Dr. Rambabu Pemula

  Unit – IV Unsupervised Learning and Dimensionality Reduction 1.     Types of Unsupervised Learning, 2.     Challenges in Unsupervised Learning, 3.     Clustering Algorithms: a.      K-Means, b.     Agglomerative, c.      DBSCAN, d.     Comparing and Evaluating Clustering Algorithms. 4.     Dimensionality Reduction: a.      Linear Discriminant Analysis (LDA), b.     Principal Component Analysis (PCA), c.      Factor Analysis (FA).

  ESSENTIALS OF MACHINE LEARNING Course Outcomes: At the end of this course, students will be able to: 1. Understand the Concepts of Machine Learning. 2. Develop Simple Regression Models. 3. Build various classification algorithms. 4. Analyse the need of ensemble learning and dimension reduction. 5. Apply the Clustering algorithms for developing applications. Unit – I:  Introduction to Machine Learning What is Machine Learning, Types of Machine Learning, Applications of Machine learning, preparing to Model, Modelling and Evaluation Unit – II:  Supervised Learning: Regression Introduction to Regression, Example of Regression, Simple Linear Regression, Multiple Linear Regression, Assumptions in Regression Analysis, Improving the accuracy of the Linear Regression Model, Ridge Regression, Lasso Regression. Unit – III:  Supervised Learning: Classification What is Classification, General Approach to Classification, K-Nearest Neighbour Algorithm, L...

  Machine Learning The Machine Learning Landscape Classification Support Vector Machines Decision Trees Ensemble Learning and Random Forests Dimensionality Reduction Clustering 👉YouTube Link:  https://www.youtube.com/@drrambabupemula 👉  Machine Learning 1 Syllabus Unit I: The Machine Learning Landscape : What Is Machine Learning? Why Use Machine Learning? Types of Machine Learning Systems , Supervised /Unsupervised Learning, Batch and Online Learning, Instance-Based Versus Model-Based Learning, Main Challenges of Machine Learning , Insufficient Quantity of Training Data, Nonrepresentative Training Data, Poor-Quality Data, Irrelevant Features, Overfitting the Training Data, Underfitting the Training Data, Stepping Back, Testing and Validating. 👉 UNIT 1(A) NOTEs : The Machine Learning Landscape Notes 👉 UNIT 1(A) PPTs: The Machine Learning Landscape 👉 UNIT 1(B) NOTEs: The Machine Learning Landscape NOTEs 👉 Machine Learning 1 : UNIT 1(B) PPTs: The Machine Learning Lan...

Machine Learning Syllabus  Unit I: The Machine Learning Landscape : What Is Machine Learning? Why Use Machine Learning? Types of Machine Learning Systems, Supervised/Unsupervised Learning, Batch and Online Learning, Instance-Based Versus Model-Based Learning, Main Challenges of Machine Learning, Insufficient Quantity of Training Data, Nonrepresentative Training Data, Poor-Quality Data, Irrelevant Features, Overfitting the Training Data, Underfitting the Training Data, Stepping Back, Testing and Validating. Unit II: Classification: Training a Binary Classifier, Performance Measures, Measuring Accuracy Using Cross-Validation, Confusion Matrix, Precision and Recall, Precision/Recall Tradeoff, The ROC Curve, Multiclass Classification, Error Analysis, Multilabel Classification, Multi Output Classification. k-NN Classifier. Unit III: Support Vector Machines: Linear SVM Classification, Soft Margin Classification, Nonlinear SVM Classification, Polynomial Kernel, Adding Similari...

  Machine Learning 👉    About Machine Learning 1 The Machine Learning Landscape Classification Support Vector Machines Decision Trees Ensemble Learning and Random Forests Dimensionality Reduction Clustering 👉 YouTube Link:  https://www.youtube.com/@drrambabupemula 👉   About Machine Learning 2   Introduction Concept Learning and the General to Specific Ordering Decision   Tree   Learning Artificial Neural Networks Bayesian Learning Instance-Based Learning Genetic Algorithms Learning Sets of Rules Analytical   Learning Reinforcement Learning 👉  About Machine Learning 3 Introduction  Data Pre-processing Performance measurement of models  Supervised Learning  Decision Tree Learning  Unsupervised Learning  Ensemble Models 👉  Machine Learning MCQs 👉  Machine Learning Programs

 Machine Learning MCQs - 4  (Clustering, Dimensionality Reduction) --------------------------------------------------------------------- 1.  Which of the following is finally produced by Hierarchical Clustering? final estimate of cluster centroids tree showing how close things are to each other assignment of each point to clusters all of the mentioned Ans: 2 2.  Which of the following is required by K-means clustering? defined distance metric number of clusters initial guess as to cluster centroids all of the mentioned Ans: 4 3.  Point out the wrong statement. k-means clustering is a method of vector quantization k-means clustering aims to partition n observations into k clusters k-nearest neighbor is same as k-means none of the mentioned Ans: 3 4.  Which of the following combination is incorrect? Continuous – euclidean distance Continuous – correlation similarity Binary – manhattan distance None of the mentioned Ans: 4 5.  Hierarchical clustering shou...