 lectures, more than 25 hours of video-courses, handouts and notes to download. And you don’t even have to pay the Standford tution fee ; )
The platform is well known for its Machine Learning competitions, where companies show up with a problem and Machine Learners climbing into the ring, to build the best dedicated algorithm.
Another amazing feature Kaggle provides is the availability of public datasets. You can use this data to train your skills and get familiar with Machine Learning, before you start to import your “real-data-sheets” from GoogleAds, Search Console or MOZ.
But there are two other features I would like to highlight here, which are super useful forstarting with Machine Learning.
Kaggle offers a bunch of free online-courses for everything related to Machine Learning. Starting with Python-basics, up to Deep Learning Modelling and data Visualization. You’ll find a great documentation, a lot of code examples and a well thought structure of lessons. Really love it!
Github is an amazing platform for storing and sharing your code. You can download precoded algorithms for Machine Learning and adapt them for your purpose. Furthermore you’ll find useful introductions and courses for Machine Learning and Python. Here are some of my favorite GitHub repositories:
Jeff Heaton: Jeff Heathon, Data Scientist from Washington University in St. Louis, provides one of the greatest Machine Learning repositories I’ve seen so far. The content mirrors his classes at University including video material, install-instructions and knowledge from basic-ML to advanced insights in Deep Learning https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class01_intro_python.ipynb
Scikit Learn official: The official framework repository with more than 1.200 contributors including an installation guide, links for help and support and regularly updated code examples.
ML Turtorials: A collection of Machine Learning turtorials and related articles
SEM Smartation: Self-promotion-alert! You got me 🙂 In my own repository I’ve stored basic Python algorithms from Scikit-Learn. In addition you’ll find a bunch of GoogleAds scripts to automate your search-campaigns.
5. Codeacademy courses for Python & Machine Learning
The learning by doing approach makes this site one of the best entry-sources for Machine Learning. You’ll find a Python 2 course for free. Python 3 and a dedicated course for Machine Learning you’ll get in the pro-version. It’s worth it.
Elitedatascience.com contains a lot of useful content for people in the early access-stage of Machine Learning. You’ll find infos about libraries, frameworks, machine Learning concepts, guiding articles, coding-turtorials, how-to-start-content and and and….
In my opinion this site is a great source by following a top-down learning approach for Machine Learning.
7. Google Machine Learning crash course
Of course. Don’t forget Google when you’re talking about Machine Learning.
Answering some of the burning questions for ML-Newbies (How does ML differ from traditional programming? How do I represent my data so that a program can learn from it?).
this crash course is a marvelous place to start your Machine Learning journey.
8. Microsoft Azure Machine Learning Studio
Most of the tools for Machine-Learning and the usability in this field are still in their very infancy.
A tool that rises to this challenge is Microsoft Azure Machine Learning Studio.
To master ML-Studio in a professional way a certain knowledge of statistics is required. But for Machine Learning beginners the drag and drop functions are an amazing feature to play around with and get a feeling for algorithms and the whole process of Machine Learning.
You’ll find the tool inside the Azure cloud, which you can easly access with a Microsoft account.
9. The Artificial Intelligence Podcast by Lex Friedman
This podcast, hosted by Lex Fridman, Machine Learning/Deep Learning expert for autonomous vehicles and robotics from MIT, is educational and super entertaining at the same time. Elon Musk, Andre NG, Sebastian Thrun, he had them all. You’ll find the podcast on Spotify and also on Youtube.
10. All Research Papers about ML you can find
Ever joined an event about AI or Machine Learning? You’ll see that the audience here in most of the times is shaped by folks from universities and labs. That’s no surprise since ML is driven by Data SCIENCE. So I highly recommend to put your ear on the rails of research and read the latest and recently published papers.