Machine Learning and Deep Learning In One Semester
Language: English | Size:16.37 GB
Genre:eLearning
Files Included :
1 Introduction of the course.mp4 (34 MB)
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1 Introduction of the section.mp4 (11.61 MB)
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2 Fundamentals of Probability.mp4 (54.96 MB)
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3 Conditional Probability and Bayes Theorem.mp4 (35.26 MB)
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4 Numerical Example on Bayes Theorem.mp4 (18.94 MB)
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5 Naive Bayes Classification.mp4 (33.98 MB)
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6 Comparing Naive Bayes Classification With Logistic Regression.mp4 (18.69 MB)
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7 Project01 Naive Bayes as probabilistic classifier.mp4 (51.53 MB)
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8 Project02 Comparing Naive Bayes and Logistic Regression.mp4 (108.98 MB)
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9 Project03 Multiclass Classification With Naive Bayes Classifier.mp4 (194.96 MB)
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1 Introduction of the Section.mp4 (4.23 MB)
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10 Project03-Grid Search CV-Part01.mp4 (131.07 MB)
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11 Project03-Grid Search CV-Part02.mp4 (15.52 MB)
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12 Project04-Breast Cancer Classification with SVM.mp4 (58.29 MB)
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2 Basic Concept of SVM.mp4 (23.52 MB)
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3 Maths of SVM.mp4 (47.5 MB)
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4 Hard and Soft Margin Classifier.mp4 (15.64 MB)
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5 Decision rules of SVM.mp4 (17.21 MB)
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6 Kernel trick in SVM.mp4 (28.72 MB)
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7 Project01-Understanding SVM-Part01.mp4 (158.42 MB)
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8 Project01-Understanding SVM-Part02.mp4 (80.82 MB)
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9 Project02-Multiclass Classification With SVM.mp4 (84.88 MB)
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1 Introduction of the Section.mp4 (6.88 MB)
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10 Project02-Breast Cancer Classification With Decision Tree.mp4 (60.04 MB)
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11 Project03-Grid Search CV with Decision Tree.mp4 (171.05 MB)
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2 Concept of Decision Tree.mp4 (23.05 MB)
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3 Important terms related to decision tree.mp4 (24.17 MB)
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4 Entropy-An information gain criterion.mp4 (27.91 MB)
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5 Numerical Example on Entropy-Part01.mp4 (115.06 MB)
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6 Numerical Example on Entropy-Part02.mp4 (57.25 MB)
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7 Gini Impurity - An information criterion.mp4 (10.97 MB)
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8 Numerical Example on Gini Impurity.mp4 (87.35 MB)
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9 Project01-Decision Tree Implementation.mp4 (95.93 MB)
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1 Introduction of the Section.mp4 (5.73 MB)
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2 Why Random Forest.mp4 (18.99 MB)
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3 Working of Random Forest.mp4 (16.1 MB)
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4 Hyperparameters of Random Forest.mp4 (33.29 MB)
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5 Bootstrap sampling and OOB Error.mp4 (37.24 MB)
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6 Project01-Random Forest-Part01.mp4 (71.79 MB)
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7 Project01-Random Forest-Part02.mp4 (57.84 MB)
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8 Project02-Random Forest-Part01.mp4 (88.63 MB)
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9 Project02-Random Forest-Part02.mp4 (70.98 MB)
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1 Introduction of the Section.mp4 (4.6 MB)
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10 Extreme Gradient Boosting ( XGBoost ).mp4 (14.59 MB)
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11 Project05-XGBoost-Part01.mp4 (108.76 MB)
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12 Project05-XGBoost-Part02.mp4 (20.29 MB)
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2 AdaBoost (Adaptive Boosting ).mp4 (45.87 MB)
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3 Numerical Example on Adaboost.mp4 (61.08 MB)
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4 Project01-AdaBoost Classifier.mp4 (96.85 MB)
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5 Project02-AdaBoost Classifier.mp4 (107.39 MB)
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6 Gradient Boosting.mp4 (12.78 MB)
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7 Numerical Example on Gradient Boosting.mp4 (47.5 MB)
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8 Project03-Gradient Boosting.mp4 (81.39 MB)
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9 Project04-Gradient Boosting.mp4 (104.06 MB)
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1 Deep Learning.mp4 (6.95 MB)
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1 Introduction of the Section.mp4 (5.44 MB)
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2 The perceptron.mp4 (65.16 MB)
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3 Features, Weights and Activation Function.mp4 (23.4 MB)
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4 Learning of Neural Network.mp4 (35.43 MB)
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5 Rise of Deep Learning.mp4 (39.76 MB)
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1 Introduction of the Section.mp4 (7.28 MB)
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2 Classification by Perceptron-Part01.mp4 (26.46 MB)
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3 Classification by Perceptron-Part02.mp4 (26.11 MB)
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4 Need of Activation Functions.mp4 (19.58 MB)
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5 Adding Activation Function to Neural Network.mp4 (16.54 MB)
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6 Sigmoid as Activation Function.mp4 (34.38 MB)
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7 Hyperbolic Tangent Function.mp4 (25.66 MB)
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8 ReLU and Leaky ReLU Function.mp4 (23.13 MB)
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1 Introduction of the Section.mp4 (5.16 MB)
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2 MSE Loss Function.mp4 (14.19 MB)
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3 Cross Entropy Loss Function.mp4 (37.52 MB)
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4 Softmax Function.mp4 (35.53 MB)
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1 Introduction of the Section.mp4 (4.99 MB)
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2 Forward Propagation.mp4 (31.89 MB)
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3 Backward Propagation-Part01.mp4 (60.34 MB)
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4 Backward Propagation-Part02.mp4 (40.04 MB)
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1 Introduction of the Section.mp4 (4.7 MB)
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2 What in Intelligence.mp4 (31.03 MB)
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3 Machine Learning.mp4 (17.07 MB)
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4 Supervised Machine Learning.mp4 (51.2 MB)
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5 Unsupervised Machine Learning.mp4 (20.87 MB)
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5 1 5-Unsupervised Machine Learning.mp4 (5.55 MB)
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6 Deep Learning.mp4 (39.08 MB)
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1 Introduction of the Section.mp4 (3.83 MB)
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2 Project01-Neural Network for Simple Regression-Part01.mp4 (140.83 MB)
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3 Project01-Neural Network for Simple Regression-Part02.mp4 (162.5 MB)
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4 Project02 Neural Network for Multiple Regression.mp4 (101.92 MB)
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5 Creating Neural Network Using Python Class.mp4 (46.31 MB)
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1 Introduction of the Section.mp4 (13.18 MB)
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10 Code Preparation for Custom Images.mp4 (24.52 MB)
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11 Project03-Neural Networks for Custom Images.mp4 (196.28 MB)
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12 Code Preparation for Human Action Recognition.mp4 (16.01 MB)
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13 Project04-Neural Network for Human Action Recognition.mp4 (133.23 MB)
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14 Project05-Neural Network for Feature Engineered Dataset.mp4 (92.32 MB)
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2 Epoch, Batch size and Iteration.mp4 (22.75 MB)
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3 Project00 Tensor Dataset and Data Loader.mp4 (113.24 MB)
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4 Code Preparation for Iris Dataset.mp4 (39.6 MB)
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5 Project01 Neural Network for Iris Data Classification.mp4 (128.74 MB)
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6 Code Preparation for MNIST dataset.mp4 (20.19 MB)
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7 Project02 Neural Network for MNIST data classification-Part01.mp4 (131.19 MB)
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8 Project02 Neural Network for MNIST data classification-Part02.mp4 (177.26 MB)
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9 Save and Load Trained model.mp4 (45.93 MB)
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1 Introduction of the Section.mp4 (4.14 MB)
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2 Dropout Regularization.mp4 (57.66 MB)
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3 Introducing Dataset for dropout Regularization.mp4 (12.81 MB)
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4 Project01-Dropout Regularization.mp4 (221.95 MB)
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5 Project02-Dropout Regularization.mp4 (120.16 MB)
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6 Batch Normalization.mp4 (29.85 MB)
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7 Project03-Batch Normalization.mp4 (188.53 MB)
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8 Project04-Batch Normalization.mp4 (91.24 MB)
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1 Introduction of the Section.mp4 (6.74 MB)
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10 Project02 CNN on Custom Images.mp4 (114.66 MB)
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11 Transfer Learning.mp4 (23.32 MB)
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12 Project03-Transfer Learning With ResNet-18.mp4 (119.99 MB)
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13 Project04-Transfer Learning With VGG-16.mp4 (59.24 MB)
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2 CNN Architecture and main operations.mp4 (15.42 MB)
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3 2D Convolution.mp4 (35.2 MB)
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4 Shape of Feature Map after Convolution.mp4 (51.06 MB)
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5 Average and Maximum Pooling.mp4 (23.15 MB)
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6 Pooling to Classification.mp4 (21.54 MB)
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7 Project01-CNN on MNIST-Part01.mp4 (192.26 MB)
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8 Project01-CNN on MNIST-Part02.mp4 (97.12 MB)
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9 An Efficient Lazy Linear Layer.mp4 (36.13 MB)
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1 Introduction of the Section.mp4 (7.49 MB)
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10 Batch size, Sequence length and Feature dimension.mp4 (59.16 MB)
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11 Project03-Interpolation and Extrapolation With LSTM.mp4 (166.46 MB)
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12 Project04- Data classification with LSTM.mp4 (129.77 MB)
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2 Why we need RNN.mp4 (15.46 MB)
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3 Sequential data.mp4 (14.69 MB)
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4 ANN to RNN.mp4 (27.38 MB)
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5 Back Propagation Through Time.mp4 (68.17 MB)
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6 Long-Short Term Memory ( LSTM ).mp4 (18.83 MB)
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7 LSTM Gates.mp4 (48.09 MB)
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8 Project01-LSTM Shapes.mp4 (204.88 MB)
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9 Project02-LSTM Basics.mp4 (91.81 MB)
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1 Introduction of the Section.mp4 (5.63 MB)
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2 Architecture of Autoencoder.mp4 (35.83 MB)
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3 Applications of Autoencoders.mp4 (27.51 MB)
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4 Project01-Image Denoising using Autoencoder.mp4 (117.17 MB)
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5 Project02-Occlusion Removing Using Autoencoder.mp4 (34.43 MB)
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6 Project03-Autoencoder as an Image Classifier.mp4 (92.15 MB)
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1 Introduction of the Section.mp4 (2.8 MB)
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2 Discriminative and Generative Models.mp4 (10.53 MB)
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3 Training of GAN.mp4 (31.64 MB)
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4 Project01 GAN Implementation.mp4 (177.4 MB)
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1 Unsupervised Machine Learning.mp4 (4.93 MB)
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1 Introduction of the Section.mp4 (6.63 MB)
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10 Project01-K-Means Clustering-Part03.mp4 (20.44 MB)
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11 Project02-K-Means Clustering.mp4 (77.05 MB)
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12 Project03-K-Means Clustering.mp4 (54.98 MB)
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2 Steps of K-Means Clustering.mp4 (35.71 MB)
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3 Numerical Example- K-Means Clustering in One-D.mp4 (31.03 MB)
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4 Numerical Example-K-Means Clustering in 2D.mp4 (42.77 MB)
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5 Objective Function of K-Means Clustering.mp4 (7.02 MB)
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6 Selecting Optimal Number of Clusters ( Elbow Method ).mp4 (41.1 MB)
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7 Evaluating Metric for K-Means Clustering.mp4 (36.13 MB)
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8 Project01-K-Means Clustering-Part01.mp4 (86.23 MB)
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9 Project01-K-Means Clustering-Part02.mp4 (89.76 MB)
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1 Introduction of the Section.mp4 (9.17 MB)
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10 Project01-Hierarchical Clustering-Part02.mp4 (49.3 MB)
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11 Project02-Hierarchical Clustering.mp4 (51.31 MB)
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12 Project03-Hierarchical Clustering.mp4 (43.98 MB)
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2 Hierarchical Clustering Algorithm.mp4 (24.57 MB)
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3 Hierarchical Clustering in One-D.mp4 (16.19 MB)
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4 Dendrograms-Selecting Optimal Clusters-Part01.mp4 (25.69 MB)
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5 Dendrograms-Selecting Optimal Clusters-Part02.mp4 (23.25 MB)
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6 Hierarchical Clustering Using d-max criterion.mp4 (35.73 MB)
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7 Hierarchical Clustering in 2D.mp4 (25.27 MB)
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8 Evaluating Metrics for Hierarchical Clustering.mp4 (15.84 MB)
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9 Project01-Hierarchical Clustering-Part01.mp4 (85.2 MB)
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1 Introduction of the Section.mp4 (8.5 MB)
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2 Importing Dataset in Google Colab.mp4 (81.06 MB)
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3 Importing and Displaying Image in Google Colab.mp4 (50.15 MB)
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4 Importing more datasets.mp4 (28.89 MB)
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5 Uploading Course Material on your Google Drive.mp4 (12.01 MB)
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1 Introduction of the Section.mp4 (5.93 MB)
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2 Definition of DBSCAN.mp4 (19.48 MB)
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3 Step by step DBSCAN.mp4 (31.79 MB)
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4 Comparing DBSCAN with K-Means Clustering.mp4 (38.61 MB)
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5 Project01-Part01.mp4 (48.22 MB)
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6 Project01-Part02.mp4 (67.23 MB)
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7 Parameters of DBSCAN.mp4 (25.16 MB)
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8 Project02-DBSCAN.mp4 (77.63 MB)
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1 Introduction of the Section.mp4 (4.4 MB)
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2 Definition of GMM Clustering.mp4 (8.32 MB)
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3 Limitations of K-Means Clustering.mp4 (34.79 MB)
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4 Project01-GMM Clustering.mp4 (160.92 MB)
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5 Project02-GMM Clustering.mp4 (94.53 MB)
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6 Project03-GMM Clustering.mp4 (64.03 MB)
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7 Binomial Distribution.mp4 (26.49 MB)
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8 Expectation Maximization (EM) Algorithm.mp4 (33.75 MB)
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9 Expectation Maximization (EM) Algorithm ( Numerical Example ).mp4 (58.29 MB)
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1 Introduction of the Section.mp4 (5.22 MB)
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10 Project06-PCA.mp4 (56.61 MB)
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2 Key Concepts of PCA.mp4 (19.74 MB)
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3 Need of PCA.mp4 (32.15 MB)
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4 PCA Algorithm With Numerical Example.mp4 (68.61 MB)
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5 Project01-PCA.mp4 (80 MB)
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6 Project02-PCA.mp4 (74.23 MB)
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7 Project03-PCA.mp4 (31.87 MB)
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8 Project04-PCA.mp4 (43.65 MB)
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9 Project05-PCA.mp4 (33.02 MB)
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1 Introduction of the Section.mp4 (9.08 MB)
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10 Plotting and Visualization-Part03.mp4 (84.52 MB)
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11 Plotting and Visualization-Part04.mp4 (58.39 MB)
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12 Lists in Python.mp4 (96.04 MB)
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13 For Loops-Part01.mp4 (94.45 MB)
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14 For Loops-Part02.mp4 (100.13 MB)
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15 Strings.mp4 (73.98 MB)
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16 Print Formatting With Strings.mp4 (18.9 MB)
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17 Dictionaries-Part01.mp4 (42.67 MB)
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18 Dictionaries-Part02.mp4 (41.35 MB)
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19 Functions in Python-Part01.mp4 (40.73 MB)
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2 Arithmetic With Python.mp4 (52.59 MB)
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20 Functions in Python-Part02.mp4 (37.33 MB)
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21 Pandas-Part01.mp4 (44.31 MB)
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22 Pandas-Part02.mp4 (33.14 MB)
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23 Pandas-Part03.mp4 (65.28 MB)
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24 Pandas-Part04.mp4 (88.26 MB)
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25 Seaborn-Part01.mp4 (57.88 MB)
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26 Seaborn-Part02.mp4 (40.6 MB)
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27 Seaborn-Part03.mp4 (46.24 MB)
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28 Tuples.mp4 (42.26 MB)
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29 Classes in Python.mp4 (92.52 MB)
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3 Comparison and Logical Operations.mp4 (27.33 MB)
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4 Conditional Statements.mp4 (40.32 MB)
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5 Dealing With Numpy Arrays-Part01.mp4 (69.73 MB)
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6 Dealing With Numpy Arrays-Part02.mp4 (75.5 MB)
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7 Dealing With Numpy Arrays-Part03.mp4 (62.04 MB)
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8 Plotting and Visualization-Part01.mp4 (104.08 MB)
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9 Plotting and Visualization-Part02.mp4 (132.25 MB)
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1 Introduction of the Section.mp4 (10.81 MB)
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10 Project05-Feature Engineering.mp4 (53 MB)
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11 Project06-Feature Engineering by Window Method.mp4 (74.67 MB)
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2 Need of Data Preprocessing.mp4 (17.39 MB)
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3 Data Normalization and Min-Max Scaling.mp4 (23.05 MB)
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4 Project01-Data Normalization and Min-Max Scaling-Part01.mp4 (56.54 MB)
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5 Project01-Data Normalization and Min-Max Scaling-Part02.mp4 (73.61 MB)
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6 Data Standardization.mp4 (17.61 MB)
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7 Project02-Data Standardization.mp4 (53.85 MB)
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8 Project03-Dealing With Missing Values.mp4 (128.53 MB)
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9 Project04-Dealing With Categorical Features.mp4 (105.34 MB)
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1 Supervised Machine Learning.mp4 (6.42 MB)
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1 Introduction of the Section.mp4 (24.26 MB)
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10 Least Square Method With Numerical Example.mp4 (16 MB)
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11 Evaluation Metrics for Regression.mp4 (29.02 MB)
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12 Project01-Simple Regression-Part01.mp4 (72.05 MB)
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13 Project01-Simple Regression-Part02.mp4 (62.01 MB)
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14 Project01-Simple Regression-Part03.mp4 (140.47 MB)
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15 Project02-Multiple Regression-Part01.mp4 (76.52 MB)
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16 Project02-Multiple Regression-Part02.mp4 (82.96 MB)
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17 Project02-Multiple Regression-Part03.mp4 (109.47 MB)
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18 Project03-Another Multiple Regression.mp4 (143.18 MB)
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19 Regression by Gradient Descent.mp4 (45.12 MB)
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2 Origin of the Regression.mp4 (47.86 MB)
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20 Project04-Simple Regression With Gradient Descent.mp4 (157.26 MB)
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21 Project05-Multiple Regression With Gradient Descent.mp4 (113.12 MB)
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22 Polynomial Regression.mp4 (22.43 MB)
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23 Project06-Polynomial Regression.mp4 (109.78 MB)
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24 Cross-validation.mp4 (13.42 MB)
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25 Project07-Cross-validation.mp4 (114.99 MB)
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26 Underfitting and Overfitting ( Bias-Variance Tradeoff ).mp4 (61.48 MB)
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27 Concept of Regularization.mp4 (18.34 MB)
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28 Ridge Regression OR L2 Regularization.mp4 (40.91 MB)
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29 Lasso Regression OR L1 Regularization.mp4 (37.37 MB)
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3 Definition of Regression.mp4 (13.89 MB)
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30 Comparing Ridge and Lasso Regression.mp4 (10.5 MB)
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31 Elastic Net Regularization.mp4 (18.21 MB)
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32 Project08-Regularizations.mp4 (184.74 MB)
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33 Grid search Cross-validation.mp4 (23.5 MB)
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34 Project09-Grid Search Cross-validation.mp4 (178.02 MB)
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4 Requirement from Regression.mp4 (11.19 MB)
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5 Simple Linear Regression.mp4 (21.85 MB)
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6 Multiple Linear Regression.mp4 (24.7 MB)
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7 Target and Predicted Values.mp4 (14.05 MB)
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8 Loss Function.mp4 (12.62 MB)
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9 Regression With Least Square Method.mp4 (48.03 MB)
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1 Introduction of the Section.mp4 (10.37 MB)
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10 ROC Curves and Area Under ROC.mp4 (11.2 MB)
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11 Project02-Evaluating Logistic Regression Model.mp4 (145.44 MB)
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12 Project03-Cross-validation With Logistic Regression Model.mp4 (125.37 MB)
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13 Project04-Multiclass Classification.mp4 (145.17 MB)
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14 Project05-Classification With Challenging Dataset-Part01.mp4 (62.59 MB)
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15 Project05-Classification With Challenging Dataset-Part02.mp4 (46.19 MB)
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16 Project05-Classification With Challenging Dataset-Part03.mp4 (62.93 MB)
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17 Grid Search Cross-validation With Logistic Regression.mp4 (95.15 MB)
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2 Fundamentals of Logistic Regression.mp4 (35.58 MB)
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3 Limitations of Regression Models.mp4 (29.4 MB)
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4 Transforming Linear Regression into Logistic Regression.mp4 (27.66 MB)
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5 Project01-Getting Class Probabilities-Part01.mp4 (54.56 MB)
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6 Project01-Getting Class Probabilities-Part02.mp4 (73.19 MB)
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7 Loss Function.mp4 (20.22 MB)
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8 Model Evaluation-Confusion Matrix.mp4 (35.22 MB)
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9 Accuracy, Precision, Recall and F1-Score.mp4 (67.91 MB)
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1 Introduction of the Section.mp4 (4.42 MB)
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10 Project04-Implementing KNN.mp4 (151.84 MB)
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11 Advantages and disadvantages of KNN.mp4 (8.52 MB)
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2 Intuition Behind KNN.mp4 (15.41 MB)
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3 Steps of KNN Algorithm.mp4 (13.13 MB)
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4 Numerical Example on KNN Algorithm.mp4 (28.07 MB)
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5 Project01-KNN Algorithm-Part01.mp4 (56.65 MB)
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6 Project01-KNN Algorithm-Part02.mp4 (95.72 MB)
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7 Finding Optimal Value of K.mp4 (8.66 MB)
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8 Project02-Implementing KNN.mp4 (114.58 MB)
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9 Project03-Implementing KNN.mp4 (72.32 MB)
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Machine Learning and Deep Learning In One Semester.z01
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Machine Learning and Deep Learning In One Semester.z02
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Machine Learning and Deep Learning In One Semester.z03
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Machine Learning and Deep Learning In One Semester.z04
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Machine Learning and Deep Learning In One Semester.z05
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Machine Learning and Deep Learning In One Semester.z06
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Machine Learning and Deep Learning In One Semester.z07
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Machine Learning and Deep Learning In One Semester.z08
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Machine Learning and Deep Learning In One Semester.z09
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Machine Learning and Deep Learning In One Semester.z10
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Machine Learning and Deep Learning In One Semester.z11
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Machine Learning and Deep Learning In One Semester.z12
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Machine Learning and Deep Learning In One Semester.z13
https://rapidgator.net/file/3c2275b9119e5b5b685d9767ebed3077/Machine_Learning_and_Deep_Learning_In_One_Semester.z13
Machine Learning and Deep Learning In One Semester.z14
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Machine Learning and Deep Learning In One Semester.z15
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Machine Learning and Deep Learning In One Semester.z16
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Machine Learning and Deep Learning In One Semester.z17
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