Many Americans have embraced the art of blogging. Out of these bloggers, many are tech bloggers who focus on various topics in machine learning (ML). The best machine learning blogs can help you learn deep learning, artificial intelligence, computer science, data mining, and so much more.
Blog posts can be written by skilled professionals, students, or amateur researchers. Each one can teach you something new about the tech industry. This guide will explore the best machine learning blogs, lessons you will learn from each blog post, and other resources for learning about machine learning.
How to Stay Informed About Machine Learning Updates
Technology is gaining momentum, and more people are interested in learning about machine learning and applying it to their projects. For example, Elon Musk and Tesla invented electric vehicles that function fully without human intervention. You can find updates about machine learning in books, YouTube videos, research reports, ML expert catalogs, and journals.
In addition, you can explore the wide array of educational articles on machine learning blogs across the Internet. According to Statista, Tumblr had almost 500 million blogs in 2020, and there are many other platforms out there. Blogs allow readers to compare information, gain machine learning knowledge, and access information effortlessly.
Success Lessons from the Top Machine Learning Blogs
Apart from developing new ML skills, you can also learn valuable lessons from the best machine learning blogs. Blogs allow you to discover the ins and outs of a topic before making mistakes on your own. Below are three important machine learning lessons you can learn from the best ML blogs.
Your ML Model Follows Your Lead
Although ML models can guide computer systems’ predictions, they don’t have the power to perform independently. Usually, your models will respond to training data, metrics, or target functions. Therefore, as you generate predictions, you need to train your model, deploy it, build a production workflow to process data, and measure the outcome.
Always Monitor Your Data
A major part of ML is data analysis. You learn to observe trends and analyze scenarios before deriving a conclusion. The data that you feed into your machine learning model is vital if you want to have good results. A good machine learning experience starts with high-quality data.
Every Problem Is Solvable
Every problem in life has some sort of solution, and machine learning is no different. An ML blog can help you learn how to troubleshoot. For example, kernel methods transform impenetrable data in lower dimensions into higher dimensions using decision boundaries.
Best of the Blogosphere: Top Blogs about Machine Learning
- Machine Learning Mastery
- Distill
- Google AI
- The BAIR Blog
- Amazon AWS Machine Learning Blog
- Towards Data Science
- OpenAI
- Carnegie Mellon University Machine Learning Blog
- Fast ML
- The Stanford AI Lab Blog
Best Machine Learning Blogs: Where to Find Machine Learning Support and Machine Learning Tutorials
Machine Learning Mastery
- Great for: Programming in Python, ML key concepts, developing a machine learning project from scratch
Machine Learning Mastery is an extensive ML blog founded by Jason Brownlee, a master’s and PhD holder in artificial intelligence. He started the blog to create a community of machine learning and artificial intelligence enthusiasts who want to learn new concepts and understand applied machine learning.
The blog covers various topics, including deep learning with Keras, linear algebra, statistics, code algorithms, and computer vision. It is the best machine learning blog for beginners and experienced ML professionals because of the range of educational posts. In addition, there is a collaborative community of ML lovers who share information and help amateurs learn.
Distill
- Great for: Research enthusiasts, learning advanced concepts in machine learning, publishing machine learning articles
Distill is less of a blog and more of a journal focusing on AI-related content. However, the online research journal explains the concepts of machine learning and artificial intelligence interactively. The site uses user-friendly data visualizations and graphics to help users understand ML.
Distill publishes high-quality articles, which are each reviewed by skilled editors for one to four weeks. The articles are also peer-reviewed for one to three months and undergo continuous review after publication. These educational posts cover research, commentary, exposition, resources, and artifacts.
Google AI
- Great for: Artificial intelligence and machine learning, research, ML applications in industry
The Google AI blog is a section of Google dedicated to artificial intelligence. The blog primarily focuses on research and how to apply AI fundamentals to products and new domains. As AI continues to dominate different industries, this blog is also a great place for beginners to understand artificial intelligence and use machine learning tools to their advantage.
The BAIR Blog
- Great for: AI research, people who enjoy ML discussions, students and beginners
The research department at UC Berkeley founded the Berkeley Artificial Intelligence Research blog. This blog aims to provide a platform where faculty members, researchers, and students can post their research findings and other AI-related updates. The blog posts cover various topics, including natural language processing, robotics, and reinforcement learning.
The posts appear once every two weeks and are written by students, faculty members, and post-docs. In addition, if you are interested in translating the blog posts, you can find an address on the official website where you can submit your requests to the authors.
Amazon AWS Machine Learning Blog
- Great for: Businesspeople integrating ML in their businesses
The Amazon Web Services blog focuses on machine learning fundamentals and their applications. You will explore the inner workings of ML at Amazon and how they can benefit your business. This is one of the best ways to learn AWS machine learning, though the best AWS bootcamps can also help.
Towards Data Science
- Great for: ML applications, data modeling, simple ML explanations
TWS is hosted by Medium and provides a platform where ML enthusiasts can discuss ideas and expand their understanding of machine learning. TWS allows worldwide contributions and works with a team of volunteer editorial associates who review every blog submission. Although TWS focuses on data science blog posts, you will find an entire section dedicated to ML and AI.
OpenAI
- Great for: AI enthusiasts, ML research, learning current AI technologies
OpenAI is a research and deployment company in San Francisco co-chaired by Sam Altman and Elon Musk. The company also owns the blog, which focuses on areas of artificial intelligence and how AI can benefit all of humanity.
The blog features contributors with a background in AI who analyze cutting-edge research and the advancements in AI technologies. OpenAI is a perfect blog for anyone interested in following the evolution of machine learning and other aspects of AI.
Carnegie Mellon University Machine Learning Blog
- Great for: Researching ML, group discussions on various ML topics, beginners and students
This blog, hosted by SCS Computing Facilities, aims to provide an accessible platform for Carnegie Mellon researchers. Contributors are free to highlight their research findings and share their perspectives on different aspects of machine learning and AI. The blog hosts bi-weekly articles and essential updates directed to CMU experts and the general audience.
FastML
- Great for: ML research, beginners with little understanding of ML, practical examples in ML
FastML is a machine learning blog run by Zygmunt Zając, who has shared insightful contributions on Hadoop, TensorFlow, Spark, and Docker. The blog’s primary goal is to tackle ML topics by making them entertaining and easy to understand. It is the go-to blog site for ML topics like pointer networks, chatbots, and neural networks.
The Stanford AI Lab Blog
- Great for: ML technologies, in-depth discussions, and practical applications
This blog accommodates Stanford University AI students, researchers, and faculty who share their findings in-house and with the general public. In addition, the blog hosts research findings, talks, lab updates, code examples, and discussions geared towards machine learning and AI fundamentals.
Where to Learn Machine Learning
You can learn machine learning through various platforms. The most common learning path is traditional university education, where you can pursue a Bachelor’s or Master’s Degree in Artificial Intelligence, Computer Science, or a related field. However, you can also learn machine learning through online training courses on sites like Coursera, Udemy, and edX.
Other learning resources include YouTube videos, machine learning books, and blog posts. Machine learning bootcamps are also an important resource if you want to learn in-depth skills. Coding bootcamps also include many other topics in the tech world.
Can You Learn Machine Learning in a Coding Bootcamp?
Yes, you can learn machine learning at a coding bootcamp. The best machine learning bootcamps are dedicated to providing students with hands-on experience and practical tech skills for the professional sector. Coding bootcamps provide a faster and more affordable alternative for people seeking to join the tech industry or enhance their tech skills.
Machine Learning Support
Machine learning support provides resources and training platforms for machine learning professionals, software engineers, and professional developers. As more people focus on this game-changing technology, industry experts provide more resources to simplify it. Below are three of the resources you can access to better understand machine learning.
Online Training Courses
Online courses offer flexibility, convenience, and more learning opportunities. You will gain real-world machine learning skills that you can apply in your day-to-day life. You can access these courses on Coursera, Udemy, edX, Udacity, and other websites.
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Machine Learning Books
Another amazing resource is machine learning books, which provide in-depth information on technical concepts. The authors are industry experts who have spent years researching and learning about ML. You will understand the history of ML, the meaning of programming languages, ML tools, and how ML powers real-life tasks.
Machine Learning Communities
Machine learning communities allow members to interact, share ideas, and enhance their ML skills. You can also work on projects, find an audience for your work, and gain access to professional opportunities. The Internet hosts several machine learning communities like the Reddit ML community, Quora, and LinkedIn communities.
What Should You Do Next to Advance Your Machine Learning Knowledge?
If you want to advance your machine learning knowledge, you should attend a coding bootcamp. These programs will help you learn everything you need to know to start a career in this field. You can also gain more knowledge by getting a job in this field, attending ML events, joining communities, or pursuing a higher degree.
According to PayScale, the average salary for machine learning engineers is $112,343. This means you can earn a lucrative salary with machine learning skills. Start learning machine learning today with the best ML blogs and begin your career.
Best Machine Learning Blogs FAQ
The best career paths for machine learning include data scientist, machine learning engineer, NLP engineer, or human-centered ML designer. Even software engineers and developers apply aspects of machine learning in their work.
To start a machine learning blog, you must be educated in the field, find suitable niche categories, and be consistent with enhancing your online presence. In addition, you must find a blogging platform and set up your website.
Yes, machine learning is an excellent technology career path. According to the US Bureau of Labor Statistics, most data science occupations earn over $100,000 per year, which is much higher than most other jobs.
Yes. These blogs explore many of the same topics because ML is a subset of artificial intelligence. However, AI covers a broader range of topics dealing with tasks that simulate human intelligence, whereas ML focuses on extracting data and enabling computer systems to make predictions.
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