Machine Learning

  Machine Learning The Machine Learning Landscape Classification Support Vector Machines Decision Trees Ensemble Learning and Random Forests Dimensionality Reduction Clustering 👉  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 Landscape PPTs 👉 Machine Learning 1: UNIT 1 The Machine Learning Landscape

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 Similarity F

  Machine Learning 👉    About Machine Learning 1 The Machine Learning Landscape Classification Support Vector Machines Decision Trees Ensemble Learning and Random Forests Dimensionality Reduction Clustering 👉   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