What are the best ways to learn about AI programming languages?

?? ?????? ?????? ???????????? ?????????? ???????????? ?????? ????? 1. Simple Learning Curve: Easy to learn for anyone regardless of the background 2. Abundant Learning Resources: Due to its popularity, Python boasts a vast collection of FREE tutorials, courses, and online communities. 3. Simple and Clean Syntax: Python's syntax is designed to be readable and easy to understand, even for those with little or no programming experience. 4. Multi-Purpose Language: Python is a versatile language that can be used for a wide range of applications, from web development and AI to scripting and automation. 5. Large and Supportive Community: Python has a large and active community of developers, ranging from beginners to experts.

Python is a great language to learn when using AI, but importantly, as you first get started there are low code tools and natural language options you can use. So I would be hesitant to advise learning a coding language is your first step in AI. You could be much better off doing a fundamentals certificate in AI from a tech provider for free, to understand the core concepts and then get started on a low code tool, such as Microsoft Copilot Studio. You can't fine tune your models to the same degree you can directly in things like Azure Open AI Studio, so you would need to move to these once you hit the limit of what they can do. But for things like search all of your policies and procedures PDFs for a keyword Copilot Studio is perfect.

Python has emerged as the leading language for AI due to its simplicity and the vast ecosystem of data science libraries, such as TensorFlow and PyTorch. R is favored for statistical analysis and visualizations. Java offers a balance of performance and usability, making it suitable for large-scale AI systems. C++ is chosen for scenarios where low-level control over memory and processing is necessary. Lisp, one of the oldest AI languages, is less common today but still valued for its symbolic processing capabilities. Each language has its niche, and the choice often aligns with the specific demands of the AI application.

To learn AI programming languages effectively, consider these steps: - Online Courses: Engage with platforms like Coursera and edX for structured courses on Python/R. - Interactive Learning: Opt for hands-on platforms like DataCamp for practical learning. - Books & Textbooks: Explore resources focusing on Python, R, and data science libraries. - Open-Source Projects: Participate in AI projects on GitHub to apply your skills. - Real-World Projects: Practice by working on AI projects like predictive or NLP applications. - Stay Updated: Follow AI trends through blogs, podcasts, and industry events. - Specialized AI Courses: Enroll in specialized courses dedicated to AI languages.

While numerous programming languages are available, some are more widely used and better suited for AI development than others. The nature and complexity of the AI challenge, the availability and compatibility of libraries and frameworks, the performance and scalability of the code, and the ease of learning and debugging are some of the aspects that influence the language choice. The most popular and suggested languages for artificial intelligence include Lisp, Python, R, Java, and C++. You can choose a language that best suits your requirements and interests by first learning about the advantages and disadvantages of each.

To learn programming effectively, especially when focusing on mastering the basics, it's essential to start with a solid foundation in syntax, data structures, control flow, functions, and object-oriented programming concepts. Dive into online courses, books, tutorials, or videos tailored to beginners, focusing on these fundamental topics. Platforms like Codecademy, HackerRank, or Codewars offer interactive exercises and challenges to practice your skills and reinforce your understanding. Additionally, engage in hands-on projects to apply what you've learned and gain practical experience.

Before tackling AI-specific concepts, mastering programming fundamentals is crucial. Learn basics like syntax, control flow, and data structures through online courses and practice platforms. A solid foundation sets the stage for successful AI development.

A great way to learn the basics is also to start to use AI products. Generative AI, LLMs, Speech Recognition, Visual AI, bots, automation, deep learning and then have the curiosity to ask questions How do they get this to do that? What are the limitations? Could I create something similar, but simpler? What problems do I have with the current solution? Once you have the Why to learn, it will then steer you to learn the path to meet your curiosity as there are so many options to learn the basics but if it is context of what you want to learn it will help you progress.

A glowingly popular way of learning any language is to use tools like chatgpt or similar ai assisted development tools. The best way of learning from recent studies we have undertaken ourselves with hundreds of developers agreeing is learn by doing, and then use the tools with gpt growing popular but YouTube actually being the most common used tool replacing stack overflow as the go to in line support tool.

I started by getting a grip on the syntax, data structures, control flow, and other foundational concepts. This groundwork makes your AI coding journey smoother and more effective. I leaned heavily on resources like online courses and interactive platforms like Codecademy and HackerRank. They offer hands-on practice and real-world examples, which are invaluable for understanding abstract concepts. Don't rush this stage; solidifying your base makes advanced topics more approachable and your code more robust.

These tools, like TensorFlow for deep learning, Scikit-learn for machine learning, and NLTK for natural language processing, equipped me with the capabilities to tackle complex AI tasks efficiently. I started by exploring their documentation and tutorials, which are treasure troves of practical insights and examples. Understanding how to implement these libraries in real-world projects, from data preprocessing to model training and evaluation, significantly accelerated my AI development journey. Engaging with community forums and GitHub also provided real-life coding insights and problem-solving strategies. Start small, experiment with different libraries, and gradually build your understanding and expertise.

Popular programming languages for AI offer a wealth of libraries and frameworks tailored to diverse tasks. From TensorFlow to PyTorch, these tools streamline the development of sophisticated AI applications. Access their power through official documentation and guides.

Language like Python has many libraries available making it easier for AI engineers to built projects in short time. Libraries like numpy, pandas, matplotlib, seaborn are used for data manipulation and data visualization purposes. Library like scikit-learn is used for Machine learning tasks. Many almost all algorithms are implemented here. Tensorflow, pytorch are used in neural networks and deep learning. Advanced deep learning architectures like CNN. RNN, etc are also implemented. Another famous library NLTK, Spacy is used for text preprocessing tasks. Moreover, there is ultralytics library where yolo algorithm is implemented commonly used in computer vision tasks. So after covering basics of programming explore these libraries as well.

Frameworks and libraries are a good place to start. Start with a problem that piques your curiosity. Whether it's creating smart bots with TensorFlow, analyzing data with NumPy, or exploring computer vision with OpenCV, choose a project that excites you. Focus on one or two tools that best fit your goals. This targeted approach makes learning more fun and less overwhelming. Dive into the documentation, experiment, and don't hesitate to engage with the community for insights. By building projects, you're not just learning—you're applying and growing. This hands-on journey stresses the practical aspects of AI programming, making your learning experience both enjoyable and fruitful. Let's get started and turn those ideas into reality!

In the AI field, mastering libraries and frameworks like TensorFlow, PyTorch, Keras, Scikit-learn, NLTK, OpenCV, and NumPy is crucial. They offer pre-written code for tasks like data handling, visualization, and complex mathematical operations. Frameworks, on the other hand, provide a structured environment for AI projects, including deep learning, NLP, and computer vision. To effectively use these tools, engage with their official documentation, tutorials, and example projects. This hands-on approach accelerates learning, allowing you to apply AI solutions efficiently.

Knock on the door and pitch your value. Listen, there is always a skill that you do excel in that they need trade it for opportunity to learn, skill up to where you want to be. GET in the mix. BE strategic, meaning go after the space you want to be in anyway. #YouGotThis and more.

I'm very big on applying what I learn directly to real-world projects. It's the best way to solidify your understanding and stretch your creative muscles. Choose a project that resonates with your interests and pushes you to apply the AI languages and tools you've been studying. Websites like Kaggle or Coursera offer a treasure trove of project-based learning opportunities and competitions. Dive in, experiment, and use these projects not just to learn, but to innovate and showcase your skills. It's through tackling actual problems that you'll truly master AI programming.

After covering basics the best way to learn and gain hands on experience is built valuable projects. Built some valuable projects. Data set will be available online on kaggle etc. Gather it preprocess it then train and evaluate the model. If needs help you can get from online sources from kaggle, stackoverflow as well. By building these projects you can add these to your resume and portfolio as well. Also try to participate in international competitions like hackathons.

Start with online courses and tutorials that focus on AI programming languages like Python and R. Practice by working on projects and contributing to open-source initiatives. Join coding communities and forums to seek advice and share knowledge. There are also many online resources such as Youtube where you can assess more hand-on information. Lastly, you can also use tools such as ChatGPT and Perplexity to learn more about the various programming languages and you can prompt based on your specific needs.

Diving into AI through hands-on projects is invaluable. It cements your understanding, fosters innovation, and enhances your portfolio. Select projects reflecting your ambitions or passions, challenging you to utilize learned language features and AI libraries. Platforms like Kaggle, Coursera, or Udemy offer project-based learning and competitions, providing both inspiration and a structured path to tackle real-world AI problems. This approach not only sharpens technical skills but also boosts problem-solving and creativity in AI.

AI learning through community engagement can be super powerful! ?? Group Learning: Engage with AI communities to leverage collective knowledge. Group settings often offer deeper insights and a broader range of perspectives than solitary study. ?? Tap into Online Communities: Platforms like Stack Overflow, Reddit, and specialised AI forums are treasure troves of knowledge, offering support, mentorship, and accountability from peers and experts. ?? Events and Resources: Join webinars, workshops, and hackathons to enhance your skills actively. Follow authoritative blogs, podcasts, and newsletters for the latest AI trends and insights, allowing you to both specialise and broaden your understanding as needed.

Joining communities enriches AI learning. Platforms like Stack Overflow, Reddit, and Quora offer spaces to query, share insights, and collaborate. Engage in webinars, workshops, and hackathons for hands-on skills and networking. Follow blogs, podcasts, and newsletters for updates on AI trends and opportunities. This collective wisdom accelerates your learning curve, providing real-world applications and a network of peers and mentors.

Joining platforms like Stack Overflow or Reddit has been invaluable for troubleshooting and gaining different perspectives. Engaging in these communities lets you ask questions, share experiences, and even help others, which reinforces your own understanding. Plus, participating in hackathons or online workshops is not just about learning new skills; it’s a fantastic way to network and get your work noticed. And don’t forget to stay informed with blogs and podcasts – they’re great for keeping up with AI trends and ideas. Remember, in the AI community, sharing knowledge helps us all grow faster.

AI is fast-changing field. So in order to stay in touch with the latest trends and techniques in AI join AI communities. Especially join AI groups on linkedin and keep viewing posts regarding AI. Also follow big AI tech companies on social media like openai, anthropic ai, meta ai , google deep mind, etc. Also try to attend webinars in this way you will learn a lot also will grow your network. Try to read research papers in free time. Read blogs, articles on AI as well.

You are not gonna learn to code by watching some "learn programming" video. The only way is to have a project you want to build & figure it out as you go. Want to learn ML? Don't start with "how I ML my macaroni". Have a real problem first. Learn simple stuff, then maybe it can help your macaroni. AI is moving fast. ChatGPT automates basic coding now. Learn problem-solving, that's always needed. Also, learn cutting-edge too. Build real things, learn by doing, but watch where AI's headed.

Online Courses: Platforms like Coursera and Udacity offer AI programming courses covering Python, R, and other languages. Books: "Python Machine Learning" by Sebastian Raschka or "Deep Learning" by Ian Goodfellow provide in-depth knowledge. Tutorials: Explore language documentation and online tutorials for practical learning. Hands-On Projects: Platforms like Kaggle offer real-world projects for application-based learning. Online Communities: Join forums like Stack Overflow and Reddit's r/MachineLearning for discussions and guidance. Coding Bootcamps: Attend bootcamps specializing in AI and machine learning for structured learning and mentorship.

? Pick Language: Start with Python/R. ? Online Courses: Enroll, learn systematically. ? Read Books: Explore guides, textbooks. ? Practice Coding: Solve real problems. ? Join Communities: Engage, ask, learn. ? Attend Workshops: Gain practical insights. ? Follow Research: Keep abreast of advances. ? Build Projects: Apply knowledge practically.

I've never learned programming or other computer science skills, but I can say that you can learn yourself! A lot of courses are free and give you tremendous value! Some tips for free (full) courses: - Microsoft AI Course (career essentials) - MIT AI (this is an 8-week course, free!) - Google's free GenAI course - Udacity - IBM's AI for Everyone

You can also immerse yourself in online courses with hands-on projects, engage with the AI community through forums and meetups, and challenge your skills in competitions like Kaggle. Don't forget to work on personal projects that ignite your passion, and always stay updated with the latest in AI by following blogs, podcasts, and research. This focused approach will not only enhance your knowledge but also keep you motivated and connected with the evolving world of AI.