You want to get your first AI job but have no experience. What’s the best way to get started?

I am of the belief that the best way is through your current domain expertise. Say you are in the area of supply chain, with your subject matter expertise, first identify problem statements where you think AI will be able to solve these issues. In parallel, one should learn the basics through recommended MOOCs, and possibly download a small dataset to learn through practice. Being able to demonstrate how you solve the painpoints in your domain with AI will help you build your portfolio projects for showcase.

AI is gaining popularity recently due to significant advancements in compute capacity, but it has existed for a very long time. Here are the 5-areas that are foundational, when it comes to preparing for a career in AI/ML: - Quantitative: Applied Mathematics, Statistics - Computer Science: Coding skills in at least 1 core language, with solid understanding of software development lifecycle - Data Science: Exposure to neural nets, and large language models - Research Mindset: Ability to break down a complex problem, into actionable solution steps - Curiosity: Read, Read and Read esp. papers from organizations at forefront of AI/ML, to keep up to date on the latest advancements in this space

They say AI is the best and perhaps the last invention humans will make. It is a great time to start your AI journey, if you have not started yet. Here are the top three steps that worked for me in my AI journey. 1. Start: Sometime the biggest hurdle is just the first step. Start with being a user. There are a plethora of free applications. 2. Courses: Google and Microsofts' of the world have many free courses and conference you can be a part of to start the journey. 3. Experiment: Don't be scared to experiment. Learning and growth will happen when you step out of you comfort zone.

No AI experience? No worries! Here's your launchpad: -Brain food: Feast on online courses, tutorials, and podcasts to learn the AI language. Think of it as cracking the code to a secret club. -Build your mini-AI world: Start small with fun stuff, like a music recommender or a silly chatbot. Hands-on practice builds confidence. -Join the AI party: Online communities, meetups, hackathons - connect with others, learn from their journeys, maybe even team up! -Resume revamp: Show off your AI skills, even if self-taught! Highlight your projects - they're your golden tickets. -Don't be shy! (Most imp): Apply for entry-level roles and internships. Network, ask questions, and show your genuine passion. Remember, the key is to start.

?? ?????? ???? ?????????? ???? ???????????????????? ???? ?????????????? ?????????????????? 1. ?? Start with basic AI projects like predictive modelling, text analysis or chatbots to showcase coding and AI understanding. 2. ?? Diversify your portfolio with projects in data analysis, machine learning, and NLP to show versatility. 3. ?? Document each project with a README on the problem, approach, technologies, and outcome for clarity. 4. ?? Ensure your portfolio includes code access (via GitHub) and live demos or video walkthroughs for easy assessment. 5. ?? Use real-world data from Kaggle or APIs in projects to demonstrate real data handling skills. 6. ?? Continuously update your portfolio with new skills and projects. 7. Share your work

?? My AI portfolio showcases a diverse range of projects, each highlighting my expertise in applying cutting-edge techniques to solve real-world challenges. From building a chatbot to enhance customer interaction, to developing a face recognition system for enhanced security, and crafting a recommender engine for personalized recommendations, each project demonstrates my versatility and skillset. Leveraging open-source datasets, APIs, and libraries, I meticulously document my code and methodology, providing clear insights into my process. Whether it's sentiment analysis tools for social media sentiment or predictive modeling for financial markets, my portfolio reflects my passion and proficiency in the field of AI.

??The most effective learning method is through hands-on experience! ?? Select a real-world project that interests you and begin applying AI techniques to tackle it. With perseverance, you will quickly gain invaluable experience! ??

There is no replacement for experience. Build a portfolio of AI projects that are a mix of 1) practical 2) quirky and 3) based on your passion. This gives us a good understanding of your work ethic and adaptability.

Theoretical knowledge has its limits - you can gain practical knowledge in AI/machine learning by gathering hands-on experience. The projects you work on to build your portfolio can also be showcased on your resume/GitHub and give you a leg up on your hiring process. Working on personal AI projects can help you decide if you really enjoy working in AI and want to pursue a career on it. ??

Networking and connecting are pivotal for landing your first AI job. Engage in AI-related events, like meetups, webinars, or workshops, to immerse yourself in the community ??. Join online forums and groups, where you can share insights and learn from others ??. Seeking out mentors and influencers can provide valuable guidance and advice ??. These connections offer opportunities to gain feedback on your portfolio and enhance your skills. Additionally, networking can lead to referrals or recommendations, opening doors to job prospects. Actively participating in these avenues allows you to stay updated on industry trends and forge meaningful relationships with professionals in the field.

Networking is essential for breaking into the AI field without prior experience. Attend industry events, join online communities, and connect with professionals in the field. Engage in conversations, ask questions, and share your enthusiasm for AI. Building relationships with experienced professionals can lead to valuable mentorship opportunities, job referrals, and insights into the industry. Don't hesitate to reach out and connect with individuals who inspire you or work at companies you're interested in. Networking is a powerful tool for opening doors and advancing your AI career.

Networking is a two-way street. Be genuine, show interest in others' work, and be willing to offer assistance or share your expertise when appropriate. Building strong connections and relationships in the AI community can open doors to new opportunities and provide valuable support throughout your career journey.

AI advances with big steps. Opportunities to grow in this field appear every day. Networking is key to learn but also to get opportunities. Read, listen, talk, get your hands dirty coding and running tools. Feel inspired by all these references, AI experts. Follow creators, invite to connect. Find mentors in your contacte and be proactive!

How does AI impact fields beyond just algorithms and code? The answer lies in building a diverse network beyond just fellow AI nerds! When you attend a meetup on responsible AI development, you rub shoulders with data scientists who understand the business side, entrepreneurs who see the bigger picture, and even ethicists who ensure AI stays on the right track. It's not just about showcasing your expertise by answering questions on forums; it's about learning from diverse perspectives and building a reputation as someone who thinks beyond the code. And the cherry on top? Finding a mentor, an experienced AI researcher who can guide you, review your projects, and help you navigate the exciting yet complex world of AI.

With a solid skill set and a standout portfolio, start targeting AI job openings that align with your skills and interests. Tailor your application to reflect your most relevant experiences and projects. Utilize job search platforms and tools to enhance your application, ensuring that your resume and cover letter effectively communicate your strengths and potential contributions to prospective employers.

When transitioning to an AI career, research and application are crucial steps ??. Utilize job boards, social media platforms, and company websites to discover suitable AI positions. Tailor your resume and cover letter to emphasize relevant skills, projects, and accomplishments ??. Leverage tools like resume builders and interview simulators to refine your application materials ???. Networking is also vital; engage with professionals in the field and attend industry events to expand your opportunities. Stay updated on AI trends and technologies to demonstrate your knowledge and enthusiasm during interviews. Remember to showcase your portfolio and any relevant certifications to stand out among applicants.

Remember that the AI job market can be competitive, so be persistent and patient. Continue to improve your skills, stay updated with the latest developments, and maintain an active presence in the AI community. With a combination of diligent research, tailored applications, and effective networking, you can increase your chances of securing your desired AI job.

Follow these tips: Tip #1: Speak their language. Research each role and highlight projects that address their needs. For example, when applying for self-driving car jobs, showcase your computer vision and sensor fusion skills instead of sending out generic resumes. Tip #2: Think outside the "AI Job" box. Internships and entry-level roles, even if not labeled "AI," can be your golden ticket. Many companies hide AI projects within these positions, offering a valuable stepping stone. Tip #3: Use LinkedIn and events to connect with company insiders and talk to the people who do the work! Tip #4: Highlight your problem-solving and communication skills, along with finance data analysis experience, when applying for an AI startup role.

Entering the AI field requires persistence. Research potential roles and companies that align with your interests and skills. Tailor your applications to highlight how your unique blend of skills and projects makes you a valuable asset, despite lacking traditional experience. Moreover, don't restrict yourself to job advertisements; speculative applications can sometimes uncover unadvertised opportunities. Keep learning, applying, and iterating on your approach based on the feedback you receive. This process is not just about landing your first job but about continuous growth and adaptation in an ever-evolving field.

To ace your interview, research the company and role, and practice answering common and role-specific questions, highlighting your experiences using the STAR method. Refresh your technical knowledge as needed. Conduct mock interviews for feedback and confidence. Dress appropriately, arrive on time, and communicate clearly. Show enthusiasm for the position, and ask thoughtful questions to demonstrate your interest and engagement.

Preparing for AI job interviews requires practice, especially with technical interviews that include coding challenges, problem-solving questions, and discussions about AI concepts and projects. Utilize platforms like LeetCode and HackerRank to practice coding. Be ready to discuss your projects in detail, explaining your thought process, the challenges you faced, and how you overcame them. Demonstrating your problem-solving skills and ability to learn from experiences is key.

Imagine acing your AI interview, confidently discussing image recognition algorithms and ethical dilemmas. That's the power of preparation! Platforms like LeetCode and HackerRank become your coding dojo, where you master AI-specific challenges relevant to your desired role. But remember, AI isn't just about code. Practice answering behavioral questions – how would you handle an ethical AI dilemma? Explain your teamwork approach to a panel of engineers. And don't forget the soft skills: clear communication is key to explaining complex ideas to non-technical folks. Hone these skills through coding competitions or hackathons. Stay ahead of the curve – follow AI blogs, research papers, and online discussions.

"...Getting your first AI job without any experience might seem daunting, but it is not impossible." While this may be true, I can't think of an easier way to differentiate yourself than to be an AI thought leader. For practical purposes, especially for marketers, this technology is still just over a year old (November 30, 2022, what we refer to as AI Day - when ChatGPT launched.) Anyone who is willing to invest the time and experimentation to become a leading thinker has an excellent opportunity to land an AI job. It reminds me of the internet some 30 years ago.

To break into the AI field without experience: 1. Start by mastering the fundamentals of machine learning and AI. 2. Stay updated on industry trends through online courses, blogs, and research papers. 3. Participate in hackathons to apply your knowledge and build projects showcasing your skills. 4. Networking and contributing to open-source projects can also enhance your credibility.

-Start by gaining a solid understanding of the fundamentals of AI and machine learning. There are plenty of online resources, courses, and tutorials available for free or at a low cost. Focus on learning programming languages such as Python, as well as libraries like TensorFlow or PyTorch commonly used in AI development. -Work on projects to apply what you've learned. Start with simple projects and gradually move on to more complex ones. You can find datasets online to experiment with or participate in platforms like Kaggle where you can work on real-world problems and compete with others. - Attend AI meetups, conferences, workshops, and webinars both in-person and online. - Get involved in open-source AI projects on platforms like GitHub.

To land your first job in AI, it's essential to not only master the fundamentals and build a strong portfolio but also to actively network, engage with the community, and stay informed about industry advancements. Leveraging networking platforms like LinkedIn can be invaluable in connecting with professionals in the field. Rather than directly asking for jobs, focus on presenting your skills and expertise while also offering assistance to others or engaging with startups and organizations in the AI space. By consistently demonstrating your passion and capabilities, you'll increase your chances of being noticed and considered for opportunities in AI.

It always important to remember, consistency and a willingness to learn are key! Start small, don't be afraid to experiment, and showcase your progress through a portfolio or blog. With dedication, you can build the skills and experience to land your dream AI job.

You don't need to have AI in your title to learn, build and test building with and around Gen-AI. 30 min a day for a month of focused time will go a long way. Follow smart people that do what you dream of doing. Look for those that build in public and learn from their experiences. They're gold. Then start small. Look at your day to day and reflect on how you can apply what you've learned. Learn prompting. Make your gpts. Use no code tools. Learn python. Expand towards what's interesting for you. Oh, you didn't get the role (yet)? Keep learning and building. That stays with you regardless. The world is your playground. Use it!

Embarking on a career in AI also means being prepared for lifelong learning. The field is rapidly evolving, with new tools, algorithms, and best practices emerging regularly. Cultivate a mindset of curiosity and continuous learning. Additionally, consider the ethical implications of AI work. Understanding and advocating for ethical AI practices is increasingly important as AI becomes more integrated into our lives