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#Mlmodels Reel by @data_greek - A model that performs perfectly on training data might still fail in the real world. In this short video, I explain the difference between Overfittin
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@data_greek
A model that performs perfectly on training data might still fail in the real world. In this short video, I explain the difference between Overfitting and Generalization — two key concepts every Machine Learning practitioner should understand. If you're building ML models, the real goal isn't just high training accuracy… it's creating models that perform well on unseen data. 🎬 Video created with assistance from NotebookLM by Google. #machinelearning #datascience #artificialintelligence #mlconcepts #overfitting #generalization #ai #mlengineer #deeplearning #datascientist
#Mlmodels Reel by @smart_tech_ai_unfolded - Small validation mistakes can destroy models in production. Learn how to evaluate ML systems the right way. #machinelearning #mlengineering #modeleva
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@smart_tech_ai_unfolded
Small validation mistakes can destroy models in production. Learn how to evaluate ML systems the right way. #machinelearning #mlengineering #modelevaluation #datascience #ai
#Mlmodels Reel by @nomidlofficial - 🚨 Your dataset is incomplete… and your model is suffering. Missing values are one of the biggest reasons why ML models fail. If not handled properl
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@nomidlofficial
🚨 Your dataset is incomplete… and your model is suffering. Missing values are one of the biggest reasons why ML models fail. If not handled properly, they can: ❌ Reduce accuracy ❌ Introduce bias ❌ Break algorithms Here’s how to handle them 👇 ✔ Drop missing data (simple but risky) ✔ Mean / Median / Mode imputation ✔ Forward & backward fill ✔ Interpolation methods Smart handling = better model performance 🚀 📌 Follow @nomidlofficial for more ML & coding content. Read more info: https://www.nomidl.com/machine-learning/missing-values-treatment-methods-in-machine-learning/ #machinelearning #datascience #python #ai #coding
#Mlmodels Reel by @cloud_x_berry (verified account) - Follow @cloud_x_berry for more info #MachineLearning #MLAlgorithms #DataScience #AI #LearnML machine learning algorithms explained, linear regressio
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@cloud_x_berry
Follow @cloud_x_berry for more info #MachineLearning #MLAlgorithms #DataScience #AI #LearnML machine learning algorithms explained, linear regression model, logistic regression classification, decision tree algorithm, support vector machine svm, knn algorithm explained, dimensionality reduction techniques, random forest algorithm, k means clustering algorithm, naive bayes classifier, supervised learning algorithms, unsupervised learning algorithms, classification vs regression, ml basics for beginners, data science concepts, ai model types, feature engineering basics, model selection techniques, ml interview preparation, machine learning fundamentals
#Mlmodels Reel by @smart_tech_ai_unfolded - Even the best machine learning algorithm cannot fix bad data. Discover why data quality is the real foundation of successful ML models. #machinelearn
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@smart_tech_ai_unfolded
Even the best machine learning algorithm cannot fix bad data. Discover why data quality is the real foundation of successful ML models. #machinelearning #datascience #dataquality #mlconcepts #learnml
#Mlmodels Reel by @my_life_with_audhd - These are the 2 mistakes I made when I started on my ML journey: ❌️ Mistake 1 - skipping exploratory data analysis (EDA): I used to just load the dat
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@my_life_with_audhd
These are the 2 mistakes I made when I started on my ML journey: ❌️ Mistake 1 - skipping exploratory data analysis (EDA): I used to just load the data and start building a model immediately in my early days. Thats a big mistake. EDA shows you what's actually in your data - outliers, missing values, patterns you'd never catch otherwise. I learnt how the quality of data can influence the model performance. So, now the first step is always EDA. ❌️ Mistake 2 - not testing with unseen data: Initially when I started, I was using the same data to test with. Model performance would be great, but its not actually good, since the model could have memorized for the data given. In real world projects, testing should be done on completely new datasets. In some cases even completely different source of data. These seem obvious now, but would have saved me time and effort if I knew when I started. If you are new to ML, hope this was helpful and follow along to learn more. #machinelearning #dataengineer #datascience #audioml
#Mlmodels Reel by @codevisium - If your ML model is too simple it underfits, if it's too complex it overfits. The key is balancing bias and variance so the model generalizes well on
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@codevisium
If your ML model is too simple it underfits, if it’s too complex it overfits. The key is balancing bias and variance so the model generalizes well on new data. #MachineLearning #ArtificialIntelligence #DataScience #MLEngineering #LearnAI
#Mlmodels Reel by @assignmentonclick - Feature engineering is one of the most powerful steps in the machine learning workflow. In this episode, we explore the fundamentals of feature engine
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@assignmentonclick
Feature engineering is one of the most powerful steps in the machine learning workflow. In this episode, we explore the fundamentals of feature engineering, including how to create new columns, transform raw data, and derive meaningful metrics that improve model performance. This video explains how data scientists transform raw datasets into valuable features that help machine learning algorithms understand patterns more effectively. By applying feature engineering techniques, models can achieve higher accuracy, better generalisation, and improved interpretability. In Episode 25, you will learn practical approaches to creating features from existing data, transforming variables using normalization and encoding techniques, and generating derived metrics such as ratios, time-based variables, and interaction features. The video also demonstrates real-world examples such as retail sales prediction and time-series analysis, showing how feature engineering can uncover deeper insights from datasets. Whether you are a beginner in data science or an experienced analyst looking to refine your workflow, this episode will help you understand why feature engineering is a critical step in building successful machine learning models. Watch this episode to learn how to convert raw data into meaningful features and significantly improve the predictive power of your models. #FeatureEngineering #MachineLearning #DataScience #DataAnalytics #PythonForDataScience #ArtificialIntelligence #MLModels #PredictiveAnalytics #DataPreparation #DataEngineering #AIForBusiness
#Mlmodels Reel by @smart_tech_ai_unfolded - Strong validation score but weak production results? Learn why ML models drop performance after deployment. #machinelearning #mlengineering #modelval
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@smart_tech_ai_unfolded
Strong validation score but weak production results? Learn why ML models drop performance after deployment. #machinelearning #mlengineering #modelvalidation #datadrift #datascience
#Mlmodels Reel by @instructo_edu - Overfitting vs Underfitting - a common challenge in Machine Learning. When building ML models, the goal is to learn patterns from data and make accur
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@instructo_edu
Overfitting vs Underfitting — a common challenge in Machine Learning. When building ML models, the goal is to learn patterns from data and make accurate predictions. But models can sometimes learn too much or too little. 🔹 Overfitting happens when a model learns the training data too well, including noise and unnecessary details. It performs very well on training data but fails to generalize to new, unseen data. 🔹 Underfitting happens when a model is too simple to capture the underlying patterns in the data. As a result, it performs poorly on both training data and new data. The key is finding the right balance — a model that learns meaningful patterns without memorizing the data. Understanding this balance is essential for building reliable Machine Learning systems.
#Mlmodels Reel by @datasciencewithdhrumil - machine learning models don't understand units they understand numbers. when features have different scales, models can give more importance to large
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@datasciencewithdhrumil
machine learning models don’t understand units they understand numbers. when features have different scales, models can give more importance to larger values, leading to poor performance. feature scaling solves this problem by bringing all features to a similar range, allowing the model to learn more effectively. normalization rescales data between 0 and 1, while standardization centers data around the mean. this small step can significantly improve model accuracy and training speed. save this post if you’re learning machine learning step by step. follow @datasciencewithdhrumil for daily data science content. #machinelearning #datascience #artificialintelligence #python #mlalgorithms
#Mlmodels Reel by @simplifyaiml - Most models don't fail because they're bad. They fail because they're either too simple… or too complex. That's the bias-variance tradeoff. Learn to b
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@simplifyaiml
Most models don’t fail because they’re bad. They fail because they’re either too simple… or too complex. That’s the bias–variance tradeoff. Learn to balance it, and your models start making sense. 👉 Follow @simplifyaiml for practical AI concepts #datascience #machinelearning #artificialintelligence #linearregression #python

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