AI বা Machine Learning শেখার ক্ষেত্রে শুরু থেকে কিভাবে আগানো উচিত?

15 thoughts on “AI বা Machine Learning শেখার ক্ষেত্রে শুরু থেকে কিভাবে আগানো উচিত?”

1. Kono akdin Jhankar vai er sathe dekha korte chai..❤️ Allah jeno se sujug ta den..amin

2. 🖤✨

3. আমি এইচএসসি পাশ করেছি ২০১৯ সালে। আলহামদুলিল্লাহ রেজাল্ট ভালোই ছিল। কিন্তু একটা এক্সিডেন্টের কারনে আর কোথাও ভর্তি হয় নাই। এখন আমার AI বা Machine learning শিখতে মন চাচ্ছে।
   এখন প্রশ্ন হচ্ছে,
   ১. আমি কি AI বা Machine learning শিখতে পারব/ আমার দ্বারা শিখা সম্ভব হবে??
   ২. AI বা Machine learning শিখে remote job বা Freelancing marketplace গুলোতে কাজ পাওয়া যাবে??
   অনুগ্রহ করে একটু পরামর্শ দিবেন ভাইয়া, প্লিজ🙏🙏

4. totally helpful… Thank you Sir Jhankar mahbub…. I Have a Question that Does Phitron Teach Full OF Machine Learning?

5. Answer 6:10

6. আমি একটা ব্যাবসায়িক সফটওয়ার বানাতে চাই।
   এ প্রযুক্তির আওতায় কি সম্ভব।
   কোথায় যেগাযোগ করবো?

7. Vai apnar kono Course ache ML ba Ai ba python programing?

8. Course details plz

9. software developer roadmap skills 2024

10. software developer roadmap skills 2024

11. Ami business Department er student . Ami ki machine learning engeeniar hote parbo? Ar way ta ki jodi ami hote chai? Kindly aktu janaben?

12. Sure, here's a general syllabus outline for an introductory course on artificial intelligence (AI):

    Week 1: Introduction to Artificial Intelligence
    – What is AI?
    – History and evolution of AI
    – Applications of AI in various fields
    – Ethical considerations in AI

    Week 2: Problem Solving and Search Algorithms
    – Problem-solving methods
    – Search algorithms (e.g., depth-first search, breadth-first search, A* search)
    – Heuristic search techniques

    Week 3: Knowledge Representation and Reasoning
    – Representing knowledge in AI systems
    – Propositional and predicate logic
    – Inference rules and reasoning mechanisms

    Week 4: Machine Learning Fundamentals
    – Introduction to machine learning
    – Types of machine learning (supervised, unsupervised, reinforcement learning)
    – Basic concepts: features, labels, training, testing

    Week 5: Supervised Learning
    – Regression and classification
    – Linear regression
    – Logistic regression
    – Decision trees and ensemble methods (e.g., random forests)

    Week 6: Unsupervised Learning
    – Clustering algorithms (e.g., K-means clustering, hierarchical clustering)
    – Dimensionality reduction techniques (e.g., principal component analysis)

    Week 7: Neural Networks and Deep Learning
    – Introduction to artificial neural networks
    – Feedforward neural networks
    – Convolutional neural networks (CNNs)
    – Recurrent neural networks (RNNs)

    Week 8: Natural Language Processing (NLP)
    – Basics of NLP
    – Text preprocessing
    – Language modeling
    – Named entity recognition (NER)
    – Sentiment analysis

    Week 9: Reinforcement Learning
    – Introduction to reinforcement learning
    – Markov decision processes (MDPs)
    – Q-learning
    – Deep Q-networks (DQN)

    Week 10: AI Applications and Future Trends
    – Real-world applications of AI (e.g., autonomous vehicles, healthcare, finance)
    – Emerging trends in AI (e.g., explainable AI, AI ethics, AI for social good)
    – Challenges and future directions in AI research

    This syllabus provides a structured overview of key topics in artificial intelligence, covering foundational concepts, algorithms, and applications. Depending on the course duration and depth of coverage, additional topics or case studies may be included to further enhance students' understanding of AI principles and practices.

13. Machine Learning engineer and deta scientists ki same???

14. ai course কোথায় করব?

15. Phitron theke AI/ML er course anle valo hbe🤩

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