Explore tweets tagged as #mlbasics
π Overfitting vs. Underfitting β MLβs Balancing Act! Too complex? Your model memorizes noise. Too simple? It misses patterns. π― The goal: Just the right fit for accurate predictions!. Follow #AI365 π #MachineLearning #AI #DataScience #MLBasics
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π₯New video for Python & ML beginners!. Variables, Data Types & Type Casting in Python is LIVE .A must-watch if you're building your AI/ML skills from scratch. π Watch: #python #ai #machinelearning #typecasting #mlbasics #learnpython
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AI and MLβ buzzwords everywhere, but do you really know the difference?. #AI #MachineLearning #ArtificialIntelligence #ML #TechTalk #TechExplained.#FutureOfTechnology.#Innovation #DeepLearning #AITech #AIVsML #AIML.#UnderstandingAI #MLBasics.#AIExplained #TechEducation
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Reinforcement learning starts with a strong foundation. Learn its formulation here: #AI #MLBasics #DataScience
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What is Max Margin Classification, and why does it matter? Unlock its significance in this concise video: #MLBasics #AI #DataScience
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Gradient Descent: The engine behind regression solutions. Discover how it works step by step: #AI #MLBasics #DataScience
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Day 1 β #60DaysOfLearning2025.Normalization vs Standardization I covered:.β
What they do.β
Math behind them.β
When & why to use.β
Code examples on real data. Github: #LSPPDay1 #LearningWithLeapfrog #MLBasics #DataPrep #60DaysChallenge.@lftechnology
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Convolutions: The secret behind image recognition. Uncover their power in this brief video: #AI #DeepLearning #MLBasics
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π€ Introduction to Machine Learning β Summary.A concise overview of machine learning: key concepts, types of models, and real-world applications. π Read the summary: #MachineLearning #AI #DataScience #MLBasics #XNewData #ArtificialIntelligence
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Supervised learning is like teaching a child with flashcards β the machine learns from labeled data to make accurate predictions. #SupervisedLearning #MachineLearning #AI #ArtificialIntelligence #DataScience #MLBasics #DeepLearning #AIExplained #LearnAI #Codestreamlab
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Day 93: Revisited types of ML based on learning styles: supervised, unsupervised, semi-supervised, and reinforcement learning along with offline/online learning and instance/model-based methods. ππ‘ Gaining deeper clarity on various approaches! #MachineLearning #MLBasics
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π§΅ Strings in Python - essential for ML/AI workflows!.Covers string methods, slicing, and formatting tips. Watch here π #python #ai #machinelearning #stringmanipulation #mlbasics #learnpython
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Deep Learning 101: Lesson 10: Key Concepts and Techniques. #DeepLearning #AIIntroduction #MachineLearning #NeuralNetworks #AIConcepts #101ai #101ainet #ArtificialIntelligence #MLBasics #AI101
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AI vs. ML: Know the Difference.π€ .#ArtificialIntelligence #MachineLearning #AIvsML #TechEducation #DataScience #DeepLearning #AIExplained #MLBasics #KnowTheDifference
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Deep Learning 101: Lesson 1: Data Scaling. #DataScaling #MachineLearning #AI #DataNormalization #Standardization #MLBasics #DataScience #DataPreparation #101ai #101ainet
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Deep Learning 101: Lesson 3: Loss and Metric. #LossFunction #ModelMetrics #MachineLearning #ModelEvaluation #AI #DeepLearning #MLBasics #DataScience #101ai #101ainet
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Deep Learning 101: Lesson 2: Linear Regression. #LinearRegression #MachineLearning #DataAnalysis #RegressionModels #StatisticalModeling #MLBasics #AI #DataScience #101ai #101ainet
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