@datascibykashi: 1️⃣ Supervised Learning • Learns from labeled data (data with correct answers). • Example: Predicting house prices 🏠 based on past sales. • Common Algorithms: Linear Regression, Decision Trees. 2️⃣ Unsupervised Learning • Learns from unlabeled data (no answers given). • Finds patterns, groups, or structures in the data. • Example: Grouping customers 🛒 based on buying behavior. • Common Algorithms: K-Means Clustering, PCA. 3️⃣ Semi-Supervised Learning • Uses a small amount of labeled data + a large amount of unlabeled data. • Example: Identifying diseases in medical images 🩻 when only some images are labeled. • Common Algorithms: Self-training, Semi-supervised SVM. 4️⃣ Reinforcement Learning • Learns by trial and error with rewards & penalties. • Example: A robot learning to walk 🤖 or an AI winning in chess ♟️. • Famous Example: AlphaGo. ⸻ 🎯 Quick Tip: • Answers given → Supervised • No answers → Unsupervised • Few answers + many without → Semi-Supervised • Trial & reward → Reinforcement ⸻ 💬 Which type should I explain next in detail? 🔁 Save this for your ML learning journey! #100DaysOfML #MachineLearning #SupervisedLearning #UnsupervisedLearning #SemiSupervisedLearning #ReinforcementLearning #AIForBeginners #PakistanTech #datascibykashi

Data Scientist | Kashi

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Friday 08 August 2025 15:42:01 GMT