How I Learn with Vibe Learning: A New Approach to Education

🚀 ML Myths Debunked: "Machine Learning is only for math geniuses." When students hear about Machine Learning, many instantly think: 📉 “It’s too complicated.” 📐 “I need to be a math wizard.” 🤯 “It’s not for me.” But here’s the truth: Machine Learning isn’t just about crunching equations — it’s about solving problems. Think about it — every time you: Use Netflix and get a spot-on movie suggestion, Scroll through Instagram and wonder “how does it know what I like?”, Or even ask Google Maps for the fastest route — 👉 You’re experiencing Machine Learning at work. And the people behind these systems? They started as learners like you. Not math prodigies. Not coding superheroes. Just curious minds willing to take the first step. 🔑 What really matters is: A basic grasp of concepts (yes, math helps, but you’ll build it over time), Hands-on practice with datasets & libraries (Python, Scikit-learn, TensorFlow, etc.), And most importantly — curiosity to ask questions like “What if I could teach a machine to do this?” ✨ Here’s a secret: The field of ML grows because of learners who experiment and explore, not because of geniuses who knew it all from day one. 💡 So, if you’re a student wondering, “Is ML for me?” — the answer is simple: YES. Start small, stay consistent, and you’ll be surprised how far you can go. 👉 Remember: You don’t need to be a math genius to begin. But once you begin, you’ll slowly uncover the genius within you. #MachineLearning #AI #Students #TechForAll #LearningJourney #StayCurious

Most AI roadmaps feel like Netflix menus. Too many options. No idea where to start. You scroll. You save. You forget. As an electrical engineer getting into AI and data science, I’ve been through that loop more times than I’d like to admit. Jumping between tutorials, bookmarking articles, watching courses… and still not building anything real. Then I found a Medium article by Benedict Neo and it actually delivered. It’s not another motivational thread or list of buzzwords. It’s a full roadmap with real resources, step-by-step guides, toolkits, and a clear path to start building AI projects with confidence. It shows you how to start coding before you feel ready, how to share your learning journey publicly, and how to break free from endless tutorial loops. Here’s the article that helped me get unstuck: https://lnkd.in/dWtkKfTp I’ve started using it to move from passive learning to real progress. And tell me honestly — what’s the one thing that always slows you down when trying to learn something new?

Excited to share this gem from AI pioneer Andrew Ng! 🚀 If you’ve ever wanted to dip your toes into coding but felt overwhelmed, or if you’re looking to level up your skills with the power of generative AI, check out his brand-new course series: “AI Python for Beginners.” It’s a set of four short, beginner-friendly courses that teach coding from scratch—no prior tech experience needed. The best part? They’re FREE for a limited time! Andrew’s approach is game-changing: • Use AI chatbots as your personal coding sidekick to write, debug, and brainstorm code. • Build real AI apps right away, like poem customizers, recipe generators, and to-do list managers powered by large language models. In today’s world, a bit of coding knowledge can supercharge any role—whether you’re in marketing, journalism, investing, or beyond. Imagine automating tasks, analyzing data faster, or creating custom tools to boost your productivity. As Andrew says, “Beginning programmers can learn to do useful things with code far faster than they could have even a year ago.” Spot on! If you’re an experienced dev, spread the word to your non-tech friends. Let’s democratize AI skills! https://lnkd.in/gHatBx5T #AI #Python #CodingForBeginners #DeepLearning #AndrewNg #GenerativeAI #TechEducation

Feeling confused about where to start with AI or which skills you should learn next? You’re not alone. In today’s fast-paced world, there’s an overwhelming amount of advice about in-demand skills, trending technologies, and endless online courses—making it easy to feel lost or intimidated. I remember when I first started exploring AI. It felt like standing at the base of a skyscraper, looking up at all the things I didn’t know. The fear of choosing the wrong skill or not being “techy” enough can hold you back from making progress. The pressure to “keep up” makes it even harder. But here’s the good news: You don’t have to figure it all out at once. Start small. Pick a single skill—maybe Python basics, prompt engineering, or data analysis. Dedicate just a few minutes daily to learning and experimenting. You’ll be amazed at the momentum that builds when you focus on consistent, manageable steps. Don’t compare yourself to experts; compare yourself to who you were yesterday. Remember, every expert was once a beginner. If you’re feeling stuck, send me a message or comment below. Let’s grow together and turn confusion into confidence, one skill at a time!

As I continue my Machine Learning journey, today I explored one of the most fascinating ensemble techniques – AdaBoost (Adaptive Boosting). 🚀 Before jumping into AdaBoost, let’s quickly understand three important concepts: 1️⃣ Weak Learners: A model that performs slightly better than random guessing (accuracy just above 50%). 2️⃣ Decision Stump: A simple decision tree with only one split (maximum depth = 1). 3️⃣ +1 and -1 Labels: +1 is used to represent the positive class, while -1 represents the negative class. ✨ What is AdaBoost? AdaBoost (Adaptive Boosting) is a powerful ensemble technique that combines multiple weak learners into a single strong learner. It is a stage-wise additive method, which improves itself step by step: Each data point in the dataset is assigned a weight. The weak learner is trained, and the errors are identified. Misclassified samples are given higher weights so that the next weak learner focuses more on them. This process continues iteratively, and in the end, predictions are made by combining all weak learners with weights based on their performance. 🔹 Why AdaBoost Works ✅ Converts weak learners into a strong classifier ✅ Reduces both bias and variance ✅ Handles complex decision boundaries effectively ✅ Works well with simple learners like decision stumps 🔹 Intuition with Analogy Think of AdaBoost like a group of teachers helping students: The first teacher teaches everything. The second teacher only focuses on the questions students got wrong. The third teacher again focuses on the mistakes left out. By the end, the students learn from all the teachers together, which is much stronger than learning from just one. 🔹 Key Parameters in AdaBoost n_estimators → Number of weak learners (more learners = more power, but too many can overfit). learning_rate → Shrinks the contribution of each weak learner. Lower values = slower but more robust learning. estimator → The base learner (commonly a decision stump). 🔹 Where is AdaBoost Used? Credit scoring 🏦 Face detection in images 📸 Medical diagnosis 🏥 Text classification 📄 Learning AdaBoost made me realize a beautiful lesson: even weak models, when combined smartly, can become very powerful — just like how teamwork in real life creates extraordinary results. ✨ #MachineLearning #AdaBoost #Boosting #EnsembleLearning #DataScience #AI #MLAlgorithms #Python #LearningJourney

I've been studying machine learning for 15,000 years. Here is the only tutorial you'll ever need... What? You seriously thought there would be an answer here? There is no secret sauce, no magical tutorial or book or course that will teach you everything you need to know about ML (or any topic) and nothing else. Any of the 2000+ machine learning courses or tutorials you'll find with a simple Google search is more or less the same. Some a bit better in some parts, worse in others. Stop waiting for the perfect resource. Learning is more about doing than anything else. Just take whatever YouTube video or Kaggle notebook or Substack post lands in your feed, and do the drills. Write the code, train the models, run the numbers. Then do it again, over and over. Trust me, you don't need the perfect teacher. You just need to put the effort. In a year or two, you'll be ready for a junior ML role. Bonus if you take some time to learn a bit of the foundational math.

I'm teaching a machine learning class this week. One of my favorite parts of teaching is helping technical folks not just learn the concepts, but also see how they apply directly to their own domains. Now, I often get asked: “Can’t AI just replace ML?” The honest answer: sometimes. But it’s usually more expensive, slower, and often delivers worse performance. “Can’t AI just solve ML problems for me?” Again, sometimes. But without an understanding of the fundamentals, you can’t sanity-check the output. And trust me, AI goes off the rails more often than you’d think. (Ask me how I know…) Here's a drawing I made during my course yesterday to help students remember the difference between precision and recall. What do you think of it? (As always, follow me for more insights on Python, tabular data science, and AI applications. And if your team needs help with these tools, reach out.)

7/ The biggest skill in the next 5 years? Not coding. Not math. Not a degree. It’s learning to think through AI tools. The faster you master them, the faster you can build, learn, create, and compete. Those who are AI-native will leave the rest behind.

🚨 Big news! We (Towards AI) teamed up with O'Reilly (the publisher of the very first ML books I ever read back in 2018 🦉📚) to launch our brand-new “Building & Operating Large Language Models” 10-hour bootcamp, now live on the O’Reilly learning platform! Put simply: the same practical LLM playbook we share with $50 K+ clients is now just one click away for every O’Reilly member. What you’ll nail in a single (epic) day: ⚙️ Prompt vs. RAG vs. Fine-Tuning vs. Agents. Know when and why to use each. 🔍 Evaluate & debug with automated + human-in-the-loop metrics 🛡️ Optimize & harden models (distillation, quantization, RLHF, safety guardrails) 🏋 Applied examples with a good mix of 50:50 foundations and practical examples (and exercises) 🚀 Concerns when shipping a real product No PhD required (obviously, since I dropped out of it), just curiosity and a dash of Python helps. Huge thanks to the O’Reilly team for believing in our mission to make practical AI education accessible to everyone. Ready to 10× your AI skillset? Check it out here: https://lnkd.in/g3fc5Mrk #LLM #GenAI #OReilly #AIEngineering #MachineLearning

If you want to test your understanding of machine learning, explain it to your grandmother. I once sat down with mine, a basket of fruits in hand. “Grandma, imagine you always sort apples from bananas. You’re really good at it.” She nods. “Of course.” “Now imagine you teach the basket to sort itself. That’s machine learning.” She squints. “So when will the basket start cooking dinner too?” That’s the thing about AI. For us, it feels groundbreaking. For many people, it feels… absurd. And that’s why communication is just as important as code. If you can’t explain it simply, you probably don’t fully understand it. Machine learning may be “teaching machines to learn,” but the real challenge is teaching humans to trust. 👉 How do you explain what you do to family members who think “Python” is just a snake? #MachineLearning #ArtificialIntelligence #DataScience #Storytelling #CareerDevelopment