The most progressive, the most cutting-edge, the most exciting… Data science and machine learning are those areas nowadays that are enormously appealing and hot, hot, super-hot topics. But to stay tuned with all the advances and movements in these fields, you need to put lots of effort — researching, reading, checking all the information, news, guides, and other stuff.
This task is far away from being an easy solution. Right now, you can stumble upon a bunch of places with vivid titles and promising headlines, but are they useful enough? Every day I see a crazy flow of information, and, unfortunately, there are lots of false or worthless stuff, and especially on data science and ML. Where to find all the relevant and useful material? — that is the question.
Here is my collection of favorite and trustworthy resources I want to share with you.
Places to make an unforgettable adventure to the world of Machine Learning & Data Science
#1 r/datascience and r/MachineLearning
Both for professionals and amateurs, Reddit is a great place to share information among the scientists and ML-engineers of various experience levels, or just aspiring beginners. You can discuss and debate questions, memes, hot topics, all the latest achievements and more — Reddit will give you a variety of interesting things. Personally I use these sites with sorting filters — I select the hottest and popular topics — very often there are lots of important stuff.
I can’t imagine a data science career without DataCamp. Why so? Of course, there is a perfect option for total beginners and not only. I find that if you are interested in learning a new language or learning a new part of a new language, they are a great way to do that. However, while they are great, they are not enough to make you a data scientist. What I feel is lacking from their program is an actual project where they give you the challenge to solve. They do this to a minimal extent. In my experience, the best way to learn data science is to stumble through some actual projects.
One of the most popular resources on this list. There are probably articles covering all possible directions, questions, and cases — news, jobs, software, events, and more — you can find everything there. So it’s a complete package for data science lovers. You will get information about what’s new happening in data science fields, what courses you need to do, etc. However, KDnuggets is organized a bit differently, and it focuses on industry news, opinions and interviews, publicly available datasets, and data science software.
Datafloq offers information, insights, and opportunities to drive innovation with big data, blockchain, artificial intelligence and other emerging technologies (like data science). The site’s goal is to become a hub for reading high-quality posts, finding big data and technology vendors, connecting with talent, and publishing events. Datafloq offers online training as well. This blog isn’t just for data science practitioners either, also featuring sections on security and the Internet of Things.
It is an online mentoring platform to learn to program, and I am thrilled with it. Its main focus is to provide tutorials for all the amateurs who strive to learn to code, And for ML and data science, this skill is not redundant. The site offers insight from senior developers, customized reading lists, and the ability to connect with developers from all over the world. Hot topics included here are Angular, JavaScript, Node.js, Ruby, and Python. What I like about this site the most is that the people who work there are responsive (given that our time zones are vastly different) — they are professional, and they care about customers and mentors. In my experience, it can be good if you are diligent about screening the mentors. A lot of people will get you into a paid session just to google your errors for you, which isn’t very helpful obviously.
Distill claims to provide clear, dynamic and vivid Machine Learning research. Although it is not so popular among scientists, it really provides great stuff. The vast majority of articles there is interesting research and discoveries — but the most important thing is this — everything is written and edited by top specialists who work in companies like Open AI, Apple, and Tesla.
DATAVERSITY Education is a publisher of educational content for business and Information Technology professionals on the uses and management of data. Their team provides content to its worldwide community of practitioners, experts and developers who benefit from face-to-face hosted conferences, live webinars, white papers, online training daily news and articles, and blogs. They also offer a free weekly newsletter.
Data Science Central is perhaps the best independent data science blog on the web. Designed for big data practitioners, the site provides a community experience that includes an expansive editorial platform, social interaction, forum-based technical support, and the latest in technology, tools, and trends, along with a classifieds section for industry job opportunities. Data Science Central also offers webinars and a unique membership package that provides access to everything on the site for free.
First, what it doesn’t do: It doesn’t introduce you to Machine Learning. It won’t walk you through what Neural Nets are, the math behind word embeddings, and all that. You’ll have to pick up the theory elsewhere. It won’t take you from zero to hero. You need a foundation of math and command of programming before you can tackle machine learning.
But when you’ve brushed up on Matrices, have some sort of idea what a ‘Tensor’ is, when you have learned about various AI approaches from Support Vector Machines to Convolutional Neural Nets, and are ready to experiment and to build, MachineLearningMastery provides a working, simple example of every goddamn thing you could possibly imagine.
Data Science Dojo offers five-day public and private data science bootcamps. It features a community of mentors, students, and professionals committed to the field, and more than 3,600 users from 700 countries have graduated from the program. The Dojo blog provides a wide range of content spanning data science basics all the way up to more advanced topics like ethics and security and access control.
This is an exciting company that is going DataRobot transforms and accelerates predictive analytics with automated machine learning. What is great is that this company not only do great stuff but also provides the latest updates on what’s happening in the world of automated machine learning and data science.
Nate Silver’s data science blog, FiveThirtyEight, is one of the best data science blogs for analyzing the latest and greatest in the world of data science. The blog’s articles routinely feature interactive examples and in-depth articles detailing the ways in which data applies to politics, culture, the economy, and other facets of everyday life.
Data Science 101 provides all the resources aspiring data scientists will need while learning the tricks of the trade. Run by Ryan Swanstrom, the blog provides a constant stream of content, with topics ranging from the top companies to work for if you are a data scientist to job interview tips. Data Science 101 includes an active user community as well, and there are even an open Facebook group readers can join if they want to continue the conversation.
TDS strikes a good balance between solid machine learning and practical examples. There is a wealth of quality articles written by practicing Data Scientists. I see TDS as a place where Data Scientists and other machine learning practitioners document what they are working on, which is exactly what a good blog should be. TDS is able to promote praxis while not shying away from theory when it’s needed. While there is a slight oversupply of Deep Learning, TDS seems much less beguiled by DL than other sources, which is great for real-world Data Scientists actively trying to solve data-driven challenges.
insideBIGDATA is a news outlet that offers news, strategies, products, and services in the world of big data for data scientists and IT and business professionals. Their editorial focuses on big data, data science, AI, machine learning and deep learning. Its team of content producers features some of the brightest minds in the field, and really caters to the technical industry professionals looking to keep tabs on the most cutting-edge facets of machine learning and AI.
It is a big software company and the company has an amazing blog that features a number of articles and guides on all sorts of software like Hadoop, Apache, etc. which is very useful.
It is a research laboratory based in San Francisco, California. They offer comprehensive resources on AI — a blog, research papers, and interesting articles. Everything is up-to-date provided by the experts in their field.
#18 Tombone’s Computer Vision Blog
Deep Learning, Computer Vision, and the algorithms that are shaping the future of Artificial Intelligence.
This iss a free weekly newsletter with top data science picks from around the web. Covering machine learning, data visualization, analytics, and strategy. Definitely worth subscribing!
I can’t imagine a life without StackOverflow, can you? Stack Overflow — it’s an open community for people who spend their lives coding and are looking for answers to all types of questions or simply enjoy searching through interesting threads. It’s a great platform for sharing your knowledge and discovering new things.
Don’t be afraid to risk, don’t be scared to fall. Just learn — you can ;)
Machine learning and data science may seem like abracadabra. You may think you will never learn or understand all the concepts and definitions perfectly. But this is limited thinking leading you to nothing. I would say in this kind of learning — the most important thing is the art of making small steps. To make progress, you should do a regular scope of knowledge and make it your habit. I will guarantee you if you are interested in topics like that, you will see excellent results with time. I hope this selection will bring you lots of insights and opportunities to grow.
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