You’re considering a career in AI. What’s the best way to get started?

One of the very essential skill (and probably underestimated one) is Mach skills. It is important to consider that in fact mathematical analytical thinking brings many options for us while developing some model and giving decision. It is quite necessary especially when you have many options. Understanding patterns in data and being able to predict best working model in advance saves the time, energy and computational resources. Therefore, mathematical thinking plays an important role at this point.

Build a Strong Foundation: - Master math, stats & coding. One can check out all the latest courses offered by Google and Microsoft. Specialize in Machine Learning: - Become an algorithm and framework pro. Engage in Real world Projects: - Apply your skills hands on. Stay Updated: - Keep pace with the latest research. Develop Problem Solving Skills: - Tackle tough AI challenges. Network: - Connect with industry experts. Contribute to open source Projects: - Boost your profile with AI contributions. Continuous Learning: - Take advanced courses and join competitions. Explore Different AI Domains: - Discover AI applications across industries. Stay Adaptable: - Be ready to learn and evolve in the fast moving AI world

The best way to enter the AI world is to be passionate. Every day we have new tools, new technologies, and new things to learn and test. You only can test or work with this if you love this.

In addition to the fundamental concepts mentioned, I would strongly recommend diving deeper into specific languages and tools that are widely used in AI. Like Python and SQL is a must, but it's also crucial to master libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn for data manipulation and analysis. Understanding machine learning frameworks like TensorFlow or PyTorch would be highly beneficial as well, given their extensive use in creating AI models. SQL is equally critical for handling databases. Lastly, don't overlook the importance of getting comfortable with Linux and command line interfaces as they are widely used in managing servers and cloud based platforms.

While a robust foundation in mathematics, statistics, and programming is undeniably advantageous, mastery of every aspect of AI is not a prerequisite for starting a career in AI. If you're among those who find themselves lacking in these areas, you should know that your AI journey doesn't demand perfection. The reality is that even seasoned professionals need to continuously learn new concepts to stay relevant. Rather than waiting until you possess an encyclopedic understanding of every math concept, algorithm, or programming language, embrace a mindset of continuous learning. Start an AI project that you find interesting and take advantage of the myriad of free resources at your disposal to increase your expertise step by step.

Choose the right AI domain: 1. Identify interests and skills 2. Assess strengths 3. Research current applications 4. Consider future trends 5. Seek guidance from professionals 6. Experiment with projects or internships 7. Stay adaptable to new opportunities

Think about AI like a superpower that you can use in different areas. If you love helping people, you could dive into healthcare and see how AI can make medical stuff better. Or, if you're into making cool stuff, you could explore AI in art and see how it can help create music, pictures, or stories. Find what excites you the most, and that's your domain!

Selecting a domain that aligns with your skillset involves understanding the contrast between conventional AI and generative AI. If you possess robust mathematical and coding abilities, conventional AI, primarily focusing on supervised learning, may be your preference. Alternatively, consider Generative AI. This field, being relatively young, facilitates quicker adaptation. Moreover, there's a notable surge in the utilisation of Large Language Models (LLMs) APIs, such as those offered by OpenAI and Google Gemini. Consequently, tasks like data labeling and model training, traditionally central to conventional AI, diminish in importance. Instead, attention shifts towards areas like Prompt Engineering, Data Embedding, RAG, and Fine-tuning.

I would second Michael A. Alicea. The biggest hurdles today for AI projects are Legal and Info Security. The starting assumption is that the risks of sending confidential data to open models are not well quantified. And, proprietary models have neither the budgets, expertise, data, or computing power to command confidence that public-facing AI solutions can be relied on under normal use, let alone remain safe from adversarial attacks. Programming skills (python, prompt engineering, ...) and data modeling skills are the starting point, but to stand out you need to have an understanding of and strategy for compliance, security, data privacy, and bias.

An alternative way to look at this is instead of vertically slicing the domains into industries, consider horizontal slices such as, Regression, Clustering, Image Analysis, Natural Language processing. Knowing these more horizontal aspects means you can move around between several industries which can broaden your understanding and puts more tools in your toolbox. You'll be able to draw parallels between various industries to help come up with solutions that have more versatility.

Steps for enhancing AI skills: 1. Start with building chatbots to learn NLP and ML basics. 2. Progress to sentiment analysis for a challenge. 3. Use Kaggle and GitHub for resources and collaboration. 4. Solve real-world problems to showcase expertise. 5. Document work thoroughly for clarity and future reference. 6. Seek feedback to improve skills. 7. Stay updated on tools and trends for better career prospects.

Think of building projects like crafting cool stuff with your AI skills! Start simple with fun things like making a chatbot that talks to you, a face recognition app that knows your friends' faces, or an AI for a game that can play against you. Then, challenge yourself with bigger projects like analyzing feelings in text, suggesting cool stuff to buy, or even making a car drive itself! Use online tools like Kaggle or GitHub to find ideas, code, and share your awesome creations with others!

While starting with simple projects is a great idea, I suggest focusing on projects that align with real world problems. This approach not only challenges you but also makes your portfolio more appealing to potential employers. Like, you could work on a predictive model for credit risk analysis if you're interested in finance, or a deep learning model for disease detection using medical images if healthcare is your field of interest. I also recommend contributing to open source projects. This can help you understand large codebases, learn from seasoned developers, and also improve your coding skills. It's also a great way to give back to the community and make connections within the industry.

When venturing into the competitive game of AI job hunting, having a portfolio of developed projects can be your secret weapon. Beyond qualifications on paper, tangible projects demonstrate your practical skills and problem-solving ability. Employers crave evidence of your capabilities, and a well-curated portfolio provides just that. Showcasing your projects not only validates your expertise but can also spark meaningful conversations during interviews.

Kaggle is a great resource for building projects. For example, they have a competition called "Titanic - Machine Learning from Disaster." Basically, you're using a machine learning model (pre-built) to predict how many passengers survived the Titanic. It's so much fun! Kaggle designed it specifically for beginners who want to get hands on practice in machine learning. Also, Kaggle has a discord where you can socialize with other data scientists, access job postings, career discussions, resources, and other competitions. Try it out and feel free to share you projects with me. I'd love to see them. Have a lovely day.

Some of the great ways to learn anything including AI is follow news, articles and discussion forums. If there are some interesting meetups happening, then joining is a great way to connect like minded people. I have personally benefited from attending meetups a lot.

Think of networking and collaborating like making friends who love AI as much as you do! Join online hangouts like Reddit, Stack Overflow, or LinkedIn where you can chat, ask questions, and learn from each other. You can also go to online or real-life events like webinars or meetups to meet cool people who can teach you stuff and maybe even become your mentors! By connecting with others, you'll get feedback, support, and maybe even find awesome new opportunities!

Going to events, user groups, and getting involved with online communities is crucial to really gaining an understanding of the landscape. You will know all of the rules of the game from the books, but knowing how they are applied, and when to break them requires interaction in the AI community.

Consider leveraging local and virtual hands-on events as a valuable avenue for growth and connection. These events not only offer a valuable opportunity to solve real-world problems using AI technologies but also facilitate direct interaction with professionals and enthusiasts in the field. By participating, you can showcase your skills, learn from others, and possibly attract the attention of mentors and employers looking for emerging talent. These events can often result in forming lasting friendships and partnerships and gaining practical experience that is highly regarded in the tech community. LinkedIn offers AI events that are beginner-friendly, to help you get started, making events an excellent option for those new to AI.

In any pursuit, whether it's AI or any other field you're considering for the future, mastering the fundamentals is crucial. Just like building a sturdy foundation for a building, understanding the basics lays the groundwork for success. It's not about rocket science; it's about dedicating yourself to grasping the essentials. By focusing on fundamentals, you'll pave a clear path forward, ensuring a solid understanding and progress along the way.

Think of learning AI like keeping up with the latest gossip or trends. Stay in the loop by following blogs, podcasts, or social media accounts that talk about cool AI stuff. If you're really into it, you can take more advanced courses or even get certified in AI. And don't worry about messing up sometimes – it's all part of the learning journey! Just keep exploring, trying new things, and staying curious, and you'll keep growing in the exciting world of AI!

"Once you stop learning, you start dying." In my opinion, the pursuit of knowledge is a lifelong endeavor, particularly in a field as dynamic as AI. So, embrace the changes, enjoy the process, and let your passion for learning be the guiding force on your career path in AI.

With the rapid advancements in the field of AI, it is crucial for AI enthusiasts to stay up to date with the latest trends and innovations. I would definitely recommend engaging with different learning resources such podcasts, articles, or even social media platforms to stay informed about the latest technologies. Nonetheless, research plays a vital role in understanding the foundational principles of AI. Discipline yourself, even for 15 minutes a day, to learn something about AI and I guarantee that you will progress and become better at AI.

In any field of tech, keeping up with the fast-paced progress is essential. However, this is even more the case with AI. At the current stage of AI blooming, there is so much happening that keeping yourself in the loop takes dedicated effort. Blogs, social media accounts, and newsletters are the primary sources of keeping a tab on the new technologies. AI has essentially two sides you can follow: 1. The market trends and what the big tech is doing in the AI battle. (the drama) 2. The research and what is being developed at the moment. So while choosing resources to learn and follow AI, keep in mind what aspect of it you want to focus on.

A typical mistake to make when getting started in AI is to skip the fundamentals. It is very tempting to get hands-on straight away and start building models on datasets. And maybe that is a good thing to do first - just to understand what it is like. But after that, please quickly dive into some of the fundamental theories behind your flavor of AI. The flavors can be roughly divided into predictive AI and generative AI. The fundamentals of predictive AI can be best studied by understanding how classifiers work in a 2D space, and how you measure their performance. For generative AI, it's important to understand what transformer models do, and what embeddings mean. Good luck!

Very important is to lose your illusions about AI fuelled by media, marketers, sales people and others. AI is not answer for everything and not every task can and should be automated. Why? Because we are at the beginning of AI transformation and some of the tasks are for AI hard to accomplish and thus it could be very costly and even risky to use AI. Don’t look at AI as solution for all problems but instead make sure you do proper assessment what you should and shouldn’t automate. AI is not a magical black box that can do everything but it’s a technology that like any other needs to be use and implemented correctly.

Curiosity! Everyone is curious about it and experimenting with different ways AI can be applied is really essential. The application piece is critical, can you look at a business and understand where AI could actually add value? That will really help you stand out And remember, everyone is new to this so you have as much of a chance as most!

While this may not be a popular opinion, AI might very well be the next .com bubble. Everyone is an AI startup, and getting into the AI groundswell. Not everyone will make it in the industry. Don't overlook learning how the tier one IT companies build and deploy AI products like Microsoft, AWS, and Google. While they may not be popular amongst the free internet, they are the 800 pound gorillas and they will dictate how much of the industry evolves through sheer mass if nothing else.

We are in the early stages of AI and personal R&D is incredibly valuable. Not one person, team, or Org is a master of this domain and there is room for your individual discovery.