What do you do if you want to master artificial intelligence through online resources?

Lower your bareer of entry: be sure you have basic knowledge of python, calculus - basic understanding of matrix algebra, a bit of statistics, some basic function differenciation. If you understand, can derive and implement a linear fit, you are ready.

Learning a new subject always comes with new words/concepts. It's a matter of communication, just like learning a new language, you have to learn the jargon. My first iteration would be to try reading a research paper (probably the best place to find the most complicated words in a field) and list all the words or concepts I have never heard of. Once this glossary is created and the research done, I would start reading from the beginning again and again, until I can understand the subject of a paper in a general sense and be able to mentally rewrite it in simplified terms. Hopefully, nowadays this process can now be greatly accelerated by using ChatGPT, which is very good at this type of tasks.

Embarking on your AI journey can be challenging for a newcomer. In order to master AI (or, in fact, any new area of knowledge) you have to start with the basics and follow these steps: 1. Goal setting - Identify the main concept you want to focus on. 2. Simple start - Get a general overview of what that concept means: its definition, its functionality and its purpose. 3. Dive deep - begin learning the theory behind the concept and, at the same time, try to put it into practice. 4. Practice - Come up with your own examples and practical applications of the concept and try to implement them. 5. Revise - Go over the previous steps periodically.

To master artificial intelligence through online resources, you would typically start by identifying your specific interests within Al, then exploring relevant courses, tutorials, and resources offered by platforms like Coursera, edX, Udacity, and others. Additionally, participating in online communities, reading research papers, and working on practical projects can further deepen your understanding and expertise in the field.

Embarking on your AI journey requires solid fundamental concepts. Here’s how to start: - Understand algorithms, data structures, and computational thinking. These form the bedrock of AI. - Learn programming languages like Python—simple, versatile, and widely used in AI. - Study Algorithms (to know how computers solve problems), Data Structures (to organize and store data efficiently) and Computational Thinking (to develop problem-solving skills). - Take beginner-friendly courses on platforms like Coursera, edX, Udacity or LinkedIn Learning. There are many courses that introduce concepts gradually. A strong foundation ensures smoother progress as you delve deeper into AI complexities. Happy learning! ??

Math is how we count nature no matter the vagaries. Ask and seek for math behind natural phenomena such as what is the mean, median, mode of the height of oak trees (or your favorite tree) in your region? Seek to estimate unknown mathematical facts such as depth of the water in a lake near you and then look for the actuals. What was the error? AI is after all an algorithmic reduction of errors in a mathematical representation of real world. Exercising that skill in your mind will guide your use of Math when applying AI algorithms.

Math concepts such as linear algebra, calculus, probability, and statistics form the foundation of many AI algorithms and techniques. Understanding these concepts will enable you to grasp the underlying principles of AI algorithms, implement them effectively, and develop advanced AI models. Therefore, dedicating time to studying and practicing math is crucial for anyone looking to excel in artificial intelligence.

AI heavily relies on mathematical principles, particularly linear algebra, calculus, probability, and statistics. Linear Algebra: Concepts like vectors, matrices, and linear transformations are fundamental for representing and manipulating data in AI algorithms such as neural networks. Calculus: Understanding derivatives and gradients is crucial for optimizing AI models through techniques like gradient descent, enabling them to learn from data and improve performance. Probability and Statistics: Probability theory and statistics are essential for modeling uncertainty, analyzing data, and making informed decisions in AI systems.

AI is a rapidly evolving field, so stay curious and keep learning. Follow AI blogs, attend conferences, and explore new technologies to stay abreast of advancements and expand your skill set.

Explore AI Basics: - Supervised Learning: Models learn from labeled data. - Unsupervised Learning: Find patterns in unlabeled data. - Reinforcement Learning: Agent-environment interaction. Neural Networks and Deep Learning: - Neural Networks: Mimic the brain. CNNs for images, RNNs for sequences. - Deep Learning: Many hidden layers; revolutionized AI. Online Courses and Practice: - Coursera, Kaggle, Fast.ai: Learn, build, and collaborate. Remember, curiosity fuels AI mastery! ????

In the field of programming, curiosity leads to a search: how to start programming projects? set clear goals, choose the right tools and get started. it's important to start!

Apply theoretical knowledge through hands-on projects and coding exercises. Platforms like Kaggle, GitHub, and online coding environments offer datasets and challenges to hone your skills and experiment with real-world AI applications.

it is important to learn from everyone, but especially from people who know more than you, who do similar projects and who have had the same problems as you

As you embark on your AI journey, it is important to engage with the online AI community: - Join AI forums like Stack Overflow, Reddit, or specialized platforms. Ask questions, share insights, and learn from others. - Attend virtual events hosted by AI organizations or interest groups. There are many free events. These gatherings offer networking opportunities and valuable insights. - Contribute to open-source AI projects on platforms like GitHub. Collaborate with developers worldwide. - Follow AI blogs, newsletters, and podcasts. Be aware of the latest research, tools, and breakthroughs. - Write blog posts, create tutorials, or make YouTube videos. Engage in discussions. Remember to think on community. Connect, learn, and contribute ??

AI's moving faster than a speeding bullet, with new products popping up left and right. Even if you're glued to your screen trying to keep up, it's like trying to drink from a firehose—totally overwhelming. So, first off, cut yourself some slack. We're all a few steps behind, and that's perfectly okay. Here's how I try to keep my head above water Twitter: All the latest AI chatter is here. Following big names like Yan LeCun, Sam Altman, and Andrew Ng keeps me in the know. The Information: For AI news, these folks have their finger on the pulse. They're my go-to for the latest scoop. Research Papers: Arxiv is a goldmine for the newest research, but it's a jungle out there. I keep up by following researchers on social media.

To master AI, one can study mathematics, algorithms, neural networks, data science, etc. But you have to start somehow. Enjoy it! Test real-world use cases. Explore available solutions. Experiment with existing models. In the '90s, the most accessible way to engage with computer science was through eight-bit machines. Everyone started by playing around on Spectrum, Commodore, or Atari. If someone managed to do more than just complete the Boulder Dash game back then, ten years later they were successfully working in the IT industry. In the third decade of the twenty-first century, AI is accelerating at a similar pace.

As you gain foundational knowledge, consider specializing in a subfield of AI that interests you, such as computer vision, natural language processing, or robotics. Many online resources cater to specific areas.

Mastering AI needs a really structured plan . One must target Foundations of Programming , Deep understanding of Mathematics and then move towards classical Machine learning and then building ability to understand state of the art models. Eventually attention based models before he/she work on large language models like GPT4. Often learners do not understand the importance of Mathematics and they directly jump to implement state of the art models which make their life together when one is asked to tweak model performance. It is very important to understand mathematical approximations of each model.

Find an ai expert who is willing to build a plan for you and mentor you. If you’re a beginner and you try to do it by yourself you will succeed but maybe after spending 10 fold time and 10 fold money. Good luck!