Ch 16: Machine Learning | Exam Fusion Prep

Introduction to Machine Learning / मशीन लर्निंग का परिचय

Machine Learning enables computers to learn from data and make decisions.
The chapter frames ML around History, Algorithms and Applications.
The source later summarizes that Machine Learning is transforming industries worldwide.

हिंदी सार

मशीन लर्निंग कंप्यूटर को डेटा से सीखने और निर्णय लेने में सक्षम बनाती है। इस अध्याय में इतिहास, मशीन लर्निंग के प्रकार, एल्गोरिदम, मूल्यांकन मेट्रिक्स और अनुप्रयोग दिए गए हैं।

Source-derived visual: three learning types listed in the PDF.

History of Machine Learning / मशीन लर्निंग का इतिहास

Timeline built only from the dates and milestones stated in the source.

Period / Year	Source milestone
1950s	Alan Turing introduced the Turing Test.
1959	Arthur Samuel defined ML.
1980s–1990s	Neural networks evolved.
2000s–present	Deep learning revolution.

Types of Machine Learning / मशीन लर्निंग के प्रकार

Supervised Unsupervised Reinforcement Learning

Classification exactly follows the three categories shown in the source.

Supervised Learning / पर्यवेक्षित शिक्षण

Uses labeled data to train models.

Examples: Linear Regression, Decision Trees, SVM.

Unsupervised Learning / अपर्यवेक्षित शिक्षण

Finds hidden patterns in data.

Examples: K-Means Clustering, PCA.

Reinforcement Learning / सुदृढीकरण शिक्षण

Uses rewards & penalties to learn.

Examples: Q-Learning, Deep Q-Networks.

Exam Trap

K-Means is placed under Unsupervised Learning in the source; Q-Learning is under Reinforcement Learning.

Machine Learning Algorithms / मशीन लर्निंग एल्गोरिदम

Algorithm groups and evaluation metrics from the source.

Group	Examples listed in source
Regression / रिग्रेशन	Linear, Logistic
Classification	Decision Tree, Naïve Bayes
Clustering	K-Means, Hierarchical
Neural Networks & Deep Learning	Listed as an algorithm area in the chapter.

Evaluation Metrics / मूल्यांकन मेट्रिक्स

Regression Metrics

MSEMAER² Score

Classification Metrics

AccuracyPrecisionRecallF1-score

Applications of Machine Learning / मशीन लर्निंग के अनुप्रयोग

The four application areas named in the PDF.

Healthcare / स्वास्थ्य सेवा

Named as an application area.

Finance / वित्त

Named as an application area.

E-commerce / ई-कॉमर्स

Named as an application area.

Autonomous Vehicles / स्वायत्त वाहन

Named as an application area.

MCQs for Practice / अभ्यास के प्रश्न

Who is the father of ML?
Source answer: Arthur Samuel.

What type of learning is K-Means?
Source answer: Unsupervised.

What algorithm is used in spam detection?
Source answer: Naïve Bayes.

Conclusion in source

Machine Learning is transforming industries worldwide.
मशीन लर्निंग वैश्विक उद्योगों को बदल रही है।

Quick Recall / त्वरित पुनरावृत्ति

Core Idea

Machine Learning → Learn from Data → Make Decisions.

Term	Recall cue
1950s	Alan Turing — Turing Test.
1959	Arthur Samuel defined ML.
Supervised	Labeled data.
Unsupervised	Hidden patterns; K-Means, PCA.
Reinforcement	Rewards & penalties; Q-Learning, Deep Q-Networks.
Regression	Linear, Logistic.
Classification	Decision Tree, Naïve Bayes.
Clustering	K-Means, Hierarchical.
Regression metrics	MSE, MAE, R² Score.
Classification metrics	Accuracy, Precision, Recall, F1-score.
Applications	Healthcare, Finance, E-commerce, Autonomous Vehicles.

Exam Trap

Supervised = labeled data; Unsupervised = hidden patterns; Reinforcement = rewards & penalties.

Exam Trap

The source practice question gives Arthur Samuel as the answer to “Who is the father of ML?”