Teaching machine learning: AI vs ML fundamentals

Learning is a key to the knowledge. AI is a key to the learning (what? yes!). Sooo, learning AI seems to be a double feature, right? Another reminder that "revisiting basics is a good thing", especially if you have kids who are interested, so check out the lesson of core minimum of AI: https://lnkd.in/gtR-MGE2, the best pick for the Family TV Friday night. And of course, hundreds of other videos if you're curious about Python or Algorythms, or Data Structures and SQL, or... (well, you got it): https://lnkd.in/g2G3gw7c A huge thanks to David J. Malan and the whole crew of CS50 for making the Harvard University level of education (at least the first steps of it) available for everyone. Online. For free.

“Prompt engineering is too hard for beginners.” That’s the biggest myth I see. The truth? AI isn’t about coding. It’s about how well you communicate. I used to overcomplicate it — then I realized the best “hack” is just learning to ask better questions. Here are 3 free resources anyone can start with: 1️⃣ AI for Everyone – Andrew Ng (Coursera):https://lnkd.in/dwaV37kz 2️⃣ Harvard ’s Machine Learning & AI with Python:https://lnkd.in/dbxBCe4F 3️⃣ Microsoft AI Learning Hub:https://lnkd.in/dD45Qjh6 🌍 The ones who learn AI now won’t just keep their jobs. They’ll lead the future of work. 👉 What’s the most useful AI skill YOU’VE picked up so far? #AI #FutureOfWork #Learning #ArtificialIntelligence #Growth

Start Learning AI for Free in 2025! AI is transforming the world 🌍 and you don’t need to pay a single rupee to get started. Here are 5 top free resources you can use today: 1️⃣ freeCodeCamp – Machine Learning with Python 📺 Full Course on YouTube: https://lnkd.in/dvxAGFgH 2️⃣ Simplilearn – Artificial Intelligence Full Course 📺 Full Course on YouTube: https://lnkd.in/dbbB5n4d 3️⃣ Google – AI Essentials Specialization 🎓 Free on Coursera: https://lnkd.in/d3KfaCk2 4️⃣ DeepLearning.AI – AI for Everyone 🎓 Free Course Link: https://lnkd.in/dVgEwadJ 5️⃣ MIT OpenCourseWare – Introduction to Deep Learning 🎓 Free Course Link: https://lnkd.in/dpu6kg85 💡 Just passion & curiosity needed! Start learning today and build your future #AI #MachineLearning #FreeCourses #LearnAI #FutureSkills

These YouTube channels got you covered if you’d like to start learning AI Learning Artificial Intelligence can feel confusing, as there are so many concepts to grasp, starting with math foundations to deploying successfully. But with the right channels, you can learn everything step by step, for free. Here’s a cheat sheet highlighting the best YouTube channels for AI in 2025, organized into categories like Math, Python, Core ML, Deep Learning, MLOps, and Research. Each channel simplifies tough concepts, shows practical coding examples, and keeps you updated with the latest AI breakthroughs. ✅ Channels Covered: 1. Math for ML → Essence of Linear Algebra (3Blue1Brown), Khan Academy, StatQuest with Josh Starmer. 2. Python for ML → Programming with Mosh, Tech with Tim, Sentdex, freeCodeCamp.org. 3. Core ML Fundamentals → Stanford CS229, Andrej Karpathy, CodeEmporium, Corey Schafer. 4. MLOps & Deployment → Krish Naik, CodeBasics, Cloud With Raj, MLOps Community. 5. Projects & Kaggle → Abhishek Thakur, Data Professor, CodeBasics (projects), Krish Naik (end-to-end ML). 6. Deep Learning → Andrej Karpathy, freeCodeCamp.org, DeepLearningAI, Two Minute Papers. 7. Staying Updated (Research) → Arxiv Insights, The Cutting Edge School, Two Minute Papers, Yannic Kilcher. Feel free to save this guide and share with others who have similar interests. #AI

Unlocking the World of Artificial Intelligence: Top YouTube Channels for 2025! In this post Greg Coquillo stated if you are emparking on a journey to learn Artificial Intelligence (AI) can often seem daunting. From mathematical foundations to mastering deployment techniques, the path to becoming AI-savvy is filled with diverse concepts. He has put together a comprehensive cheat sheet featuring the best YouTube channels that will guide you through the essentials of AI, meticulously categorized to enhance each facet of your learning journey. Math for Machine Learning: - Essence of Linear Algebra (3Blue1Brown): Visualize and understand complex mathematical concepts. - Khan Academy: Solidify your foundational math skills with accessible lessons. - StatQuest with Josh Starmer: Simplify statistics and machine learning principles through engaging explanations. Python for Machine Learning - Programming with Mosh: Master Python with clear, beginner-friendly tutorials. - Tech with Tim: From basics to advanced topics, explore Python’s application in AI. - Sentdex: Dive into real-world tutorials on natural language processing, deep learning, and more. - freeCodeCamp.org: Comprehensive programming courses covering Python, AI, and beyond. Core ML Fundamentals: - Stanford CS229: Learn machine learning from one of the top courses by Andrew Ng. - Andrej Karpathy: Insights from one of the leading voices in the AI community. - CodeEmporium: Explore key machine learning topics with practical advice. - Corey Schafer: Python tutorials that bridge the gap to machine learning fundamentals. MLOps & Deployment: - Krish Naik: Learn about deploying models and managing machine learning projects. - CodeBasics: Step-by-step guides on moving your ML models from theory to practice. - Cloud With Raj: Harness the power of cloud computing for AI solutions. - MLOps Community: Stay updated with best practices in MLOps. Projects & Kaggle: - Abhishek Thakur: Enhance your skills with practical projects and Kaggle competition insights. - Data Professor: Hands-on project-based learning for data science enthusiasts. - CodeBasics: Broaden your understanding with in-depth project tutorials. - Krish Naik: Real-world applications of machine learning from start to finish. Deep Learning: - Andrej Karpathy: Delve into neural networks with one of the AI field's pioneers - freeCodeCamp.org: Free resources that make complex topics digestible. - DeepLearningAI: High-quality content from the creators of the popular Deep Learning Specialization. - Two Minute Papers: Stay in the loop with the latest in the world of AI research. Staying Updated (Research): - Arxiv Insights: Simplified breakdowns of the latest research papers. - The Cutting Edge School: Engage with the newest trends in AI development. Use this guide as your map to navigate the expansive territory of AI and transform your learning experience. Credit : Greg Coquillo Repost by :Salem Bagami

Fascinating results from the rStar2-Agent paper at Microsoft! A 14B Math Reasoning Model Trained with Agentic Reinforcement Learning to Achieve Frontier-Level Performance. Yet another compelling case for smart training over brute-force scaling. The magic is how the model was trained. Agentic reinforcement learning for teaching the model to use a Python environment and actually check, refine its own work and experiment. Something really cool to observe, the model exhibits interesting behaviors, with high-entropy tokens which shows both self-reflection and exploration, as well as specific "reflection tokens" that emerge in response to tool feedback. The three-stage training is another clever trick: it starts with SFT focused purely on tool use to avoid early reasoning bias, then uses RL to learn from a diverse sample of failed attempts, not just successes. In short, a more efficient model is built by teaching it how to think, explore, and self-correct with tools. A very impressive direction for AI reasoning. Check out the paper and code here: https://lnkd.in/eh_8Jy2M

Your Complete Guide to Start & Master Machine Learning! 🤖 I’ve put together everything you need to get started with Machine Learning from scratch, including hands-on implementations and key resources. 🔹 What’s Included in My Project: Bare-bones NumPy implementations of ML models Linear & Logistic Regression, Decision Trees, K-Means Clustering Neural Networks & Deep Learning fundamentals Step-by-step algorithms with explanations Gradient Descent, Cost Functions, and Backpropagation 🔹 Why This Matters: Understanding ML from scratch helps you truly grasp how algorithms work—rather than just calling a library function. Perfect for beginners and anyone looking to deepen their skills. 🔹 Extra Resources to Master ML: Free tutorials & guides online 🌐 Open datasets for practice 📊 Mini projects to apply theory 💻 Recommended textbooks and videos 📚🎥 💻 Check out the full project & code here: https://lnkd.in/dcu-UPfK 💡 Tip: Start small, build one model at a time, and experiment—hands-on practice is key! #MachineLearning #NumPy #Python #DeepLearning #DataScience #MLFromScratch #LearningResources #AI #Algorithms #NeuralNetworks #Programming #OpenSource

AI Engineer Learning Roadmap!!!! Dear Students and AI Enthusiasts, The journey to becoming an AI Engineer is exciting but often overwhelming without the right direction. To make it simpler, I’ve created this step-by-step roadmap—from foundational programming skills to advanced topics like Deep Learning and Generative AI with LLMs. In the roadmap, I have only referred one or two sub-topics. Please complete each steps meticulously. 1. Start with strong basics in Python, Data Structures, and Algorithms. 2. Strengthen your core with Mathematics (Linear Algebra, Calculus, Probability & Statistics). 3. Move into Machine Learning & Deep Learning to understand models and optimization. 4. Finally, explore the latest frontier: Generative AI & Large Language Models. Each step also includes sub-skills to guide your practice, coding exercises, and conceptual mastery. Remember, AI is not just about coding—it’s about solving problems, thinking critically, and applying knowledge to real-world challenges. I encourage my students to use this roadmap as a compass, not just a checklist. Stay curious, keep experimenting, and most importantly—enjoy the process of learning. #AI #MachineLearning #DeepLearning #GenerativeAI #LearningRoadmap #Students

I am feeling great to share this news 🙂: since 2 months, I have been learning about AI. From the big horizon of AI, I selected initially following four courses (please do not ask why) and completed one after one. 1. Machine Learning with Python from IBM In this course, I applied python and Scikit on both supervised and unsupervised ML-Algorithms like regression, classification, clustering and dimensionality reduction. With Python, I have been working with couple of years, but Scikit simple and consistent Interface surprised me a lot. it made very easy to implement ML-Algorithms using Scikit. This IBM course was full of hands-on labs and exploratory data analysis projects with real-world use cases. 2. Foundations and Core Concepts of PyTorch from Packt. In this course, I learned and used the open source machine learning library: PyTorch. Due to my Python experience I was able understand many concepts in PyTorch. Main outcome from this course was to learn about neural networks including activation and loss functions, forward/backward propagation and optimisation techniques. Using same data by splitting it in to training and testing, you can validate your model. The course had many practical exercises to test your learning. I enjoyed the course very much. 3. Building and Training Neural Networks with PyTorch from Packt In ML Engineer role, you are supposed to have good practical skills to accomplish a given problem. Therefore, I took this course as well. The course was about implementing multi class classification model, multi-label datasets and convolutional neural networks for image and audio classification. I also learn about YOLO Algorithm in this course. You may argue why I did not select TensorFlow, may be I wanted to start with the easier one. 4. Fundamentals of Reinforcement Learning: This one is my favourite and I liked it very much. Only after doing this course, I realised how complex is a human brain. While taking any decision (both real time and long time), we consider and weight so many factors and inputs. Markov Decision Processes, Exploration and Exploitation trade off, value function and Bellman Equation are some of the techniques, which we (at least I) use unknowingly in our (my) daily life. Reinforcement Learning is about to make machine think in human way. The journey is not ended. I will keep on learning.

PyTorch vs Tensorflow- Which one should you learn? PyTorch is the beginner-friendly, intuitive and heavily used in research and is perfect if you are just starting or love experimenting. It's easy to debug which makes experimenting with models feel natural and fast. Its gives you flexibility and helps you build strong fundamentals without unnecessary friction. On the other hand tensorflow excels in production with having strong deployment tools and is trusted by companies for scaling AI in real world applications. Both frameworks teach you core deep learning concepts. If you just starting out, I did recommend first learn pytorch to build intuition and then go for tensorflow for production and scaling expertise. Frameworks will keep evolving but your AI fundamentals will always remain your true edge. "Don't get attached to tools, get attached to fundamentals." So, which framework are you focusing on right now? Keep Upskilling:) #AI #DeepLearning #MachineLearning #Python #TensorFlow #PyTorch #Learning