@drrambabupemula - 58 本の動画
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Classification of Iris Flowers using Decision Tree Classifier CART Algorithm || GINI Index || CART
Decision Tree Regression
Regularization Hyperparameters || Decision Tree Classifier
Gini Impurity or Entropy || Decision Tree Classifier
Example for Construction of Decision Tree using Classification And Regression Tree (CART) Algorithm
Computational Complexity || Decision Tree || ID3 || CART
The CART Training Algorithm || Decision Tree
Estimating Class Probabilities || Decision Tree Classifier
Making Predictions || Decision Tree
Training || Visualizing || Decision Tree
Types of Decision Trees Algorithms || ID3 || C4.5 || CART
Decision Tree || Introduction
SVM Regression
Iris Flowers Classification using SVM Classification | Iris Flowers| Iris | SVM | SVC |
SVM Computational Complexity | SVM | Machine Learning
SVM GAUSSIAN RBF KERNEL | SVM| Machine Learning
SVM Adding Similarity Features | SVM | Machine Learning
SVM Polynomial Kernel | SVM | Machine Learning | Classification
Nonlinear SVM Classification || SVM|| SVC|| Classification || Machine Learning
SVM Softmargin Classification || SVM || Classification || ML
Linear SVM Classification || SVM || ML || Classification
Iris Flower Classification Using KNN Classifier
KNN Classifier Algorithm
KNN Classifier Example
Multioutput classification
Linear Regression | Simple Linear Regression
Multilabel Classification
Error Analysis | Machine Learning | Improve Your Model Performance
Multiclass Classification
ROC-AUC Curve Explained | Machine Learning Model Evaluation
Precision-Recall Trade-off Explained | Machine Learning Evaluation Metrics
Precision, Recall, and F1 Score Explained! | Evaluation Metrics for Classification Models
CONFUSION MATRIX|Confusion Matrix in Machine Learning | Precision, Recall, F1-Score
Measuring Accuracy Using Cross Validation
Steps in Machine Learning||Machine Learning||Machine Learning Process||Lecture||Fundamental concepts
Performance Measures ||Machine Learning||Lecture||youtube video
Training a binary classification||MACHINE LEARNING||LECTURE||YOUTUBE VIDEO
MNIST DATASET||MACHINE LEARNING||LECTURE||YOUTUBE VIDEO
ML|| Non -Representative Training Data||LECTURE VIDEO
ML|| Underfitting the training data|| LECTURE||VIDEO
ML||IRRELEVANT FEATURES||LECTURE||VIDEO
MACHINE LEARNING||OVERFITTING THE TRAINING DATA||LECTURE||VIDEO
MACHINE LEARNING|| Insufficient Quantity of Training Data||LECTURE VIDEO
MACHINE LEARNING||POOR QUALITY DATA||LECTURE||VIDEO
MAIN CHALLENGES OF MACHINE LEARNING||LECTURE||VIDEO
ML# INSTANCE-BASED VS MODEL-BASED LEARNING#LECTURE#VIDEO
ML#ONLINE MACHINE LEARNING SYSTEM#LECTURE #VIDEO
ML#BATCH AND ONLINE LEARNING#LECTURE#VIDEO
INTRODUCTION TO MACHINE LEARNING# LECTURE#VIDEO
REINFORCEMENT LEARNING # LECTURE#VIDEO
SEMISUPERVISED UNSUPERVISED#LECTURE#VIDEO
UNSUPERVISED LEARNING#LECTURE# VIDEO
SUPERVISED LEARNING# LECTURE# VIDEO
TYPES OF MACHINE LEARNING# LECTURE# VIDEO
Examples of Machine Learning# Lecture # VIDEO
Why Use Machine Learning
HOW MACHINE LEARNING WORKS||LECTURE||VIDEO
MACHINE LEARNING LESSION 1 - INTRODUCTION #video