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Are you starting a career in AI? If so, then you’re about to embark on an interesting journey into a whole new world of technology. Here are seven steps to take that will help get you started: Learn the Basics of AI: Get familiar with the core concepts and basic terminology of this field. Some of those subsets include machine learning, deep learning and natural language processing, among other areas. Online vendors like edX, LinkedIn, Coursera and Udacity offer great courses, at reasonable prices, and at very convenient times. Develop Programming Skills: Python is the most widely used application in AI circles, but close behind is Java, C++ and R. And if programming is not your thing, then focus on data analysis, big data, deployment of AI technology, or any of a variety of other fields that support the implementation of this technology. Get Practical Experience: There are a billion opportunities to participate in AI-related projects – Meetup groups, hackathons, competitions, and even open-source projects. The  experience that you will glean from that work will serve you well and might just open the door to an amazing job opportunity and career path. Pursue Certifications: AWS, Google and IBM are just a few of the vendors that offer certifications in widely used AI applications and programs. Never stop learning!: AI technology is evolving at the speed of light, so keep up on related developments and trends in this even changing world of technology. And be sure to subscribe to and follow trending authors and orgs that track AI development. Network, network, network!: Connect with professionals in the AI field whether virtually or in-person and via live events. You never know when a friend might spot a rare opportunity for you that could make your career. Apply for AI Jobs: Apply for entry-level AI positions or internships. That’s a great place to start your journey and discover the AI specialization that’s designed just for you. These steps will get you going but ultimately, it’s up to you. So, get going, don’t stop, and look forward to an exciting and interesting field in the AI industry. Good luck! “Your existing skills can help open doors in the booming AI field. Here's what you need to know and do before you pivot.” #aicareers #careerdevelopment #careerstrategies #litrendingtopic

Advice for Those Learning AI As you know, AI is a field that is really booming, with lots of research specifically underway. Many people pursuing their MSC degrees are heavily involved in AI research. Every day, new research papers are being published, and right now, generative AI is gaining popularity, especially in relation to LLM Models and L Models and many other use cases. In light of these developments, two questions typically arise in people's minds: · Job security. · How much do I need to learn? Considering these points, there are numerous dos and don'ts. Following these pieces of advice can make it easier for you to excel in the field, whether you are a fresher or an experienced professional. If someone suggests learning AI, don't just blindly follow that advice. - Understand the importance of AI First and foremost, understand the importance of AI within your domain, whether you are in full stack development, DevOps, backend development, software engineering, cloud computing, etc. Understanding how AI can be utilized in your domain's use cases will broaden your perspective. For example, a full stack developer who comprehends the importance of AI in website development and full stack development will find their work easier, more creative, and impressive to showcase in front of so many people out there. AI integration is becoming prevalent across all technologies and domains. It's crucial to understand the importance of AI within your specific domain. For freshers, AI skills are becoming increasingly important alongside traditional software engineering skills like DSA. Having differentiating AI skills can make you stand out in today's job market. - Efficient Learning: The 80/20 Rule For efficient AI learning, follow the 80/20 rule. Focus on mastering the core concepts (20%) and then build upon them through projects (80%). You'll naturally learn additional concepts while working on solutions. This 80/20 approach is very important in my own opinion. Don't let people discourage you by saying AI requires advanced math skills like linear algebra and calculus. Learning AI is more about understanding core concepts and applying them to real-world scenarios. Take statistics, for example. While you might spend years studying it in an academic setting, Krish Naik(a leading data scientist) claims that most data science use cases only require 10-15% of statistical concepts to solve over 90% of business problems. - Continuous Learning is Key The fields of AI, data science, and machine learning are constantly evolving. This necessitates continuous learning to ensure a smooth transition whenever the need arises. Daily exposure to new developments in AI is key to your growth in this field. Dedicate at least one to two hours daily to learn new things. You can’t just stop learning!!!. Credit: Krish Naik #datascienceenthusiast #cloudcomputing #machinelearning #AI #bigdatadeveloper

Advice for Those Learning AI As you know, AI is a field that is really booming, with lots of research specifically underway. Many people pursuing their MSC degrees are heavily involved in AI research. Every day, new research papers are being published, and right now, generative AI is gaining popularity, especially in relation to LLM Models and L Models and many other use cases. In light of these developments, two questions typically arise in people's minds: · Job security. · How much do I need to learn? Considering these points, there are numerous dos and don'ts. Following these pieces of advice can make it easier for you to excel in the field, whether you are a fresher or an experienced professional. If someone suggests learning AI, don't just blindly follow that advice. - Understand the importance of AI First and foremost, understand the importance of AI within your domain, whether you are in full stack development, DevOps, backend development, software engineering, cloud computing, etc. Understanding how AI can be utilized in your domain's use cases will broaden your perspective. For example, a full stack developer who comprehends the importance of AI in website development and full stack development will find their work easier, more creative, and impressive to showcase in front of so many people out there. AI integration is becoming prevalent across all technologies and domains. It's crucial to understand the importance of AI within your specific domain. For freshers, AI skills are becoming increasingly important alongside traditional software engineering skills like DSA. Having differentiating AI skills can make you stand out in today's job market. - Efficient Learning: The 80/20 Rule For efficient AI learning, follow the 80/20 rule. Focus on mastering the core concepts (20%) and then build upon them through projects (80%). You'll naturally learn additional concepts while working on solutions. This 80/20 approach is very important in my own opinion. Don't let people discourage you by saying AI requires advanced math skills like linear algebra and calculus. Learning AI is more about understanding core concepts and applying them to real-world scenarios.   Take statistics, for example. While you might spend years studying it in an academic setting, Krish Naik claims that most data science use cases only require 10-15% of statistical concepts to solve over 90% of business problems. - Continuous Learning is Key The fields of AI, data science, and machine learning are constantly evolving. This necessitates continuous learning to ensure a smooth transition whenever the need arises. Daily exposure to new developments in AI is key to your growth in this field. Dedicate at least one to two hours daily to learn new things. You can’t just stop learning!!!. Credit: Krish Naik

🌟 Do you know what the future of artificial intelligence in programming will look like for you and your organization?? 🤖💡 The implementation of AI tools in software development and technical roles is a given. Today's technology workers have firsthand experience of many new emerging innovations. Yet, when it comes to managing change and learning how to operate alongside AI technologies, L&D can fall short. This Training Industry article by Mike Loukides, VP of emerging tech content for O'Reilly Consulting Media & Community Relations, reveals 3 technical skills every programmer needs for AI. Key Takeaways: -Essential AI Skills: Learn what programmers really need to master AI, from understanding algorithms to implementing machine learning models. -Practical Applications: Discover how AI can streamline coding tasks, improve efficiency, and reduce errors. -Industry Insights: Get the latest trends and expert advice on integrating AI into your development workflow. Dive into the full article for an in-depth look at AI skills training for programmers! 📚

🌟 How Have I Applied Artificial Intelligence to Become the Best Engineer and University Student? 🌟 I've been using AI for almost two years now in nearly everything I do, from cooking (thanks to ChatGPT and its recipes) to studying and acquiring new skills, like learning languages (French), discrete mathematics, workout routines, and much more! And I've realized that there are many ways to take advantage of AI. Here's how: 1️⃣ Have a Clear Idea of What You Want to Achieve I’ve learned that when starting something, it’s crucial to know exactly what you want. This makes it easier for AI to assist you more efficiently. Example: I want to cook rice. How do I want it? Fluffy, grainy? Should it be ready quickly? Do I want to pair it with meat, chicken, or salad? Having a clear goal helps AI guide you better. 2️⃣ Assist, Don't Do It All for You I’ve learned that AI works best when I ask for assistance rather than asking it to do everything for me. This way, it processes the steps better, and I end up learning a lot more! Example: Want to learn Vue.js? Instead of asking it to write all the code, I give it context about where I am and what I know so far, allowing it to guide me step by step. This way, I not only solve the problem but also learn better. 3️⃣ Optimize Your Time AI has allowed me to be more efficient. I don’t waste time starting from scratch; instead, I use AI to get the best suggestions and tools that speed up my learning process. Example: Want to study a new technology? I ask for a list of the best up-to-date resources and work through them in record time. 4️⃣ Continuous Improvement with Feedback One of the great things is that AI gives you immediate feedback. If you make mistakes, you can learn instantly and improve your approach without losing momentum. Example: When studying mathematics, I can solve exercises and get detailed explanations on the steps I got wrong or areas where I can improve. 5️⃣ Creativity in Solutions Sometimes, AI has offered me creative ideas I hadn’t considered before. It’s like having a partner that suggests new ways to solve problems and tackle situations. Example: While working on a Java project, I received optimization suggestions that significantly improved my code’s performance. 💡 Follow me on LinkedIn to keep reading more posts like this one. This is just one in a series of posts, articles, and guides about projects, technology, and more. #AI #ArtificialIntelligence #Productivity #Engineering #Learning #Technology #LinkedIn #Student #PersonalDevelopment #VueJS #Java #Gym #Innovation #DataScience #DigitalTransformation #Education #MachineLearning #Automation #SmartWork #Python #BigData #Creativity #DigitalSkills #ProfessionalGrowth #Coding #Algorithms #Programming #ContinuousLearning

The new wave in the development of artificial intelligence and machine learning has made them much more accessible. How can they help businesses and science, and how will they affect the data analytics industry? These topics were discussed at the NES Popular Science Days session by NES Professor Ivan Stelmakh as well as NES graduates: Daria Levina, Strategy and Operations Manager of DataGPT (Canada), and Stanislav Mazurenko, Research Team Leader, AI in Protein Engineering, Masaryk University. Below are some tips on how AI affects education, while the highlights of the whole discussion are available here: https://lnkd.in/dUm3s8HU Why does fundamental education remain relevant? Stanislav: The world is changing much faster than before, so we need to learn flexibility. It's no longer advisable to rely on a clear career path in a specific field–it's more appropriate to focus on specific skills that you can use in the future in another professional sphere or with existing technologies. Classical education remains relevant in such conditions–thanks not only to knowledge, but also to soft skills that are developed during the learning process. Tools may change, but fundamental knowledge remains a valuable asset.   Ivan: Once, all education was fundamental, then its focus shifted to teaching tools;for example, learning Python and becoming a programmer. Now education is taking a step back–there is an understanding that tools can change, and code can be written with virtual assistants (like Copilot), while fundamental skills such as critical thinking, flexibility, and the ability to learn will be useful and supportive assets in any situation. Quality education should reach more people than it currently does.   Stanislav: The discussion about updating education can be divided into two parts. First, we need to offer more courses on how AI works, and open them not only in computer science faculties but also in biology and chemistry departments. Now, regarding AI, we can already talk without plunging into mathematics, explaining the essence of models, their logic, limitations, and advantages. Second, the conversation may involve restructuring education. Universities should answer the question, ”Should students be allowed to use, let’s say, ChatGPT when preparing their thesis?” Another option is to completely abandon such a format of testing final knowledge as a thesis, and some faculties are already considering this direction.   Daria: Personally, the most valuable thing in education for me was that it taught me to think and solve complex problems very quickly. Speaking about mastering new technologies, there are many ways to learn data analytics at a basic level–to master Power BI or learn programming in SQL. However, businesses need people who can think and apply knowledge in practice. That's why I always advocate for fundamental, scientific education, and after you get it, everything else will fall into place. #AI #ML #jobmarket #NESalumni