What do you do if you want to learn AI as a beginner?

I am not sure about the technical points, however, if you are coming from a non-technical background, there are great resources out there. The first step is to solidify your grasp of fundamental concepts, such as artificial intelligence principles, machine learning, and deep learning. Platforms like Coursera offer introductory courses designed to ease you into these topics. Afterwards, if you would like to learn more on the AI governance and policies, I would suggest BlueDot Impact courses and Centre for AI and Digital Policy resources. When you want to learn about standardisation, I suggest you check ForHumanity and Babl AI certificate courses. Moreover, don't forget to engage with organisations' slack channels such as All Tech is Human.

Start with beginner-friendly resources like online courses or books. Don't forget to tinker with small achievable projects – it's like learning to swim by jumping in the pool but not at the deep end. Finally, embrace the journey! Every glitch is just a funny story waiting to be told.

Focus on practical experience by starting with traditional models such as linear regression, decision trees and NN. Prioritize understanding and accurately modeling the problem at hand, as this foundational skill is crucial. Deploy your models early and often, as the majority of real-world challenges and learning opportunities arise during this stage, giving you an edge over other practitioners. Once you have a solid base of problem modeling and basic techniques, you can progress to more complex models like deep learning. This step-by-step approach ensures a strong foundational understanding before tackling advanced AI concepts.

There are a lot of solutions masquerading as 'AI' these days. Start with a quick primer on the fundamentals of machine learning and artificial intelligence to understand the difference between true AI and other technologies branded as AI. Once you have a solid grasp of the basics, you can explore different branches like computer vision, natural language processing, robotics, etc. based on your interests. Start with online courses, books, and tutorials from reputable sources to build a strong foundation. Hands-on projects and participating in online communities can also accelerate your learning journey.

AI is a vast ocean. To navigate it: 1. Start with the fundamentals → Algorithms, data structures, machine learning basics. Build your AI toolkit gradually. 2. Explore free online resources → Courses, tutorials, blogs. Grasp concepts without getting overwhelmed. 3. Identify your niche → Computer vision, NLP, robotics? Dive deeper based on your interests. 4. Hands-on projects accelerate learning → Apply concepts to real-world problems. Join online communities for support. 5. Understand AI's potential impacts → It's not just tech; it's a transformative force. Stay informed on ethical and societal implications. ? With a strong foundation and focused exploration, you'll be well-equipped to navigate the complex yet rewarding world of AI.

Online coursers are my favorite source of learning. In the case of AI, I usually use Coursera, Udemy and YouTube for learning about algorithms, methods, theory and applications. Give preference to courses where you can learn by example and with practical hands-on.

Online courses provide structured AI learning paths covering data science, machine learning, neural networks. Many foster hands-on learning via projects and community forums. However, true mastery stems from applying knowledge beyond curricula - actively tackle real-world challenges stretching skills. Prioritize courses encouraging this exploratory mindset, bridging theory and practical expertise. Enrich online journeys by combining guided instruction with self-directed problem-solving, pushing boundaries. With dedication, online education catalyzes transformation from novice to proficient AI practitioner.

There are so many options out there to learn AI, you really have the chance to find the kind of courses that really works for you. Try a couple and follow the one that is best for your learning type! Here are some free resources to try out: - Youtube has a bunch of free Stanford full courses - you can tried out Coursera courses for free - Deeplearning.ai also offer great ones

I found the introductory course "AI for Everyone" by Andrew Ng an incredibly helpful entry point into everything AI. There are some challenging concepts but it is explained in a very easy to understand manner. Another recommendation I have is to complement formal learning with practical application and it will supercharge theory learning and aid recall. Build an Auto GPT, play around with applications, build AI into your existing work life. Lastly, ideally seek courses which have a community aspect (eg a forum where you can ask and view questions)

Online courses offer a structured path to learning AI. But the real value lies in applying these learnings to real-world scenarios. Don't just passively consume content; challenge yourself to solve problems outside the curriculum. Seek courses that encourage this out-of-the-box application, as it is essential for bridging the gap between theoretical knowledge and practical expertise.

I think experimenting with the different forms of AI models is crucial. Some algorithms may be further along than others. For example, the image generators makes it easier to identify the issues in a model such as too many fingers or misalignment of the eyes or misplaced objects. It's not as easy to spot defects in data. Where text generators may be subjective and less error prone, data anomalies may take more effort to identify. Understanding image distortions enable one to see that even though things appear normal at first glance, attention to the details are critical to ensure proper model training and accurate outcomes.

Going through tutorials, reading documentation, or doing a course helps in a limited capacity. Unless you dirty your hands trying to implement a project for a specific use case, it is difficult to understand and retain all the information consumed in the process. Starting with a small proof of concept will also give you confidence in the skills that you are learning and helps create a portfolio too.

There are many ways a beginner can get started, and there are tons of resources on GitHub, Microsoft Learn, Amazon Web Services, GCP, and courses on Udacity, Coursera, and DataCamp to get started. In your choices, try to prioritize hands-on AI projects rather than spending too long dabbling in theory.

I have found practical projects indispensable for a deeper understanding of AI concepts. While theory and coursework lay the essential groundwork, building something helps me identify the knowledge gaps. As a beginner, I recommend starting with simple projects to implement basic AI techniques like data preprocessing, model training, and evaluation. As you progress, you can take on more complex challenges in areas of natural language processing, feature extraction, and deploying machine learning models. To really push the envelope, you can even open-source your project on GitHub.

Aim to solve real problems you're passionate about. This personal connection not only keeps you motivated but also ensures you're learning AI in a context that matters. The process of applying AI to areas of personal interest or societal need can provide deeper insights and a more fulfilling learning experience.

I would recommend to learn from others in the community, a lot of AI tools have different communities where people share prompts, tips and what they create. I would recommend that as it will keep you inspired and not get bored. ??, Discord and LinkedIn has such communities.

Immersing yourself in AI communities provides invaluable support and growth opportunities. These groups, found on social media, forums, and local meetups, connect you with experienced professionals willing to share insights. Engaging with like-minded individuals keeps you informed on the latest trends while expanding your network. Communities offer a collaborative space to ask questions, discuss challenges, and receive guidance - accelerating your learning journey. Embracing these supportive ecosystems allows you to learn from diverse perspectives and form meaningful connections vital for sustained success in the dynamic AI landscape.

Become part of AI communities. Platforms like GitHub, Reddit, and Stack Overflow host vibrant communities of AI enthusiasts and professionals. Participating in these communities can provide support, inspiration, and opportunities to collaborate on projects, helping you grow and stay motivated in your AI learning journey.

Joining AI communities can supercharge your learning journey. These spaces are packed with enthusiasts, professionals, and experts sharing insights, challenges, and solutions. Platforms like Reddit, Stack Overflow, and LinkedIn groups offer vibrant forums for discussion, advice, and networking. Participating in hackathons or online challenges can also boost your skills and connect you with like-minded learners. These communities provide a supportive environment for asking questions, sharing projects, and staying updated on AI trends. Plus, the connections you make can open doors to opportunities in the field.

For practitioners, which most of us are, AI is a tool and it's new so everyone is firstly figuring out how to use it, secondly what it can be applied to effectively, and thirdly how to govern it. Thus communities are great for people with similar interests and stages to come together to share ideas and bring benefit to each other. It also helps with networking and building relationships in the industry given everyone there has a common interest or purpose, and just as importantly, these could lead to potential business or collaboration opportunities. You never know what AI can bring. Try it + connect with me ??

Books offer in-depth and comprehensive AI learning opportunities beyond online courses. Carefully selected texts, especially those beginner-friendly and recommended by professionals, provide thorough overviews and specialized topic explorations. Combining introductory and advanced readings fosters a well-rounded understanding. Books' dedicated focus allows concepts to be explored more extensively than digital formats, facilitating conceptual mastery. Supplementing hands-on practice with literature insights cultivates a robust knowledge foundation vital for navigating AI's complexities confidently. Leveraging books alongside interactive resources equips learners with the breadth and depth needed to thrive in this dynamic field.

Learning AI as a beginner through reading books can be a great way to build a solid foundation in the field. Here's a curated list of books suitable for beginners: 1. Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell. 2. Artificial Intelligence: Foundations of Computational Agents by David L. Poole and Alan K. Mackworth. 3. Python Machine Learning by Sebastian Raschka and Vahid Mirjalili. When starting with AI as a beginner, it's essential to choose books that align with your level of understanding and learning preferences. Additionally, supplementing book learning with practical exercises, online tutorials, and hands-on projects can further enhance your understanding and skills in AI.

Reading books is great for deep learning about AI. They give detailed insights on topics and are more thorough than online resources. Choose beginner-friendly books recommended by AI experts. A mix of basic and specialized books helps build a broad understanding of AI.

If books is your thing, then read them, otherwise interactive chats with ChatGPT or Claude will be able to help you learn about AI and ask questions to deepen your understanding much quicker and poignant than a static book ever could.

As a beginner looking to learn AI, start by reading books on fundamental concepts and practical applications. Dive into introductory texts that cover topics like machine learning, data analysis, and neural networks. Familiarize yourself with programming languages such as Python and R, essential for implementing AI algorithms. Additionally, explore online courses and tutorials to supplement your reading with hands-on learning experiences. Don't hesitate to join AI communities and forums to connect with experts and fellow learners, exchanging insights and resources. Continuous practice and experimentation will solidify your understanding and proficiency in AI technologies.

Take ChatGPT4 with you for a day. Ask it everything that comes to mind (no uploading confidential data). This includes everything like: - "give me a backgrounder on <insert customer name> and highlight their potential pain points" - "Explain what the heck Waldorf and. Montessori schools are" - "Summarize these three reports and give me five smart questions to ask about them" - "Create a ten-question survey to understand a customer's needs for <insert your interest>" - "Use the non-violent communication framework to deal with this difficult client. <describe the challenge and conflict" - create a silly, personalized birthday image for a friend - "create a custom cocktail to celebrate women history month" Enjoy. Be curious

Like any new thing, start by getting comfortable with your surroundings. Start in play mode, driven by curiosity, before moving into learn mode, followed by experiment mode, which ultimately leads to business mode. But the most important thing is add some sort of AI interactions to your daily habits. Familiarity and repetition will be your key to unlocking progress.

Embracing experimentation is vital in AI. My journey has shown me the value of experimenting with algorithms, adjusting parameters, and understanding their effects on outcomes. This practical exploration deepens our grasp of AI, turning theoretical knowledge into actionable insights. Remember, in AI, trial and error are more than steps towards success—they're essential learning tools. Each challenge or mistake is an opportunity to learn and improve. My work with AI Advantage Agency has reinforced this: breakthroughs often emerge from unexpected trials. So, let's keep experimenting and learning from every outcome.

I would be curious and experiment a lot especially with things you are familiar with as that will help you understand the downsides which are hallucinations and fact checking.

I actively pursued learning AI through various online courses like Coursera ML and Udemy's OpenCV, along with the Michigan Computer Vision program. However, I craved practical application. While I used these skills at work, I yearned to expand my knowledge. Discovering Kaggle was a game-changer. It provided a platform to tackle real-world challenges, explore new trends and model optimizations through others' work, and continuously learn from the community.

To learn AI as a beginner: 1. Start with basics. 2. Take online courses. 3. Practice coding in Python. 4. Read beginner-friendly books. 5. Join AI communities. 6. Work on projects. 7. Stay curious and persistent.

Stay up-to-date! AI is a rapidly evolving field. Follow AI researchers, read papers and blogs, attend meetups or online courses to keep learning. The key is to start and stay curious, be open to learning new things in this exciting and rapidly-evolving field.

If you're a beginner eager to learn AI and excel as technical wizard, start by grasping foundational concepts in machine learning, statistics, and programming languages like Python. Online courses from platforms like Coursera or edX offer structured pathways. Pick a specific AI vertical, such as natural language processing or computer vision, to focus your learning. Engage with projects and communities to apply what you've learned, and keep updated with the latest AI research and developments.

In addition to books and courses consider the following subscribing to AI newsletters Listen to AI YouTube channels Follow AI content creators The above will keep you connected to the most up to date topics and news items. Once you have a base of exposure start to think about where you want to focus your interests in AI. That will determine what added steps to take.