Machine learning (ML)
*Machine learning (ML) hai ek subset of artificial intelligence (AI) jo computers ko data se seekhne aur predictions ya decisions lene ki capability provide karta hai.* _Machine Learning ki Key Features:_ 1. _Data-driven_: ML algorithms data par based hote hain aur data se seekhte hain. 2. _Self-improvement_: ML algorithms apne performance ko improve karte hain jab ve adhik data par train hote hain. 3. _Prediction_: ML algorithms unknown data par predictions ya decisions lene ke liye use kiye jate hain. 4. _Automation_: ML algorithms manual intervention ke bina tasks ko automate kar sakte hain. _Machine Learning ke Types:_ 1. _Supervised Learning_: Isme algorithm ko labeled data par train kiya jata hai. 2. _Unsupervised Learning_: Isme algorithm ko unlabeled data par train kiya jata hai. 3. _Reinforcement Learning_: Isme algorithm ko environment se feedback milta hai aur vah apne actions ko adjust karta hai. 4. _Deep Learning_: Isme neural networks ka use kiya jata hai jo complex patterns ko recognize kar sakte hain. _Machine Learning ke Applications:_ 1. _Image Recognition_: ML algorithms images ko recognize aur classify kar sakte hain. 2. _Natural Language Processing (NLP)_: ML algorithms text aur speech ko understand aur process kar sakte hain. 3. _Predictive Analytics_: ML algorithms historical data par based predictions ya decisions lene ke liye use kiye jate hain. 4. _Recommendation Systems_: ML algorithms users ko personalized recommendations provide kar sakte hain. _Machine Learning ke Benefits:_ 1. _Improved Accuracy_: ML algorithms data se seekhne ke kaaran accurate predictions ya decisions lene ke liye use kiye jate hain. 2. _Increased Efficiency_: ML algorithms manual tasks ko automate kar sakte hain aur productivity ko badha sakte hain. 3. _Enhanced Customer Experience_: ML algorithms users ko personalized recommendations aur services provide kar sakte hain. 4. _Competitive Advantage_: ML algorithms companies ko competitive advantage provide kar sakte hain aur unhe market mein ahead rakh sakte hain. _Machine Learning ke Challenges:_ 1. _Data Quality_: ML algorithms ke liye high-quality aur relevant data ki zaroorat hoti hai. 2. _Model Complexity_: ML models ko train aur deploy karne ke liye specialized skills ki zaroorat hoti hai. 3. _Explainability_: ML models ke decisions ko understand aur explain karne ke liye challenges hote hain. 4. _Bias aur Fairness_: ML models mein bias aur unfairness ke issues hote hain jo address kiye jane ki zaroorat hoti hai.