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Support Vector Machine Fundamentals

Track :

Computer Science

Lessons no : 49

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What will you learn in this course?
  • Master how Support Vector Machines optimize data separation for accurate classification tasks using kernel functions and margin maximization techniques
  • Apply SVM algorithms to real-world datasets for effective binary and multi-class classification problems
  • Implement SVM models with different kernel types to handle linear and non-linear data distributions in practical scenarios
  • Evaluate SVM performance metrics such as accuracy, precision, recall, and F1-score for model assessment and improvement
  • Utilize optimization techniques like quadratic programming to enhance SVM training efficiency and effectiveness
  • Configure hyperparameters such as C and gamma to optimize SVM model performance on diverse datasets
  • Identify the strengths and limitations of Support Vector Machines in various classification contexts and data complexities
  • Integrate SVM algorithms into machine learning workflows for scalable and robust data analysis solutions
  • Troubleshoot common issues in SVM implementation, including overfitting, underfitting, and kernel selection challenges
  • Compare SVM with other classification algorithms like logistic regression and decision trees for informed model selection
  • Apply cross-validation and grid search techniques to tune SVM models for optimal results
  • Develop practical skills to deploy SVM models in real-world applications across industries such as finance, healthcare, and image recognition

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Lessons | 49
Machine Learning And Deep Learning Fundamentals And Applications Introduction Video
00:14:36

Machine Learning And Deep Learning Fundamentals And Applications Introduction Video
Lec 1 Introduction to Machine Learning
01:38:18

Lec 1 Introduction to Machine Learning
Lec 2 Performance Measures of Classification
00:39:56

Lec 2 Performance Measures of Classification
Lec 3 Bias Variance Tradeoff
00:51:37

Lec 3 Bias Variance Tradeoff
Lec 4 Regression
01:17:43

Lec 4 Regression
Lec 5 Bayesian Decision Theory
00:45:38

Lec 5 Bayesian Decision Theory
Lec 6 Bayesian Decision Theory
00:49:05

Lec 6 Bayesian Decision Theory
Lec 7 Bayes Decision Theory Binary Features
01:05:49

Lec 7 Bayes Decision Theory Binary Features
Lec 8 Bayesian Decision Theory
00:54:53

Lec 8 Bayesian Decision Theory
Lec 9 Bayesian Decision Theory
00:31:08

Lec 9 Bayesian Decision Theory
Lec 10 Bayesian Belief Networks
00:59:10

Lec 10 Bayesian Belief Networks
Lec 11 Parameter Estimation and Maximum Likelihood Estimation
00:45:10

Lec 11 Parameter Estimation and Maximum Likelihood Estimation
Lec 12 Parameter Estimation and Bayesian Estimation
00:51:18

Lec 12 Parameter Estimation and Bayesian Estimation
Lec 13 Concept of non parametric techniques
00:44:18

Lec 13 Concept of non parametric techniques
Lec 14 Density Estimation by Parzen Window
00:37:03

Lec 14 Density Estimation by Parzen Window
Lec 15 Parzen Window and K nearest neighbor algorithm
00:40:42

Lec 15 Parzen Window and K nearest neighbor algorithm
Lec 16 Linear Discriminant Functions and Perceptron Criteria Part I
00:41:44

Lec 16 Linear Discriminant Functions and Perceptron Criteria Part I
Lec 17 Linear Discriminant Functions and Perceptron Criteria Part II
00:41:49

Lec 17 Linear Discriminant Functions and Perceptron Criteria Part II
Lec 18 Linear Discriminant Functions and Perceptron Criteria Part III
00:53:04

Lec 18 Linear Discriminant Functions and Perceptron Criteria Part III
Lec 19 Support Vector Machine Part I
00:57:04

Lec 19 Support Vector Machine Part I
Lec 20 Support Vector Machine Part II
00:42:09

Lec 20 Support Vector Machine Part II
Lec 21 Logistic Regression
00:39:39

Lec 21 Logistic Regression
Lec 22 Decision Tree
00:55:26

Lec 22 Decision Tree
Lec 23 Hidden Markov Model HMM
01:02:40

Lec 23 Hidden Markov Model HMM
Lec 24 Ensemble Classifiers Part I
00:34:57

Lec 24 Ensemble Classifiers Part I
Lec 25 Ensemble Classifiers Part II
00:41:59

Lec 25 Ensemble Classifiers Part II
Lec 26 Dimensionality Problem and Principal Component Analysis
00:57:08

Lec 26 Dimensionality Problem and Principal Component Analysis
Lec 27 Principal Component Analysis
00:53:08

Lec 27 Principal Component Analysis
Lec 28 Linear Discriminant Analysis LDA Part I
00:57:13

Lec 28 Linear Discriminant Analysis LDA Part I
Lec 29 Linear Discriminant Analysis LDA Part II
00:49:50

Lec 29 Linear Discriminant Analysis LDA Part II
Lec 30 Gaussian Mixture Model and EM Algorithm
00:51:45

Lec 30 Gaussian Mixture Model and EM Algorithm
Lec 31 K means clustering
00:35:19

Lec 31 K means clustering
Lec 32 Fuzzy K means clustering
00:38:52

Lec 32 Fuzzy K means clustering
Lec 33 Hierarchical Agglomerative Clustering and Mean shift Clustering
00:34:32

Lec 33 Hierarchical Agglomerative Clustering and Mean shift Clustering
Lec 34 Artificial Neural Networks for Pattern Classification PART 1
01:06:06

Lec 34 Artificial Neural Networks for Pattern Classification PART 1
Lec 35 Artificial Neural Networks for Pattern Classification PART 2
00:49:27

Lec 35 Artificial Neural Networks for Pattern Classification PART 2
Lec 36 Artificial Neural Networks for Pattern Classification PART 3
01:00:00

Lec 36 Artificial Neural Networks for Pattern Classification PART 3
Lec 37 Introduction to Deep Learning and Convolutional Neural Network CNN
00:52:46

Lec 37 Introduction to Deep Learning and Convolutional Neural Network CNN
Lec 38 Vanishing and Exploding Gradients in Deep Neural Networks
01:02:30

Lec 38 Vanishing and Exploding Gradients in Deep Neural Networks
Lec 39 CNN Architectures LeNet 5 and AlexNet
00:34:44

Lec 39 CNN Architectures LeNet 5 and AlexNet
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Ahmad Zakaria 

Very Usefull
2025-03-25

Babeetha Selvamani 

The course was very explanatory and the professors gave a detailed approach.
2024-01-19

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Support Vector Machine course, In this course we will learn about the Support Vector Machine algorithm, its applications in classification, and optimization techniques for effective data separation.