#Overfitting Machine Learning

Machine learning relies heavily on mathematical foundations. #tech #ml #explore #fyp #ai

The Secret to Perfect Data Models #MachineLearning #PolynomialRegression #Statistics #Math #Manim Ever wondered why your machine learning model isn't performing as expected? In this video, we break down polynomial curve fitting, a fundamental concept in data science and statistics. We explore the visual differences between Degree 1 (Underfitting), Degree 3 (Good Fit), and Degree 11 (Overfitting). Learn how increasing the degree of a polynomial affects how it captures data trends and why the optimal model is crucial for accurate predictions.

I’ve been asked many times where to start learning ML, so after talking to so many experts in this field, this is a good place to start. Comment down below “TRAIN” and I’ll send you a more in-depth checklist along with the best GitHub links to help you start learning each concept. If you don’t receive the link you either need to follow first then comment, or your instagram is outdated. Either way, no worries. send me a dm and I’ll get it to you ASAP. #cs #ai #dev #university #softwareengineer #viral #advice #machinelearning

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Overfitting happens when a model learns the training data too closely, including its noise or random fluctuations, instead of capturing the general pattern. Imagine you’re fitting curves to a set of data points that roughly follow a parabolic trend. A linear model (a straight line) is too simple — it underfits because it can’t capture the curve, leading to both high training and test errors. A quadratic model (a simple parabola) matches the data’s true shape, achieving low error on both training and test sets — this is the ideal fit. However, if you use a cubic model, it may twist and bend to pass through every training point. While this gives it very low training error, it will likely overshoot on new, unseen data, giving a much higher test error. This is overfitting: the model performs well on what it has already seen but poorly on what it hasn’t. Want to learn ML/AI? Accelerate your learning in our Weekly AI Newsletter—educational, easy to understand, mathematically explained, and completely free (link in bio 🔗). C: Welch Labs Join our AI community for more posts like this @aibutsimple 🤖 #machinelearning #datascience #technology #python #programming #deeplearning #bigdata #coding #tech #computerscience #datascientist #pythonprogramming

Day 128 | ⬇️Resources + Study Group below⬇️ Both Ridge and Lasso are powerful regularization techniques that help prevent overfitting in regression models by adding penalties to the model’s complexity. But how do they differ? 🤔 📉 Ridge Regression: Adds an L2 penalty (squared coefficients), which shrinks all coefficients towards zero. Useful when you want to reduce multicollinearity without eliminating features! 🔗 Lasso Regression: Adds an L1 penalty (absolute value of coefficients), which not only shrinks but can actually zero out less important features. Ideal for feature selection! Choosing the right one can lead to a more robust and interpretable model! ⬇️⬇️⬇️⬇️⬇️⬇️⬇️⬇️⬇️⬇️⬇️⬇️⬇️ —-——-——-——-——-——-——-——-— **Study Group Info** Want to master Machine Learning? ➡️Comment “Study” and get DMed a 7-Day Free Trial to my ML group!** —-——-——-——-——-——-——-——-— ⬆️⬆️⬆️⬆️⬆️⬆️⬆️⬆️⬆️⬆️⬆️⬆️⬆️ Resources: https://online.stat.psu.edu/stat857/node/155/ https://www.publichealth.columbia.edu/research/population-health-methods/least-absolute-shrinkage-and-selection-operator-lasso —- ⏳ 1 H —- #math #ml #ai #machinelearning #artificialintelligence #DataScience #computerscience

Main Challenges in Machine Learning: 1. Insufficient or Poor-Quality Data Lack of labeled data for supervised learning. Noisy, incomplete, or biased data can lead to poor models. 2. Overfitting and Underfitting Overfitting: Model performs well on training data but poorly on new data. Underfitting: Model is too simple to capture the underlying pattern. 3. High Computational Cost Training complex models (e.g., deep learning) requires powerful hardware and GPUs. 4. Scalability Models trained on small datasets may not scale well to real-world data. 5. Model Interpretability Many powerful models (like deep neural networks) act as "black boxes" with low transparency. 6. Data Privacy and Security Legal and ethical concerns around collecting and using personal data (e.g., GDPR). 7. Bias and Fairness Models can inherit or amplify biases present in training data, leading to unfair outcomes. 8. Deployment and Maintenance Moving from prototype to production can be complex (MLOps needed). Continuous monitoring and updating are essential. 9. Choosing the Right Algorithm Selecting the most suitable model and tuning it can be time-consuming and non-trivial. 10. Domain Knowledge Understanding the domain is crucial to feature selection, data preparation, and result interpretation. Special Benefits for Our Instagram Subscribers 🔻 ➡️ Free Resume Reviews & ATS-Compatible Resume Template ➡️ Quick Responses and Support ➡️ Exclusive Q&A Sessions ➡️ Data Science Job Postings ➡️ Access to MIT + Stanford Notes ➡️ Full Data Science Masterclass PDFs ⭐️ All this for just Rs.45/month! #datascience #machinelearning #python #ai #dataanalytics #artificialintelligence #deeplearning #bigdata #agenticai #aiagents #statistics #dataanalysis #datavisualization #analytics #datascientist #neuralnetworks #100daysofcode #genai #llms #datasciencebootcamp

Overfitting explain with cookies!! Follow for more #MachineLearning #AI #DataScience #ArtificialIntelligence #MLAlgorithms

The exact framework I’d use to learn ML from scratch in 2026. Save this if you’re actually trying to build - not just collect tutorials. #machinelearning #artificalintelligence #datascience #learntocode #coding

Do you think we can build a solid model at the end of this year? #formula1 #machinelearning #programming

Let’s see if I can cover the ML pipeline in 60 seconds ⏰😅 Machine learning isn’t just training a model. A production ML lifecycle typically looks like this: 1️⃣ Define the problem & objective 2️⃣ Collect and (if needed) label data 3️⃣ Split into train / validation / test sets 4️⃣ Data preprocessing & feature engineering 5️⃣ Train the model (forward pass + backpropagation in deep learning) 6️⃣ Evaluate on held-out data to measure generalization 7️⃣ Hyperparameter tuning (learning rate, architecture, etc.) 8️⃣ Final testing before release 9️⃣ Deploy (batch inference or real-time serving behind an API) 🔟 Monitor for data drift, concept drift, latency, cost, and reliability 1️⃣1️⃣ Retrain when performance degrades Training updates weights. Evaluation measures performance. Deployment serves predictions. Monitoring keeps the system healthy. It’s not linear. It’s a loop. And once you move beyond a single experiment, that loop becomes a systems problem. At scale, the challenge isn’t just modeling … it’s building reliable, scalable infrastructure that supports the entire lifecycle. Curious if this type of content is helpful! Lmk in the comments & as always Happy Building! 🤍

Comment “ML” and I’ll send you the links👇 Machine learning doesn’t have to feel overwhelming. With the right guidance, complex topics like models, training, and prediction start making real sense 🧠 📌 Check out these beginner-friendly ML videos: 1️⃣ Learn Machine Learning Like a Genius – by InfiniteCodes 2️⃣ All ML Concepts Explained in 22 Minutes – by InfiniteCodes 3️⃣ ML for Everybody (Full Course) – by FreeCodeCamp If terms like neural networks, supervised learning, or algorithms have ever confused you, these tutorials simplify everything into clear, practical explanations you can actually follow. Instead of getting stuck in heavy math or abstract theory, you’ll build strong intuition around how machine learning works — from foundational concepts to real-world AI applications. Whether you're interested in artificial intelligence, data science, Python projects, or future-proof tech skills, this is a powerful place to begin. ⭐ Save this so you don’t lose it, share it with someone learning AI, and start making machine learning finally click.