About the AI Discussions category

A dedicated forum space to engage in conversations about Artificial Intelligence.

Here, you can explore the latest advancements in the field, exchange ideas on best practices, and delve into technical aspects of AI development. Whether you’re a seasoned researcher or just starting your journey in AI, this category provides a platform for knowledge sharing and insightful discussions.

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Seeking Guidance for My AI Career Journey

Hello AI community,

I’m passionate about building a career in artificial intelligence, but I’m feeling a bit lost on where to start. I’m looking for advice on:

  1. Recommended learning resources for AI beginners
  2. Key areas to focus on (e.g., machine learning, neural networks, natural language processing)
  3. Practical projects to build skills
  4. Potential career paths in AI
  5. How to find mentorship in this field

If anyone here has experience in AI or has gone through a similar learning journey, I’d greatly appreciate your insights and recommendations. I’m eager to learn and would be grateful for any guidance on structuring my learning path.

Thank you in advance for your help!

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This is a great question, Mahek. Definitely, platforms like Coursera & Udemy have a wealth of beginner level AI courses that could be a good place to learn on your own. If you would rather prefer to have a mentor to prepare a customized roadmap and help you along, you may want to check out profiles on topmate (search for sanjan_tp_gupta), hito, LinkedIn etc. for people who are open to mentoring.

Hope this helps you and others who are new to the field of AI.

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Hey. Hope your learning journey has gone well.
As a corporate professional that wanted to diversify experience. I found it helpful to read a few introductory books and apply the principles directly to work that I had done. I also found similarities between areas at school. If you reflect on subjects you have taken those can be good sources for projects.

Most of the mathematical principles are in economics and data analytics that form part of everyday life.

Reviewing Data Science cheatsheets on programming languages helped me to learn the most important areas first, then I deep dived into other aspects that I thought I would use. Courses on coursera and udemy also helped except I think you will gain best value by applying what you learn as soon as you can otherwise it can feel like expired knowledge after some time. The practice of debugging when things go wrong is sort of what helps reinforce the learning.

Best of luck :slight_smile:

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i was also in Mahek’s situation and I can safely say Udemy is also helping me as a beginner

Could you point out some courses on Udemy you found helpful?

Thank You

If you are new to the field of artificial intelligence, consider exploring the DeepLearning platform. They offer free introductory courses, such as "AI for Everyone,"which provide an excellent foundation in understanding AI and machine learning concepts.

Once you have a basic understanding, you can further enhance your knowledge by enrolling in the Machine Learning Specialization course available on Coursera. The DeepLearning platform also provides a wide range of resources, from beginner to advanced levels, making it a valuable tool for learners at any stage.

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Hai AI Community!

I have a pretty good idea, maybe you can refute it to become a more brilliant idea.

Emotional AI Companions for Mental Health and Well-Being

In the future, AI could transform mental health care by creating Emotional AI Companions—intelligent systems designed to understand, support, and enhance human emotional well-being. These AI companions would use advanced natural language processing, sentiment analysis, and facial recognition to detect subtle emotional cues, such as changes in tone, body language, or expression.

By analyzing these cues, the AI could offer personalized support, such as calming techniques for stress, mood-boosting activities, or simply being a non-judgmental listener for those who feel isolated. It could also integrate with wearable devices to monitor physical signs like heart rate and sleep patterns, providing holistic recommendations to improve mental and physical health.

For long-term mental health management, these AI systems could act as virtual therapists, helping users track emotional trends, set goals, and practice mindfulness or cognitive behavioral exercises. They could also serve as early-warning systems, alerting healthcare professionals or loved ones if signs of severe distress or mental health deterioration arise.

This innovation could provide affordable, accessible, and stigma-free mental health support, empowering people worldwide to take control of their well-being.

Permission to answer

  1. Beginner-Friendly Learning Resources:
    Kick off your AI journey with foundational materials. Great options include:
  • Online Courses:
    • Machine Learning by Andrew Ng (Coursera)
    • Deep Learning Specialization* by DeepLearning.AI (Coursera)
    • CS50’s Introduction to AI with Python (edX)
  • Books:
    • “Artificial Intelligence: A Guide to Intelligent Systems” by Michael Negnevitsky
    • “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron
  • Interactive Platforms:
    • Explore Kaggle for datasets and competitions.
    • Use tools like Google’s Teachable Machine for hands-on practice.
  1. Key Topics to Focus On:
    Start by mastering the basics:
  • Math Skills: Learn linear algebra, probability, and statistics.
  • Programming: Focus on Python and its key libraries like NumPy, pandas, and TensorFlow.
  • Machine Learning Concepts: Understand supervised and unsupervised learning techniques.
    Once you’re comfortable, delve into advanced areas such as:
    • Natural Language Processing (NLP)
    • Computer Vision
    • Reinforcement Learning
  1. Skill-Building Projects:
    Begin with simple, practical projects to develop your abilities:
  • Use linear regression to predict housing prices.
  • Build a sentiment analysis tool with text data.
  • Develop a basic image classifier using TensorFlow or PyTorch.
  • Participate in Kaggle competitions to apply your knowledge in real-world scenarios.
  • Create end-to-end solutions, like a chatbot or recommendation system.
  1. Career Opportunities in AI:
    AI opens doors to various roles, such as:
  • AI Researcher: Working on innovative algorithms and theories.
  • Data Scientist/Engineer**: Leveraging AI to analyze and interpret data.
  • AI Developer: Creating AI-driven applications and tools.
  • Ethics Specialist: Ensuring responsible and fair AI usage.
  • AI Product Manager: Overseeing the development of AI-powered products.
    Gaining experience through internships and entry-level positions can help clarify your ideal path.
  1. Finding Mentorship in AI
  • Join AI communities on LinkedIn, Reddit, or Discord.
  • Attend webinars, hackathons, and meetups to expand your network.
  • Contribute to open-source initiatives, such as TensorFlow or Hugging Face.
  • Reach out to experienced AI professionals on LinkedIn, explaining your goals and interest in their work.
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Всім доброго дня!

Thank you, for sharing your valuable knowledge with us :slightly_smiling_face: