Here's how you can master AI algorithms with the best online resources.

There are 2 main types of online courses you can consider taking. The first type is a course that is provided by a university, such as MIT, Caltech or Columbia. These focus more on the theories of the AI / ML techniques. The second type has a specific product focus, such as those provided by Google / Microsoft / AWS on their respective cloud-based AI solutions. Choose one or more of these online courses depending on your knowledge needs and on the job requirements.

To master AI algorithms, utilize a blend of structured courses, practical application, and quality resources. Start with foundational courses like Coursera's "Machine Learning" by Andrew Ng and Udacity's "AI Programming with Python Nanodegree." Supplement with textbooks like "Deep Learning" by Goodfellow et al. and online tutorials from Google's Machine Learning Crash Course. Practice on interactive platforms like Kaggle and Google Colab. Stay updated with research via arXiv and Google Scholar. Engage in communities like Reddit's r/MachineLearning and Stack Overflow. Follow YouTube channels like 3Blue1Brown and podcasts like "AI Alignment Podcast." Apply knowledge through personal projects and open-source contributions.

Mastering AI algorithms demands commitment and access to essential tools. Here’s how to excel: Structured Learning: Platforms like edX provide structured courses from foundational to advanced levels, building a robust knowledge base. Practical Application: Participate in projects on platforms such as GitHub to gain hands-on experience with real-world datasets, reinforcing theoretical learning. Community Engagement: Join AI communities like Reddit to network with peers, exchange insights, and collaborate on solving complex problems, accelerating your AI proficiency journey.

Head over to stanford.edu, pick cs229 or either Machine Learning specialization by Andrew NG. Start working on problem sets, algorithms, projects. Write CODE! Doesn't matter if it is not optimized, keep writing, the more you write the more comfortable you get. Refer George Hotz, Andrej Karpathy on Youtube. Most importantly, Write More CODE!

Structured Learning Platforms: Start with platforms like Coursera (e.g., Andrew Ng's Deep Learning Specialisation) or edX (e.g., MITx: Introduction to Deep Learning) for a solid foundation in core algorithms and concepts. Hands-on Practice & Community Engagement: Complement your learning with coding exercises on platforms like Kaggle Learn or participate in online forums like TensorFlow Discussions to solidify your understanding and stay updated on the latest advancements.

Actually I would tend to disagree with that. I don't think you could be proficient in AI by reading books or e-books. This field relies heavily on Computer Science and you need to be able to code extensively and naturally about complex AI models. Books are amazing for Math or stats, but when dealing with AI you should get as hands-on as possible. From my experience, the best practitioners and researchers in AI are first and foremost excellent developers who have great intuition about Neural Nets and who can implement their ideas quickly.

Books and e-books on AI algorithms offer more than just technical knowledge; they represent a journey of exploration and discovery. Whether in physical or digital form, these resources provide a tangible connection to the evolving world of artificial intelligence. They not only offer in-depth explanations and practical examples but also encourage a reflective and self-paced learning experience.

These plays a crucial role in context and knowledge few examples of ebooks and e content are Foundational and Comprehensive AI Books:"Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter NorvigWidely regarded as the definitive textbook on AI, this book covers a broad range of topics, from basic concepts to advanced techniques. It is suitable for both beginners and those with some background in AI."Pattern Recognition and Machine Learning" by Christopher M. BishopThis book provides an excellent introduction to the fields of pattern recognition and machine learning. It's more mathematically rigorous and is great for those looking to deepen their understanding of the theory behind machine learning algorithms.

When diving into the world of AI algorithms, books and e-books are your best friends. These resources offer detailed explanations, case studies, and examples to enhance your understanding. Whether you opt for a physical book or a digital version, these materials enable self-paced learning and serve as valuable references for complex topics. Embrace books as your AI study companions to build a solid foundation in this transformative technology.

Video tutorials are amazing ! Let me take an example : I recently carried Research on new positional embeddings used in LLMs and I had to go over the literature in this field, starting all the way form the original 'Attention is all you need' paper in 2017. You would be surprised to see that sometimes research papers can become very obscure about their methodology and techniques used. I found that online videos were very helpful as they often provide with visualizations about complex topics in an easy-to-understand way

Video tutorials offer a dynamic learning experience that caters to different styles of understanding, making complex AI concepts more accessible and engaging. Watching experts code and explain algorithms in real-time not only demystifies technical intricacies but also fosters a sense of connection and relatability to the learning process. By following along with these tutorials, learners can not only grasp theoretical concepts but also gain practical skills through hands-on application.

Learn AI if you like watching and listening. Experts show how they code and explain stuff in real-time, making hard topics easier to get. You can see how algorithms work and try it yourself, which helps you remember better.

Mastering AI algorithms takes time, practice, and access to high-quality resources. For Forums and Communities the following can be mentioned: - Stack Overflow: A great resource for solving specific problems related to AI programming and algorithms. - Reddit (r/MachineLearning, r/ArtificialIntelligence): Active communities where you can find discussions, news, and resources about AI and machine learning. - Towards Data Science: A blogging platform on Medium where experts share tutorials, experiences and insights. In these ways, you can develop a solid understanding of AI algorithms and stay updated on the latest industry trends and technologies.

Forums & online commus are invaluable for AI learning due to their collaborative nature. Some tips and tricks to maximize their benefits: Active Participation: Don’t just observe; actively engage by asking Qs & contributing to discussions. Diverse Perspectives: Embrace the variety of viewpoints to problem-solving you’ll encounter. Networking: Use these platforms to connect with peers & experts who can provide mentorship and career opportunities. Resource Sharing: Benefit from shared resources like tutorials, & webinars that can enhance your knowledge. Collaboration: Look for opportunities to collaborate on projects or research, which can lead to innovative solutions and professional growth.

Joining forums and online communities in the AI sphere isn't just about networking; it's about becoming part of a supportive and diverse community. These platforms offer more than just technical insights—they create spaces where individuals can exchange ideas, seek guidance, and share challenges and triumphs.

Mastering AI algorithms can be greatly facilitated by leveraging the best online resources. Joining forums and online communities, such as those on LinkedIn and various groups and channels on other social media platforms, allows you to connect with AI enthusiasts and professionals, offering diverse perspectives and problem-solving techniques. Engaging in these discussions, asking questions, sharing experiences, and receiving feedback fosters a collaborative environment that significantly enhances your learning experience. This approach not only deepens your understanding but also keeps you updated on the latest developments in the field, ultimately accelerating your mastery of AI algorithms.

There is no substitute for learning from practising your knowledge! If ever you have been interested in AI or ML, now is the time to enter it. You don't need to have a PhD in AI to get into problem-solving with it. Frameworks such as PyTorch or TensorFlow can provide a head-start with limited 'pure AI domain' expertise. As AI matures, there will be a time when there is more of a software development process. To put your knowledge into practice, you should also focus on the Data workflow your organization follows - how data is collected, large data sets are processed - offline and online and how mature is the ability to recover the data insights. Data is key to put your AI knowledge into practice and demonstrate results for your projects.

Personally, I would recommend to work on projects but not so much on competitions. Let me explain, when learning about AI you need to get familiar with the ins and outs of this field and this has to go through practical coding sessions. However, competitions can so easily gamified that you could learn a way to trick the problem and overfit your learning on a single use-case without being able to generalize to other topics.

Engaging in AI projects and challenges is akin to embarking on a personal journey of discovery and growth. Starting with simpler projects allows you to build a solid foundation and gain confidence in applying your knowledge. As you tackle more complex challenges, each milestone becomes an opportunity to learn from both successes and setbacks, deepening your understanding of algorithmic intricacies and problem-solving strategies in practical contexts.

Projects and Challenges: Work on personal projects that interest you and challenge you to apply your AI and machine learning skill Participate in Kaggle competitions to practice your AI and machine learning skills and learn from others. Google AI challenges provide a platform to practice your AI and machine learning skills and learn from others. AI for Everyone that provides a range of projects that you can work on to practice your AI and machine learning skills. Machine Learning Mastery provides a range of projects that you can work on to practice your machine learning and AI skills.

Doing projects and challenges is key for really getting AI. Begin with easy projects to gain confidence, then try tougher ones. This hands-on work helps you understand AI better and learn how to solve real problems with it.

Continuous learning in AI isn't just about staying ahead in a competitive field; it's about nurturing a mindset of curiosity and growth. Subscribing to newsletters, diving into research papers, and participating in webinars are more than just activities—they're opportunities to immerse oneself in the cutting-edge developments that shape the future of AI.

To excel in the dynamic field of AI, continuous learning is key. Stay ahead by subscribing to newsletters, reading research papers, and attending webinars. This proactive approach will keep you updated on the latest advancements, enabling you to implement cutting-edge algorithms in your projects effectively. Embrace lifelong learning to stay at the forefront of AI innovation and maximize your potential in this transformative technology.

To master AI algorithms with online resources: Online Courses: Enroll in structured courses from platforms like Coursera, edX, or Udacity that offer specialized AI programs. Tutorials and Guides: Utilize free resources from sites like Khan Academy, Codecademy, or Medium, where experts share insights and tutorials. Practice Platforms: Engage with interactive platforms like Kaggle or HackerRank to practice coding and algorithm challenges. Webinars and Workshops: Attend live sessions from AI experts to gain current industry insights and ask questions in real-time. Join Forums and Communities: Participate in forums like Stack Overflow or GitHub to discuss issues, exchange ideas, and get feedback from peers.

Blogs and articles on Medium are incredibly valuable. I personally learn more effectively when I see visualisations of complex topics and a huge work has been done by AI practitioners to represent AI models with graphs and drawings. I love it !

Following AI experts on social media is a smart move. They often share valuable content, new ideas, and unique perspectives that you won't find in textbooks. By keeping up with their posts, you can learn about the latest trends, discover new concepts, and get insights that can deepen your understanding of AI. Plus, it’s a great way to stay connected with the AI community and continuously learn.

I’ve learned quite a bit from ChatGPT and others LLMs the last couple of years. I may even call it micro-learning as I can easily learn new things from these foundational llm apps on my iPhone, whenever/wherever.