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

  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" )                         array([0.96355, 0.93795, 0.95615]) ü   Above 93% accuracy (ratio of correct predictions) on all cross-validation folds? ü   This looks amazing, doesn’t it? ü   let’s look at a very dumb classifier that just classifies every single image in the “not-5” class: from sklearn.

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

  Classification of Iris flowers using Random Forest Steps: 1. Importing the library files 2. Reading the Iris Dataset 3. Preprocessing 4. Split the dataset into training and testing 5. Build the model (Random Forest Model) 6. Evaluate the performance of the Model 1. Importing the library files 2. Reading the Iris Dataset 3. Preprocessing 4. Split the dataset into training and testing 5. Build the model (Random Forest Model) sklearn.ensemble .RandomForestClassifier class   sklearn.ensemble. RandomForestClassifier ( n_estimators = 100 ,  * ,  criterion = 'gini' ,  max_depth = None ,  min_samples_split = 2 ,  min_samples_leaf = 1 ,  min_weight_fraction_leaf = 0.0 ,  max_features = 'sqrt' ,  max_leaf_nodes = None ,  min_impurity_decrease = 0.0 ,  bootstrap = True ,  oob_score = False ,  n_jobs = None ,  random_state = None ,  verbose = 0 ,  warm_start = False ,  class_weight = None ,  ccp_alpha = 0.0 ,  max_samples = None ) A random forest classifier. A random forest is a