In machine learning, if you’re not developing, you’ll fall out of the loop sooner or later. It’s a difficult and demanding discipline that requires constant growth and broadening of skills.
One of the best ways to always stay on top of the latest machine learning trends is to follow blogs, forums, and websites dedicated to this specific profession. It’s a great way to learn from other people, very often renowned experts in their field, discover the latest findings and tips that can help you become a better data scientist.
If you’re looking for the best machine learning resources that are always up-to-date, make sure to check out this list. You can find different resources based on category. They all include fields such as AI, machine learning, IT, tech, and general Data Science.
What is Machine Learning?
Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, to look for patterns in data and make better decisions in the future based on the examples that we provide.
The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.
Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system can provide targets for any new input after sufficient training.
The learning algorithm can also compare its output with the correct, intended output and find errors to modify the model accordingly.
unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data.
The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data.
The systems that use this method can considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
Reinforcement machine learning algorithms are a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning.
This method allows machines and software agents to automatically determine the ideal behavior within a specific context to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.
Machine learning enables the analysis of massive quantities of data. While it generally delivers faster, more accurate results to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.
- FORUMS & COMMUNITIES
- Other Resources
Reddit – Reddit is the most powerful source of all knowledge on machine learning, deep learning, and Data Science in general. You can find many different threads with interesting information including resources – websites, blogs, problems people face, and smart solutions to common difficulties. Try out these threads to squeeze everything you can out of Reddit: r/MachineLearning, r/DeepLearning, r/DataScience, r/learn machine learning.
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.
Quora – Quora is another forum where people seek help or share their knowledge. It is not as detailed as Reddit, but you can still use it to look for some interesting resources. Make sure to check out different spaces related to machine learning to get updated information.
Kaggle – having a problem? Kaggle will help you. Share your issue with the Kaggle community and you will have it solved. Kaggle offers a large repository of code and data to make your work easier. Use the community to get inspired, fix an issue, or develop your skills.
Jupyter community – community for people using Jupyter who need to find a solution to a problem, help others in fixing bugs and issues, or share their work.
DEV – a community of software developers. Use it to find a solution to your dilemmas, experiments, or share your knowledge.
ods.ai – Open DataScience is a superb Russian forum that unites researchers, engineers, and developers who work in Data Science. An extremely engaging place where you can build and improve relationships with other people and learn from each other.
fast.ai – similar to ods.ai, fast.ai is a place for people who want to learn, share ideas, and collaborate with others. It offers free courses for coders, software libraries, cutting-edge research, and community.
GreyCampus – here, you can find numerous courses from the field of data science. Other resources on GreyCampus include Codelabs where you can learn to code, OpenCampus with access to a large resource library, and a blog with interesting articles published regularly.
DataFlair – here, you will find helpful courses on Big Data. DataFlair is a platform that combines, training courses with discussion forums, assignments, and quizzes. You can also find interesting and extensive blog posts on different topics.
Coursera – under this link you will find one of the most popular, highly-rated courses on machine learning offered by Stanford University. Coursera is a well-liked platform with online courses. You can search for other interesting courses to expand your knowledge.
MIT OpenCourseWare – OCW is a free and open online publication of material from thousands of MIT courses, covering the entire MIT curriculum, ranging from the introductory to the most advanced graduate courses. You can check their YouTube channel for helpful videos.
edX – edX is another platform with helpful courses where you can get certified.
Harvard Online Courses – this is a Harvard website with high-quality courses covering various subjects. Everyone can find something helpful in this source of knowledge.
Stanford Courses – if you’re looking for courses on machine learning from Stanford University, make sure to check their website with online pieces of training.
MIT News – straight from MIT (Massachusetts Institute of Technology) all the latest news from the world of machine learning.
ScienceDirect – lets you explore scientific, technical, and medical research.
Nature.com – interesting research on machine learning.
Academia.edu – Academia lets people share their research papers with others working in the field of machine learning.
Paper With Code – a free and open resource with Machine Learning papers, code, and evaluation tables.
arXiv – a free distribution service and an open archive for scholarly articles in the fields of physics, mathematics, computer science.
The University of Oxford – research papers from the University of Oxford.
CIT – research papers from California Institute of Technology.
Machine Learning @ Berkley – A student-run organization at UC Berkeley working on ML applications in industry, academic research, and making ML education more accessible to all.
The Batch – a weekly newsletter from deeplearning.ai. The Batch presents the most important AI events and perspective in a curated, easy-to-read report for engineers and business leaders. Every Wednesday, The Batch highlights a mix of the most practical research papers, industry-shaping applications, and high-impact business news.
Books – if you are a bookworm, you can search through the Amazon to find a book that interests you.
Deep Learning – an MIT Press book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The Deep Learning textbook is an online book available for free. It is intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. You can also order it on Amazon.
Data Science Weekly – A free weekly newsletter featuring curated news, articles, and jobs related to Data Science. Make sure to subscribe!
Data Elixir – A free weekly newsletter with top data science picks from around the web. Covering machine learning, data visualization, analytics, and strategy.
Everything you can find on this list is chosen based on popularity and user recommendations. I’ll be constantly updating the list with helpful links so you can stay on top of machine learning news.
Make sure to leave a comment if you think something should be included or excluded. Share your opinion, I’d love to hear from you!