Study Material

Machine Learning focuses on algorithms that allow computers to learn patterns directly from data without being explicitly programmed. [1]  *  * Tom Mitchell's Definition: A computer program is said to learn from Experience ($E$) with respect to some class of Tasks ($T$) and Performance measure ($P$), if its performance at tasks in $T$, as measured by $P$, improves with experience $E$. * Data: The raw foundation, which can be structured, semi-structured, or unstructured. * Model: A mathematical representation of a real-world process derived from data. * Loss Function: A mathematical metric quantifying how much a model's prediction deviates from the true target value. [1, 2, 3]  *  ------------------------------ ## Machine Learning Frameworks & Paradigm Comparison                          [ Machine Learning Paradigms ]                        ...

  Machine Learning Techniques (MCA556) From your syllabus. Dimensionality Reduction In Machine Learning, datasets may contain many features (columns). Example: Student Data ------------ Name Age Gender Address Attendance Marks Projects Activities ... Too many features can: Increase training time Increase memory usage Cause overfitting Dimensionality Reduction reduces the number of features while preserving important information. Benefits Faster computation Less storage Better visualization Reduced overfitting Principal Component Analysis (PCA) Most important dimensionality reduction technique. Purpose: Convert many features into fewer important features. Idea: Preserve maximum variance. Reduce dimensions. Example: 100 Features ↓ PCA ↓ 10 Important Features Applications: Face Recognition Image Compression Data Visualization Linear Discriminant Analysis (LDA) Used for: Dimensionality Reduction Classification Difference: ...

 Machine Learning Techniques (MCA556) From your syllabus.  --- Learning with Trees Decision Trees are one of the most popular machine learning algorithms. They make decisions using a tree-like structure. Example: Study Hours?       |    > 5 Hours       |      Pass    < 5 Hours       |      Fail --- Components of Decision Tree Root Node Starting point of the tree. Example: Study Hours? --- Internal Node Represents a condition. Example: Attendance > 75%? --- Leaf Node Final prediction. Example: Pass Fail --- Advantages of Decision Trees Easy to understand Easy to visualize Works with numerical and categorical data Requires little data preparation --- Disadvantages Can overfit Sensitive to data changes Large trees become complex --- Constructing Decision Trees Steps: 1. Select best feature 2. Split dataset 3. Create branches 4. Repeat recursively 5. Stop when classification is ...

  Machine Learning Techniques (MCA556) From your syllabus. Supervised Learning In supervised learning: Input data is given Correct output (label) is known Model learns relationship between input and output Examples: Spam detection Disease prediction Student result prediction Logistic Regression Used for classification problems . Unlike Linear Regression, Logistic Regression predicts categories. Examples: Pass / Fail Spam / Not Spam Yes / No Sigmoid Function Logistic Regression uses the Sigmoid Function. Output range: 0 to 1 Interpretation: Close to 1 → Positive Class Close to 0 → Negative Class Applications of Logistic Regression Email spam detection Disease diagnosis Loan approval Fraud detection Support Vector Machine (SVM) SVM is a powerful classification algorithm. Goal: Find the best boundary that separates classes. Example: Students Pass ● ● ● ● ----------- Boundary ○ ○ ○ ○ Fail The boundary is called a: Hyperplane A...

  From your syllabus. Evaluation Metrics Evaluation metrics help us measure how good a machine learning model is. Confusion Matrix Used for classification problems. Actual / Predicted Positive Negative Positive TP FN Negative FP TN Where: TP = True Positive TN = True Negative FP = False Positive FN = False Negative Precision Measures how many predicted positives are actually correct. Example: If 100 emails are predicted as spam and 90 are actually spam: Precision = 90% Recall Measures how many actual positives were correctly identified. Example: Out of 100 spam emails, if system detects 80: Recall = 80% F1 Score Balance between Precision and Recall. Higher F1 Score means better model. Mean Squared Error (MSE) Used in regression models. Measures average squared prediction error. Smaller MSE = Better model. Flexibility vs Interpretability Flexible Models Examples: Neural Networks Deep Learning Advantages: High accu...

 Subject: Machine Learning Techniques (MCA556) From your Semester III syllabus.  --- What is Machine Learning? Machine Learning (ML) is a branch of Artificial Intelligence (AI) that enables computers to learn from data and make decisions without being explicitly programmed. Example Netflix recommends movies. YouTube recommends videos. Gmail detects spam emails. --- Basic Definitions Data Raw facts and figures. Example: Age = 20 Marks = 85 Dataset Collection of data. Example: Age Marks 18 70 19 75 20 85 --- Learning Learning means improving performance using experience (data). Formula: Experience + Data → Learning → Better Predictions --- Types of Machine Learning The syllabus covers several learning types.  1. Supervised Learning Data contains inputs and correct outputs (labels). Examples: Predicting house prices Predicting exam results Algorithms: Linear Regression Decision Trees SVM --- 2. Unsupervised Learning Data has no labels. Purpose: Find hidden patterns Group sim...