Here's how you can kickstart your self-learning journey in AI as a beginner.

I would kick off my self-learning journey in AI as a beginner by starting with learning the foundation of data on free-to-low cost learning platforms such as Coursera, EdX, and Udemy. I would learn SQL, one data visualization tool, Python or R, and machine learning and model lifecycle. Then I would start to go into advanced AI concepts. However, I would start with learning and understanding data because Trusted AI starts with Trusted Data.

Set Clear Goals: Define what you want to achieve in AI, such as building a specific project, understanding a particular algorithm, or pursuing a career in AI. Start with the Basics: Learn fundamental concepts in programming (Python is popular for AI), mathematics (linear algebra, calculus, probability), and statistics. Resources like Khan Academy and Codecademy are great for beginners. Take Online Courses: Enroll in online courses from platforms like Coursera, edX, or Udacity. Notable courses include Andrew Ng's "Machine Learning" on Coursera and "Deep Learning Specialization."

To kickstart your self-learning journey in AI, it's essential to first understand the basics. Familiarize yourself with foundational concepts such as machine learning, neural networks, and data science. You can start with introductory courses or tutorials that cover essential topics like supervised and unsupervised learning, classification, regression, and basic statistics. Grasping these core principles provides a solid foundation upon which you can build more advanced knowledge, making it easier to understand complex AI topics as you progress.

My AI journey started with a whole lot of curiosity and trying to solve real world problems. I'd start with online courses like those on Coursera or edX, focusing on the basics like Python programming, machine learning, and entry level data science. The most important thing though is to dive into hands-on projects and apply what you learn. Follow AI blogs and AI experts on places like LinkedIn and check out research papers to stay up to date. Join online communities and forums to network with others and get support. Stay curious and always keep experimenting and building - practical experience is crucial.

Apart from the basic LinkedIn Learning micro courses and introductory YouTube videos, I highly recommend creating a paid account on OpenAI's ChatGPT and developing your own AI Assistant or a custom GPT. Free GPTs are great, but custom GPTs can elevate your learning and productivity to the next level.

Choosing the right learning resources is critical for building a strong understanding of AI. Prioritize comprehensive, high-quality materials that include practical applications and stay current with industry developments. This strategic approach will make your self-learning journey in AI more effective and rewarding. This approach helps on many fronts 1/Foundation Building 2/Depth Over Breadth 3/Practical Application 4/Interactive Learning 5/Right mentorship 6/Updated content

Selecting the right resources is crucial for a successful self-learning journey in AI. Look for comprehensive online courses, tutorials, and books tailored for beginners. Make sure they cover key AI concepts and provide practical examples. Think of Bender from Futurama: while he's a humorous character, imagine if he had to learn AI from scratch. He'd need quality resources that break down complex ideas into manageable chunks. It's beneficial to choose resources that offer hands-on projects, as applying what you learn reinforces your understanding. Remember, the quality of the resource is more important than the quantity; it's better to deeply understand a few materials than to skim through many.

With the wealth of AI resources out there, it can be overwhelming trying to figure out where to start. The key is being selective and choosing quality materials that align with your learning goals and style. Do you prefer video tutorials, online courses, books, or hands-on projects? Maybe a mix of different formats works best for you. Identify reputable sources from respected institutions, industry experts, or good online platforms, don't just go for the cheapest or trendiest option - invest in resources that provide up-to-date content, practical applications. Remember, resources are just tools - the real learning happens when you actively engage, take notes, & apply what you're studying through practice exercises or personal projects.

One of the most helpful resources for learning about AI in my experience is simply entering a few prompts in an LLM like ChatGPT or Gemini. I spend a lot of time listening to lectures and podcasts while doing other tasks like cleaning up the kitchen. It is also helpful to watch a few YouTube videos because the visuals can be quite useful. I also have some select individuals here on LinkedIn that provide me with a wealth of knowledge on AI.

Building a digital library of text and video resources about AI, free publications for reading, I can also benefit from video resources that offer visual explanations such as watching an animation that explains how a machine learning algorithm "learns" from data, followed by a text-based explanation. Also, free online courses with a community aspect, online courses offer forums or discussion boards where beginners can connect and share their learning experiences. Also, connecting with experts and influencers on LinkedIn suxh as Fengchun Miao, UNESCO Headquarters - Chief, Unit for Technology and AI in Education, and Mike Tholfsen Principal Group Product Manager - Microsoft Education.

If you haven't written a line of code yet, it's time to start ??. With AI hype and "AI washing" everywhere, even a bit of hands-on coding experience will set you apart from the sea of overnight AI experts ??. This practical knowledge not only deepens your understanding but also enhances your ability to communicate effectively with stakeholders, whether they're technical or non-technical, senior or entry-level ???

You don't have to code if you don't want to. Having the right idea for what problem the AI will help to solve is more important. Then you can always look for a tech partner to help code it or learn one of the many no-code tools out there to build your prototype. If your goal is to write code, then go ahead and practice coding. :)

Understanding the basics is crucial before embarking on a self-learning journey in AI. Start by acquainting yourself with fundamental concepts such as machine learning (ML), a subset of AI that enables machines to improve from experience. Explore different types of machine learning like supervised, unsupervised, and reinforcement learning. Grasp the key algorithms and how data is used to train these models. Think of a Sith holocron: though a fictional artifact from Star Wars, it holds deep knowledge akin to ML, allowing for learning and adaptation to new situations. This foundational knowledge will lay the groundwork for your AI education and make advanced topics more accessible.

Python and other open source libraries like pandas, pytorch, tensor flow, are essential for anyone aspiring to work in AI. A structured approach to learning, regular practice, and applying knowledge through projects, coupled with persistence and community support, will significantly enhance your programming skills and contribute to your success in the field of AI.

Top 3 ways: 1: Hands-On Projects: Start with small projects like building simple machine learning models using datasets from Kaggle or UCI Machine Learning Repository. Gradually tackle more complex projects as you gain confidence. 2: Coding Platforms: Use platforms like LeetCode, HackerRank, and Codecademy to practice coding challenges focused on AI and data science. 3: Open Source Contributions: Contribute to open source AI projects on GitHub. This not only improves your coding skills but also exposes you to real-world applications and collaborative coding practices.

.Why Projects Matter Reinforcement of Learning: Working on projects allows you to apply theoretical knowledge in practical scenarios, reinforcing what you've learned and deepening your understanding. Skill Development: Projects help you develop problem-solving skills, learn to debug effectively, and understand the intricacies of AI tools and libraries. Portfolio Building: Completed projects serve as tangible evidence of your skills and capabilities, which can be showcased to potential employers or collaborators.

- Work on Projects - Application: Projects are a powerful way to learn AI. Start Simple: Begin with projects that match your skill level, like a recommendation system or chatbot. Progress Gradually: Tackle more complex problems as you gain confidence. Consolidate Learning: Projects reinforce your knowledge and understanding. Build a Portfolio: Showcase your work to enhance future opportunities in AI.

One of our significant projects is DOER V3, designed to empower small businesses globally through AI. The platform includes features like AI-powered onboarding, strategic advice, and daily briefings, aimed at enhancing productivity and decision-making. This project has been instrumental in refining our understanding of how AI can be applied practically to solve real-world problems, particularly in emerging markets where small businesses often lack access to advanced tools.

To truly grasp AI, it's essential to transition from theoretical knowledge to practical application, much like transforming a concept sketch into a tangible structure. Working on projects allows you to solidify your understanding and see firsthand how AI can enhance architectural and design processes. Start with small, manageable projects, incorporate AI into your design work and experiment with generative design tools to explore new forms. These hands-on experiences will not only deepen your understanding of AI but also reveal innovative ways to integrate it into your professional practice, ultimately pushing the boundaries of what we can achieve in architecture and design.

??If you don't know where to start, copy seniors or mentors' strategies initially. ?? Join communities like online forums, social media groups, and local meetups to connect with experienced individuals. ??Engage actively, ask questions, and participate in discussions to accelerate your learning in AI.

Taking on the AI learning journey alone can be daunting. Join online AI communities to connect with like-minded individuals and get support. Some example of communities Forums and Discussion Boards: Platforms like Stack Overflow, Reddit (e.g., r/Machine Learning), and AI-specific forums provide spaces to ask questions, share insights, and engage in discussions. These forums are excellent for troubleshooting specific issues and getting diverse viewpoints. Webinars and Online Conferences: Attending webinars and online conferences exposes you to the latest trends and research in AI. Organizations like AI4ALL and Data Science Central often host events that bring together experts and enthusiasts.

Kickstarting your self-learning journey in AI as a beginner can be incredibly effective by joining communities. From my perspective, engaging with AI-focused groups offers immense benefits. For example, platforms like Kaggle provide a hands-on approach through competitions and datasets, allowing you to apply what you learn in real-world scenarios. Additionally, joining AI Slack channels or Discord servers can connect you with like-minded individuals for advice and collaboration. These communities offer support, mentorship, and access to resources that can accelerate your learning and keep you motivated.

The journey to mastering AI doesn't have to be a solo endeavor. In fact, connecting with others can be a game-changer for your learning, seek out online communities, forums, and meetup groups centered around AI and machine learning, engage with like-minded individuals, ask questions, and learn from their experiences, share your own projects and get valuable feedback. Attend webinars, workshops, or conferences to expand your knowledge and network, being part of a community provides endless opportunities for collaboration, mentorship, and staying updated on the latest AI trends and developments.

Don't go it alone! Join online AI communities to connect with learners and experts. Forums, webinars, and discussions offer valuable insights and help you overcome challenges. These groups share resources, job openings, and encouragement. Networking can even lead to mentorships and collaborations to boost your AI journey.

Keeping up with the trends. The AI field is in a state of constant evolution. A new application of AI comes every day. Keeping up with AI organizations and Top Voices in the field will help you create a journey path for self-success and inspires you along your journey.

As you progress, start specializing in areas of AI that interest you the most, such as computer vision, natural language processing, or reinforcement learning. Take advanced courses in your chosen specialization to deepen your knowledge. Earning certifications can also add credibility to your expertise. Additionally, read research papers to stay updated on the latest advancements and methodologies. By diving deep into specialized fields and staying informed about cutting-edge research, you'll enhance your expertise and be better prepared to contribute to innovative AI solutions.

Following AI Leaders: Follow AI researchers and industry leaders on Twitter and LinkedIn. Conferences and Meetups: Attend AI conferences like NeurIPS, ICML, and local meetups.

The AI landscape is constantly shifting; yesterday's cutting-edge can quickly become outdated. To stay ahead of the curve, make a habit of regularly consulting trusted news sources, industry publications, blogs, and social media, attend webinars, conferences, and courses covering emerging AI techniques and applications. Most importantly, don't just consume information - experiment hands-on with new tools and methodologies yourself, maintaining an upto-date, growth-oriented mindset is essential for ensuring your AI knowledge and skills remain relevant long-term.

Top 3 ways: 1: Follow Industry News: Subscribe to AI news websites and newsletters like AI Weekly and Towards Data Science. These sources provide the latest research, trends, and developments in AI. 2: Read Research Papers: Regularly read AI research papers on platforms like arXiv and Google Scholar. This helps you stay informed about cutting-edge advancements and methodologies. 3: Attend Webinars and Conferences: Participate in AI webinars, workshops, and conferences. Events hosted by organizations like IEEE and NeurIPS offer insights from leading experts and foster continuous learning.

1. Understand AI basics. 2. Learn Python and libraries like NumPy, Pandas, TensorFlow. 3. Study linear algebra, calculus, probability, and statistics. 4. Take online courses on Coursera, edX, Udacity. 5. Work on hands-on projects. 6. Read books and research papers. 7. Join AI forums and communities. 8. Stay updated with AI trends and technologies.

I just developed an AI copywriting tool and I conduct research daily to stay up on the latest and greatest advances. Updates hit almost daily so the best way to kickstart your journey is to ABL - always be learning.

For legal professionals, here are some elements you should consider if you plan on steering your career in AI: 1- The field is rapidly evolving, so you must be dedicated to keeping up with trends and advancements. 2- Some jurisdictions have already adopted AI laws, others are in the process of adopting AI laws, and many use a complex patchwork of statutes to deal with AI. You should keep up with AI laws and legislative efforts in various jurisdictions. 3- You must be comfortable dealing with uncertainty when dealing with your clients. There is no cookie-cutter approach to solving AI issues. You'll need to work with your clients closely to find the right, ethical, and sensible approach.

I would restructure this article. The first step needs to be how to align your learning to a real world use case. Your "self learning journey" will wildly differ if you need to use AI in a Data Insight capacity as opposed to adding AI to your toolbox as a backend or even frontend developer. The current discourse on AI is riddled with so much inaccuracy and misunderstandings that we need to be more clear in how we communicate what it is, what it means to people and how they can adapt to it, learn and use it to better their businesses and life in general.

Remember the importance of ethics in AI. As you learn, consider the implications of your AI solutions. Also, explore Azure's AI ethics and governance to ensure responsible AI development.