Dr. Rambabu Pemula

  Unit – III Supervised Learning: Classification 1.     What is Classification 2.     General Approach to Classification 3.     K-NearestNeighbour Algorithm 4.     Logistic Regression 5.     Decision Trees: a.      Construction b.     classification and regression trees c.      example 6.     Naive Bayesian Classifier 7.     Support Vector Machines: a.      Optimal Separation b.     Kernels c.      Algorithm 👉 UNIT-II (A) Supervised Learning Classification Notes 👉 UNIT-III (A) Supervised Learning Classification PPTs 👉 UNIT-III(A) Important Long Answer Questions 👉 UNIT-III (A) Supervised Learning: Classification Important Questions YouTube Link

  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

  Measuring Accuracy Using Cross-Validation •         A good way to evaluate a model is to use cross-validation . •         Let’s use the cross_val_score() function to ü   evaluate our SGDClassifier model , ·        using K-fold cross-validation with three folds . •         Remember that K-fold cross-validation means ü   splitting the training set into K folds (in this case, three), then ·        making predictions and ·        evaluating them on each fold using ü   a model trained on the remaining folds . from sklearn.model_selection import cross_val_score cross_val_score ( sgd_clf , X_train , y_train_5 , cv = 3 , scoring = "accuracy" )                  ...

 WEEK 9 - Write a program to implement SVM algorithm to classify the iris data set. Print both correct and wrong predictions.