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Step 1.mp4 58.2 MB15 Kernel SVM/111 Kernel SVM in Python.mp4 57.5 MB06 Polynomial Regression/063 Polynomial Regression in R - Step 3.mp4 57.5 MB06 Polynomial Regression/058 Polynomial Regression in Python - Step 3.mp4 57.1 MB05 Multiple Linear Regression/046 Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 56.9 MB29 -------------------- Part 7 Natural Language Processing --------------------/210 Natural Language Processing in R - Step 10.mp4 56.8 MB27 Upper Confidence Bound (UCB)/174 Upper Confidence Bound in Python - Step 3.mp4 56.3 MB12 Logistic Regression/090 Logistic Regression in Python - Step 5.mp4 55.7 MB02 -------------------- Part 1 Data Preprocessing --------------------/015 Categorical Data.mp4 55.4 MB24 Apriori/159 Apriori in R - Step 1.mp4 55.4 MB15 Kernel SVM/112 Kernel SVM in R.mp4 55.4 MB09 Random Forest Regression/076 Random Forest Regression in Python.mp4 55.3 MB05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Step 1.mp4 54.7 MB29 -------------------- Part 7 Natural Language Processing --------------------/197 Natural Language Processing in Python - Step 8.mp4 54.5 MB09 Random Forest Regression/077 Random Forest Regression in R.mp4 54.4 MB35 Linear Discriminant Analysis (LDA)/271 LDA in R.mp4 53.8 MB29 -------------------- Part 7 Natural Language Processing --------------------/201 Natural Language Processing in R - Step 1.mp4 53.7 MB28 Thompson Sampling/185 Thompson Sampling in R - Step 1.mp4 53.5 MB02 -------------------- Part 1 Data Preprocessing --------------------/017 Splitting the Dataset into the Training set and Test set.mp4 53.4 MB05 Multiple Linear Regression/051 Multiple Linear Regression in R - Backward Elimination - HOMEWORK.mp4 53.3 MB16 Naive Bayes/113 Bayes Theorem.mp4 52.9 MB31 Artificial Neural Networks/234 ANN in R - Step 1.mp4 52.3 MB21 K-Means Clustering/139 K-Means Clustering in Python.mp4 52.2 MB16 Naive Bayes/119 Naive Bayes in R.mp4 52.2 MB04 Simple Linear Regression/032 Simple Linear Regression in R - Step 4.mp4 51.5 MB24 Apriori/162 Apriori in Python - Step 1.mp4 49.7 MB39 XGBoost/285 XGBoost in R.mp4 49.6 MB13 K-Nearest Neighbors (K-NN)/100 K-NN in Python.mp4 49.3 MB07 Support Vector Regression (SVR)/067 SVR Intuition.mp4 48.9 MB29 -------------------- Part 7 Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 1.mp4 48.3 MB35 Linear Discriminant Analysis (LDA)/270 LDA in Python.mp4 47.6 MB05 Multiple Linear Regression/049 Multiple Linear Regression in R - Step 2.mp4 47.4 MB02 -------------------- Part 1 Data Preprocessing --------------------/018 Feature Scaling.mp4 46.8 MB27 Upper Confidence Bound (UCB)/173 Upper Confidence Bound in Python - Step 2.mp4 46.7 MB31 Artificial Neural Networks/237 ANN in R - Step 4 (Last step).mp4 45.9 MB38 Model Selection/278 k-Fold Cross Validation in R.mp4 45.8 MB08 Decision Tree Regression/072 Decision Tree Regression in Python.mp4 45.5 MB32 Convolutional Neural Networks/244 Step 4 - Full Connection.mp4 44.8 MB14 Support Vector Machine (SVM)/104 SVM in Python.mp4 43.7 MB32 Convolutional Neural Networks/242 Step 2 - Pooling.mp4 42.2 MB04 Simple Linear Regression/028 Simple Linear Regression in Python - Step 4.mp4 41.3 MB31 Artificial Neural Networks/228 ANN in Python - Step 5.mp4 41.3 MB27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in Python - Step 1.mp4 40.9 MB17 Decision Tree Classification/122 Decision Tree Classification in Python.mp4 40.7 MB24 Apriori/160 Apriori in R - Step 2.mp4 40.7 MB38 Model Selection/279 Grid Search in Python - Step 1.mp4 40.1 MB31 Artificial Neural Networks/236 ANN in R - Step 3.mp4 39.7 MB29 -------------------- Part 7 Natural Language Processing --------------------/209 Natural Language Processing in R - Step 9.mp4 39.5 MB31 Artificial Neural Networks/224 ANN in Python - Step 1 - Installing Theano Tensorflow and Keras.mp4 39.3 MB24 Apriori/163 Apriori in Python - Step 2.mp4 39.1 MB28 Thompson Sampling/180 Thompson Sampling Intuition.mp4 39.1 MB21 K-Means Clustering/140 K-Means Clustering in R.mp4 38.7 MB06 Polynomial Regression/060 Python Regression Template.mp4 38.6 MB34 Principal Component Analysis (PCA)/267 PCA in R - Step 3.mp4 38.5 MB38 Model Selection/281 Grid Search in R.mp4 37.3 MB24 Apriori/164 Apriori in Python - Step 3.mp4 37.0 MB06 Polynomial Regression/057 Polynomial Regression in Python - Step 2.mp4 36.8 MB24 Apriori/157 Apriori Intuition.mp4 36.7 MB15 Kernel SVM/108 The Kernel Trick.mp4 36.4 MB32 Convolutional Neural Networks/251 CNN in Python - Step 4.mp4 36.3 MB27 Upper Confidence Bound (UCB)/177 Upper Confidence Bound in R - Step 2.mp4 35.8 MB31 Artificial Neural Networks/231 ANN in Python - Step 8.mp4 35.7 MB27 Upper Confidence Bound (UCB)/176 Upper Confidence Bound in R - Step 1.mp4 35.7 MB07 Support Vector Regression (SVR)/069 SVR in R.mp4 35.4 MB36 Kernel PCA/273 Kernel PCA in Python.mp4 35.0 MB32 Convolutional Neural Networks/246 Softmax Cross-Entropy.mp4 34.8 MB29 -------------------- Part 7 Natural Language Processing --------------------/199 Natural Language Processing in Python - Step 10.mp4 34.5 MB38 Model Selection/277 k-Fold Cross Validation in Python.mp4 34.4 MB05 Multiple Linear Regression/040 Multiple Linear Regression Intuition - Step 5.mp4 34.4 MB06 Polynomial Regression/062 Polynomial Regression in R - Step 2.mp4 33.9 MB02 -------------------- Part 1 Data Preprocessing --------------------/014 Missing Data.mp4 33.7 MB34 Principal Component Analysis (PCA)/260 Principal Component Analysis (PCA) Intuition.mp4 33.7 MB39 XGBoost/284 XGBoost in Python - Step 2.mp4 33.5 MB34 Principal Component Analysis (PCA)/262 PCA in Python - Step 1.mp4 33.5 MB06 Polynomial Regression/056 Polynomial Regression in Python - Step 1.mp4 33.2 MB06 Polynomial Regression/065 R Regression Template.mp4 32.9 MB30 -------------------- Part 8 Deep Learning --------------------/213 What is Deep Learning.mp4 32.8 MB16 Naive Bayes/118 Naive Bayes in Python.mp4 32.7 MB16 Naive Bayes/114 Naive Bayes Intuition.mp4 32.6 MB32 Convolutional Neural Networks/240 Step 1 - Convolution Operation.mp4 32.5 MB34 Principal Component Analysis (PCA)/265 PCA in R - Step 1.mp4 32.1 MB32 Convolutional Neural Networks/248 CNN in Python - Step 1.mp4 32.1 MB27 Upper Confidence Bound (UCB)/169 The Multi-Armed Bandit Problem.mp4 31.7 MB21 K-Means Clustering/135 K-Means Clustering Intuition.mp4 31.4 MB31 Artificial Neural Networks/215 The Neuron.mp4 31.3 MB29 -------------------- Part 7 Natural Language Processing --------------------/193 Natural Language Processing in Python - Step 4.mp4 31.2 MB29 -------------------- Part 7 Natural Language Processing --------------------/188 Natural Language Processing Intuition.mp4 31.1 MB38 Model Selection/280 Grid Search in Python - Step 2.mp4 30.9 MB32 Convolutional Neural Networks/239 What are convolutional neural networks.mp4 30.9 MB27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound (UCB) Intuition.mp4 30.7 MB31 Artificial Neural Networks/223 Business Problem Description.mp4 30.7 MB12 Logistic Regression/084 Logistic Regression Intuition.mp4 30.6 MB34 Principal Component Analysis (PCA)/266 PCA in R - Step 2.mp4 30.4 MB02 -------------------- Part 1 Data Preprocessing --------------------/012 Importing the Dataset.mp4 30.0 MB06 Polynomial Regression/064 Polynomial Regression in R - Step 4.mp4 29.9 MB31 Artificial Neural Networks/232 ANN in Python - Step 9.mp4 29.9 MB31 Artificial Neural Networks/233 ANN in Python - Step 10.mp4 29.8 MB10 Evaluating Regression Models Performance/080 Evaluating Regression Models Performance - Homeworks Final Part.mp4 29.7 MB04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1.mp4 29.3 MB32 Convolutional Neural Networks/257 CNN in Python - Step 10.mp4 29.1 MB12 Logistic Regression/094 Logistic Regression in R - Step 3.mp4 28.8 MB29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2.mp4 28.8 MB10 Evaluating Regression Models Performance/081 Interpreting Linear Regression Coefficients.mp4 28.7 MB35 Linear Discriminant Analysis (LDA)/268 Linear Discriminant Analysis (LDA) Intuition.mp4 28.3 MB31 Artificial Neural Networks/218 How do Neural Networks learn.mp4 27.8 MB02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template.mp4 27.1 MB21 K-Means Clustering/137 K-Means Selecting The Number Of Clusters.mp4 26.9 MB18 Random Forest Classification/124 Random Forest Classification Intuition.mp4 26.9 MB34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3.mp4 26.7 MB05 Multiple Linear Regression/043 Multiple Linear Regression in Python - Step 3.mp4 26.7 MB08 Decision Tree Regression/070 Decision Tree Regression Intuition.mp4 26.6 MB25 Eclat/167 Eclat in R.mp4 26.5 MB04 Simple Linear Regression/030 Simple Linear Regression in R - Step 2.mp4 26.1 MB04 Simple Linear Regression/026 Simple Linear Regression in Python - Step 2.mp4 25.8 MB01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows).mp4 25.1 MB05 Multiple Linear Regression/044 Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 25.0 MB31 Artificial Neural Networks/217 How do Neural Networks work.mp4 24.7 MB05 Multiple Linear Regression/048 Multiple Linear Regression in R - Step 1.mp4 24.6 MB01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows).mp4 24.3 MB22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms.mp4 23.9 MB29 -------------------- Part 7 Natural Language Processing --------------------/196 Natural Language Processing in Python - Step 7.mp4 23.2 MB34 Principal Component Analysis (PCA)/263 PCA in Python - Step 2.mp4 23.1 MB05 Multiple Linear Regression/052 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 23.0 MB29 -------------------- Part 7 Natural Language Processing --------------------/202 Natural Language Processing in R - Step 2.mp4 22.7 MB17 Decision Tree Classification/120 Decision Tree Classification Intuition.mp4 22.7 MB10 Evaluating Regression Models Performance/079 Adjusted R-Squared Intuition.mp4 22.5 MB39 XGBoost/283 XGBoost in Python - Step 1.mp4 22.4 MB22 Hierarchical Clustering/148 HC in Python - Step 4.mp4 22.4 MB06 Polynomial Regression/061 Polynomial Regression in R - Step 1.mp4 22.2 MB02 -------------------- Part 1 Data Preprocessing --------------------/010 Get the dataset.mp4 22.2 MB04 Simple Linear Regression/027 Simple Linear Regression in Python - Step 3.mp4 21.5 MB19 Evaluating Classification Models Performance/131 CAP Curve.mp4 21.3 MB14 Support Vector Machine (SVM)/102 SVM Intuition.mp4 20.9 MB16 Naive Bayes/116 Naive Bayes Intuition (Extras).mp4 19.9 MB29 -------------------- Part 7 Natural Language Processing --------------------/198 Natural Language Processing in Python - Step 9.mp4 19.8 MB29 -------------------- Part 7 Natural Language Processing --------------------/194 Natural Language Processing in Python - Step 5.mp4 19.7 MB31 Artificial Neural Networks/219 Gradient Descent.mp4 19.4 MB31 Artificial Neural Networks/235 ANN in R - Step 2.mp4 19.1 MB06 Polynomial Regression/059 Polynomial Regression in Python - Step 4.mp4 18.5 MB12 Logistic Regression/091 Python Classification Template.mp4 18.4 MB12 Logistic Regression/097 R Classification Template.mp4 18.4 MB22 Hierarchical Clustering/142 Hierarchical Clustering How Dendrograms Work.mp4 18.3 MB29 -------------------- Part 7 Natural Language Processing --------------------/208 Natural Language Processing in R - Step 8.mp4 18.1 MB29 -------------------- Part 7 Natural Language Processing --------------------/203 Natural Language Processing in R - Step 3.mp4 17.7 MB12 Logistic Regression/086 Logistic Regression in Python - Step 1.mp4 17.7 MB31 Artificial Neural Networks/220 Stochastic Gradient Descent.mp4 17.6 MB32 Convolutional Neural Networks/254 CNN in Python - Step 7.mp4 17.5 MB05 Multiple Linear Regression/037 Multiple Linear Regression Intuition - Step 3.mp4 17.4 MB22 Hierarchical Clustering/141 Hierarchical Clustering Intuition.mp4 17.3 MB22 Hierarchical Clustering/147 HC in Python - Step 3.mp4 17.0 MB29 -------------------- Part 7 Natural Language Processing --------------------/206 Natural Language Processing in R - Step 6.mp4 16.9 MB12 Logistic Regression/092 Logistic Regression in R - Step 1.mp4 16.5 MB15 Kernel SVM/109 Types of Kernel Functions.mp4 16.5 MB09 Random Forest Regression/074 Random Forest Regression Intuition.mp4 16.4 MB22 Hierarchical Clustering/146 HC in Python - Step 2.mp4 16.3 MB15 Kernel SVM/107 Mapping to a higher dimension.mp4 16.1 MB21 K-Means Clustering/136 K-Means Random Initialization Trap.mp4 16.1 MB19 Evaluating Classification Models Performance/128 False Positives False Negatives.mp4 15.9 MB31 Artificial Neural Networks/230 ANN in Python - Step 7.mp4 15.6 MB12 Logistic Regression/093 Logistic Regression in R - Step 2.mp4 15.6 MB31 Artificial Neural Networks/216 The Activation Function.mp4 15.5 MB31 Artificial Neural Networks/226 ANN in Python - Step 3.mp4 15.3 MB01 Welcome to the course/002 Why Machine Learning is the Future.mp4 15.2 MB32 Convolutional Neural Networks/241 Step 1(b) - ReLU Layer.mp4 14.8 MB28 Thompson Sampling/181 Algorithm Comparison UCB vs Thompson Sampling.mp4 14.8 MB12 Logistic Regression/089 Logistic Regression in Python - Step 4.mp4 14.5 MB22 Hierarchical Clustering/151 HC in R - Step 2.mp4 14.5 MB05 Multiple Linear Regression/050 Multiple Linear Regression in R - Step 3.mp4 14.5 MB22 Hierarchical Clustering/145 HC in Python - Step 1.mp4 14.4 MB22 Hierarchical Clustering/154 HC in R - Step 5.mp4 14.3 MB02 -------------------- Part 1 Data Preprocessing --------------------/011 Importing the Libraries.mp4 14.2 MB16 Naive Bayes/115 Naive Bayes Intuition (Challenge Reveal).mp4 13.9 MB19 Evaluating Classification Models Performance/132 CAP Curve Analysis.mp4 13.6 MB05 Multiple Linear Regression/034 Dataset Business Problem Description.mp4 13.2 MB27 Upper Confidence Bound (UCB)/175 Upper Confidence Bound in Python - Step 4.mp4 13.0 MB32 Convolutional Neural Networks/252 CNN in Python - Step 5.mp4 13.0 MB32 Convolutional Neural Networks/253 CNN in Python - Step 6.mp4 12.5 MB31 Artificial Neural Networks/229 ANN in Python - Step 6.mp4 12.5 MB12 Logistic Regression/095 Logistic Regression in R - Step 4.mp4 12.3 MB04 Simple Linear Regression/021 How to get the dataset.mp4 12.3 MB05 Multiple Linear Regression/033 How to get the dataset.mp4 12.3 MB06 Polynomial Regression/055 How to get the dataset.mp4 12.3 MB07 Support Vector Regression (SVR)/066 How to get the dataset.mp4 12.3 MB08 Decision Tree Regression/071 How to get the dataset.mp4 12.3 MB09 Random Forest Regression/075 How to get the dataset.mp4 12.3 MB12 Logistic Regression/085 How to get the dataset.mp4 12.3 MB13 K-Nearest Neighbors (K-NN)/099 How to get the dataset.mp4 12.3 MB14 Support Vector Machine (SVM)/103 How to get the dataset.mp4 12.3 MB15 Kernel SVM/110 How to get the dataset.mp4 12.3 MB16 Naive Bayes/117 How to get the dataset.mp4 12.3 MB17 Decision Tree Classification/121 How to get the dataset.mp4 12.3 MB18 Random Forest Classification/125 How to get the dataset.mp4 12.3 MB21 K-Means Clustering/138 How to get the dataset.mp4 12.3 MB22 Hierarchical Clustering/144 How to get the dataset.mp4 12.3 MB24 Apriori/158 How to get the dataset.mp4 12.3 MB25 Eclat/166 How to get the dataset.mp4 12.3 MB27 Upper Confidence Bound (UCB)/171 How to get the dataset.mp4 12.3 MB28 Thompson Sampling/182 How to get the dataset.mp4 12.3 MB29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset.mp4 12.3 MB31 Artificial Neural Networks/222 How to get the dataset.mp4 12.3 MB32 Convolutional Neural Networks/247 How to get the dataset.mp4 12.3 MB34 Principal Component Analysis (PCA)/261 How to get the dataset.mp4 12.3 MB35 Linear Discriminant Analysis (LDA)/269 How to get the dataset.mp4 12.3 MB36 Kernel PCA/272 How to get the dataset.mp4 12.3 MB38 Model Selection/276 How to get the dataset.mp4 12.3 MB39 XGBoost/282 How to get the dataset.mp4 12.3 MB04 Simple Linear Regression/029 Simple Linear Regression in R - Step 1.mp4 12.1 MB04 Simple Linear Regression/031 Simple Linear Regression in R - Step 3.mp4 12.0 MB28 Thompson Sampling/184 Thompson Sampling in Python - Step 2.mp4 11.8 MB12 Logistic Regression/087 Logistic Regression in Python - Step 2.mp4 11.6 MB31 Artificial Neural Networks/221 Backpropagation.mp4 11.5 MB25 Eclat/165 Eclat Intuition.mp4 11.2 MB04 Simple Linear Regression/023 Simple Linear Regression Intuition - Step 1.mp4 11.0 MB13 K-Nearest Neighbors (K-NN)/098 K-Nearest Neighbor Intuition.mp4 11.0 MB22 Hierarchical Clustering/153 HC in R - Step 4.mp4 10.7 MB22 Hierarchical Clustering/152 HC in R - Step 3.mp4 10.4 MB22 Hierarchical Clustering/149 HC in Python - Step 5.mp4 10.4 MB05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2.mp4 10.3 MB01 Welcome to the course/001 Applications of Machine Learning.mp4 10.3 MB10 Evaluating Regression Models Performance/078 R-Squared Intuition.mp4 10.3 MB31 Artificial Neural Networks/227 ANN in Python - Step 4.mp4 10.2 MB29 -------------------- Part 7 Natural Language Processing --------------------/207 Natural Language Processing in R - Step 7.mp4 10.1 MB28 Thompson Sampling/186 Thompson Sampling in R - Step 2.mp4 10.0 MB27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4.mp4 10.0 MB06 Polynomial Regression/054 Polynomial Regression Intuition.mp4 9.9 MB32 Convolutional Neural Networks/255 CNN in Python - Step 8.mp4 9.4 MB19 Evaluating Classification Models Performance/129 Confusion Matrix.mp4 9.3 MB22 Hierarchical Clustering/150 HC in R - Step 1.mp4 9.0 MB29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6.mp4 8.7 MB29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4.mp4 8.6 MB12 Logistic Regression/088 Logistic Regression in Python - Step 3.mp4 8.4 MB32 Convolutional Neural Networks/245 Summary.mp4 8.3 MB04 Simple Linear Regression/022 Dataset Business Problem Description.mp4 8.1 MB32 Convolutional Neural Networks/249 CNN in Python - Step 2.mp4 7.6 MB15 Kernel SVM/106 Kernel SVM Intuition.mp4 6.7 MB04 Simple Linear Regression/024 Simple Linear Regression Intuition - 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01 Welcome to the course/004 Machine-Learning-A-Z-Q-A.pdf 2.4 MB05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2.mp4 2.1 MB05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1.mp4 2.1 MB
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Step 4-en.srt 17.9 kB22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms-pt.srt 17.9 kB19 Evaluating Classification Models Performance/131 CAP Curve-ja.srt 17.9 kB16 Naive Bayes/116 Naive Bayes Intuition (Extras)-ja.srt 17.8 kB34 Principal Component Analysis (PCA)/266 PCA in R - Step 2-tr.srt 17.7 kB06 Polynomial Regression/057 Polynomial Regression in Python - Step 2-it.srt 17.7 kB06 Polynomial Regression/056 Polynomial Regression in Python - Step 1-tr.srt 17.7 kB15 Kernel SVM/108 The Kernel Trick-it.srt 17.7 kB22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms-it.srt 17.7 kB22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms-es.srt 17.6 kB06 Polynomial Regression/057 Polynomial Regression in Python - Step 2-pt.srt 17.6 kB06 Polynomial Regression/064 Polynomial Regression in R - Step 4-ja.srt 17.6 kB15 Kernel SVM/108 The Kernel Trick-es.srt 17.5 kB15 Kernel SVM/108 The Kernel Trick-pt.srt 17.5 kB29 -------------------- Part 7 Natural Language Processing --------------------/199 Natural Language Processing in Python - Step 10-ja.srt 17.4 kB22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms-en.srt 17.4 kB08 Decision Tree Regression/070 Decision Tree Regression Intuition-pt.srt 17.3 kB22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms-tr.srt 17.3 kB08 Decision Tree Regression/070 Decision Tree Regression Intuition-it.srt 17.3 kB34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3-ja.srt 17.3 kB34 Principal Component Analysis (PCA)/266 PCA in R - Step 2-en.srt 17.3 kB06 Polynomial Regression/056 Polynomial Regression in Python - Step 1-en.srt 17.3 kB02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template-ja.srt 17.2 kB29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2-es.srt 17.2 kB06 Polynomial Regression/060 Python Regression Template-es.srt 17.2 kB38 Model Selection/280 Grid Search in Python - Step 2-es.srt 17.2 kB08 Decision Tree Regression/070 Decision Tree Regression Intuition-es.srt 17.1 kB06 Polynomial Regression/057 Polynomial Regression in Python - Step 2-tr.srt 17.1 kB22 Hierarchical Clustering/142 Hierarchical Clustering How Dendrograms Work-ja.srt 17.1 kB06 Polynomial Regression/060 Python Regression Template-pt.srt 17.1 kB29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2-pt.srt 17.0 kB38 Model Selection/280 Grid Search in Python - Step 2-pt.srt 17.0 kB05 Multiple Linear Regression/044 Multiple Linear Regression in Python - Backward Elimination - Preparation-ja.srt 17.0 kB06 Polynomial Regression/062 Polynomial Regression in R - Step 2-ja.srt 17.0 kB06 Polynomial Regression/057 Polynomial Regression in Python - Step 2-en.srt 16.9 kB38 Model Selection/280 Grid Search in Python - Step 2-it.srt 16.9 kB15 Kernel SVM/108 The Kernel Trick-en.srt 16.9 kB15 Kernel SVM/108 The Kernel Trick-tr.srt 16.9 kB22 Hierarchical Clustering/141 Hierarchical Clustering Intuition-ja.srt 16.9 kB06 Polynomial Regression/060 Python Regression Template-it.srt 16.8 kB29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2-it.srt 16.8 kB39 XGBoost/283 XGBoost in Python - Step 1-ja.srt 16.8 kB08 Decision Tree Regression/070 Decision Tree Regression Intuition-en.srt 16.8 kB31 Artificial Neural Networks/219 Gradient Descent-ja.srt 16.8 kB29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2-tr.srt 16.7 kB10 Evaluating Regression Models Performance/079 Adjusted R-Squared Intuition-ja.srt 16.7 kB08 Decision Tree Regression/070 Decision Tree Regression Intuition-tr.srt 16.6 kB05 Multiple Linear Regression/046 Multiple Linear Regression in Python - Backward Elimination - Homework Solution-ja.srt 16.6 kB06 Polynomial Regression/060 Python Regression Template-tr.srt 16.5 kB04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1-es.srt 16.5 kB16 Naive Bayes/118 Naive Bayes in Python-ja.srt 16.5 kB16 Naive Bayes/116 Naive Bayes Intuition (Extras)-pt.srt 16.4 kB19 Evaluating Classification Models Performance/131 CAP Curve-es.srt 16.4 kB19 Evaluating Classification Models Performance/131 CAP Curve-it.srt 16.3 kB19 Evaluating Classification Models Performance/131 CAP Curve-pt.srt 16.3 kB16 Naive Bayes/116 Naive Bayes Intuition (Extras)-es.srt 16.3 kB25 Eclat/167 Eclat in R-es.srt 16.3 kB05 Multiple Linear Regression/044 Multiple Linear Regression in Python - 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Step 1-ja.srt 16.1 kB25 Eclat/167 Eclat in R-it.srt 16.1 kB06 Polynomial Regression/062 Polynomial Regression in R - Step 2-pt.srt 16.1 kB04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1-it.srt 16.1 kB06 Polynomial Regression/062 Polynomial Regression in R - Step 2-es.srt 16.0 kB29 -------------------- Part 7 Natural Language Processing --------------------/202 Natural Language Processing in R - Step 2-ja.srt 16.0 kB19 Evaluating Classification Models Performance/131 CAP Curve-en.srt 16.0 kB14 Support Vector Machine (SVM)/102 SVM Intuition-es.srt 15.9 kB14 Support Vector Machine (SVM)/102 SVM Intuition-it.srt 15.9 kB34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3-pt.srt 15.9 kB06 Polynomial Regression/062 Polynomial Regression in R - Step 2-it.srt 15.9 kB05 Multiple Linear Regression/049 Multiple Linear Regression in R - Step 2-es.srt 15.8 kB29 -------------------- Part 7 Natural Language Processing --------------------/199 Natural Language Processing in Python - 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Step 10-pt.srt 15.6 kB25 Eclat/167 Eclat in R-en.srt 15.6 kB14 Support Vector Machine (SVM)/102 SVM Intuition-en.srt 15.5 kB14 Support Vector Machine (SVM)/102 SVM Intuition-tr.srt 15.5 kB05 Multiple Linear Regression/044 Multiple Linear Regression in Python - Backward Elimination - Preparation-tr.srt 15.5 kB25 Eclat/167 Eclat in R-tr.srt 15.5 kB04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1-tr.srt 15.4 kB01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows)-ja.srt 15.4 kB06 Polynomial Regression/062 Polynomial Regression in R - Step 2-tr.srt 15.3 kB05 Multiple Linear Regression/044 Multiple Linear Regression in Python - Backward Elimination - Preparation-en.srt 15.3 kB04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1-en.srt 15.3 kB34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3-tr.srt 15.2 kB05 Multiple Linear Regression/049 Multiple Linear Regression in R - Step 2-en.srt 15.2 kB06 Polynomial Regression/064 Polynomial Regression in R - Step 4-en.srt 15.2 kB34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3-en.srt 15.2 kB29 -------------------- Part 7 Natural Language Processing --------------------/199 Natural Language Processing in Python - Step 10-tr.srt 15.1 kB06 Polynomial Regression/064 Polynomial Regression in R - Step 4-tr.srt 15.1 kB06 Polynomial Regression/062 Polynomial Regression in R - Step 2-en.srt 15.1 kB10 Evaluating Regression Models Performance/079 Adjusted R-Squared Intuition-pt.srt 15.0 kB06 Polynomial Regression/061 Polynomial Regression in R - Step 1-es.srt 15.0 kB05 Multiple Linear Regression/049 Multiple Linear Regression in R - Step 2-tr.srt 14.9 kB06 Polynomial Regression/061 Polynomial Regression in R - Step 1-pt.srt 14.9 kB10 Evaluating Regression Models Performance/079 Adjusted R-Squared Intuition-it.srt 14.9 kB06 Polynomial Regression/061 Polynomial Regression in R - Step 1-it.srt 14.9 kB02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template-es.srt 14.9 kB31 Artificial Neural Networks/219 Gradient Descent-es.srt 14.9 kB39 XGBoost/283 XGBoost in Python - 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Step 1-en.srt 14.0 kB05 Multiple Linear Regression/046 Multiple Linear Regression in Python - Backward Elimination - Homework Solution-en.srt 14.0 kB34 Principal Component Analysis (PCA)/263 PCA in Python - Step 2-ja.srt 14.0 kB02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template-en.srt 13.9 kB10 Evaluating Regression Models Performance/081 Interpreting Linear Regression Coefficients-it.srt 13.9 kB06 Polynomial Regression/061 Polynomial Regression in R - Step 1-en.srt 13.9 kB22 Hierarchical Clustering/142 Hierarchical Clustering How Dendrograms Work-tr.srt 13.9 kB29 -------------------- Part 7 Natural Language Processing --------------------/202 Natural Language Processing in R - Step 2-pt.srt 13.9 kB05 Multiple Linear Regression/052 Multiple Linear Regression in R - Backward Elimination - Homework Solution-ja.srt 13.9 kB29 -------------------- Part 7 Natural Language Processing --------------------/202 Natural Language Processing in R - 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Step 2-tr.srt 5.3 kB31 Artificial Neural Networks/226 ANN in Python - Step 3-it.srt 5.3 kB27 Upper Confidence Bound (UCB)/175 Upper Confidence Bound in Python - Step 4-tr.srt 5.3 kB15 Kernel SVM/109 Types of Kernel Functions-es.srt 5.3 kB15 Kernel SVM/109 Types of Kernel Functions-pt.srt 5.3 kB15 Kernel SVM/106 Kernel SVM Intuition-ja.srt 5.2 kB35 Linear Discriminant Analysis (LDA)/268 Linear Discriminant Analysis (LDA) Intuition-en.srt 5.2 kB35 Linear Discriminant Analysis (LDA)/268 Linear Discriminant Analysis (LDA) Intuition-tr.srt 5.2 kB31 Artificial Neural Networks/229 ANN in Python - Step 6-ja.srt 5.2 kB28 Thompson Sampling/186 Thompson Sampling in R - Step 2-en.srt 5.2 kB04 Simple Linear Regression/021 How to get the dataset-es.srt 5.2 kB05 Multiple Linear Regression/033 How to get the dataset-es.srt 5.2 kB06 Polynomial Regression/055 How to get the dataset-es.srt 5.2 kB07 Support Vector Regression (SVR)/066 How to get the dataset-es.srt 5.2 kB08 Decision Tree Regression/071 How to get the dataset-es.srt 5.2 kB09 Random Forest Regression/075 How to get the dataset-es.srt 5.2 kB12 Logistic Regression/085 How to get the dataset-es.srt 5.2 kB13 K-Nearest Neighbors (K-NN)/099 How to get the dataset-es.srt 5.2 kB14 Support Vector Machine (SVM)/103 How to get the dataset-es.srt 5.2 kB15 Kernel SVM/110 How to get the dataset-es.srt 5.2 kB16 Naive Bayes/117 How to get the dataset-es.srt 5.2 kB17 Decision Tree Classification/121 How to get the dataset-es.srt 5.2 kB18 Random Forest Classification/125 How to get the dataset-es.srt 5.2 kB21 K-Means Clustering/138 How to get the dataset-es.srt 5.2 kB22 Hierarchical Clustering/144 How to get the dataset-es.srt 5.2 kB24 Apriori/158 How to get the dataset-es.srt 5.2 kB25 Eclat/166 How to get the dataset-es.srt 5.2 kB27 Upper Confidence Bound (UCB)/171 How to get the dataset-es.srt 5.2 kB28 Thompson Sampling/182 How to get the dataset-es.srt 5.2 kB29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset-es.srt 5.2 kB31 Artificial Neural Networks/222 How to get the dataset-es.srt 5.2 kB32 Convolutional Neural Networks/247 How to get the dataset-es.srt 5.2 kB34 Principal Component Analysis (PCA)/261 How to get the dataset-es.srt 5.2 kB35 Linear Discriminant Analysis (LDA)/269 How to get the dataset-es.srt 5.2 kB36 Kernel PCA/272 How to get the dataset-es.srt 5.2 kB38 Model Selection/276 How to get the dataset-es.srt 5.2 kB39 XGBoost/282 How to get the dataset-es.srt 5.2 kB31 Artificial Neural Networks/214 Plan of attack-ja.srt 5.2 kB15 Kernel SVM/109 Types of Kernel Functions-it.srt 5.2 kB04 Simple Linear Regression/031 Simple Linear Regression in R - Step 3-tr.srt 5.2 kB34 Principal Component Analysis (PCA)/260 Principal Component Analysis (PCA) Intuition-en.srt 5.2 kB15 Kernel SVM/109 Types of Kernel Functions-tr.srt 5.2 kB04 Simple Linear Regression/021 How to get the dataset-pt.srt 5.2 kB05 Multiple Linear Regression/033 How to get the dataset-pt.srt 5.2 kB07 Support Vector Regression (SVR)/066 How to get the dataset-pt.srt 5.2 kB08 Decision Tree Regression/071 How to get the dataset-pt.srt 5.2 kB09 Random Forest Regression/075 How to get the dataset-pt.srt 5.2 kB12 Logistic Regression/085 How to get the dataset-pt.srt 5.2 kB13 K-Nearest Neighbors (K-NN)/099 How to get the dataset-pt.srt 5.2 kB14 Support Vector Machine (SVM)/103 How to get the dataset-pt.srt 5.2 kB15 Kernel SVM/110 How to get the dataset-pt.srt 5.2 kB16 Naive Bayes/117 How to get the dataset-pt.srt 5.2 kB17 Decision Tree Classification/121 How to get the dataset-pt.srt 5.2 kB18 Random Forest Classification/125 How to get the dataset-pt.srt 5.2 kB21 K-Means Clustering/138 How to get the dataset-pt.srt 5.2 kB22 Hierarchical Clustering/144 How to get the dataset-pt.srt 5.2 kB24 Apriori/158 How to get the dataset-pt.srt 5.2 kB25 Eclat/166 How to get the dataset-pt.srt 5.2 kB27 Upper Confidence Bound (UCB)/171 How to get the dataset-pt.srt 5.2 kB28 Thompson Sampling/182 How to get the dataset-pt.srt 5.2 kB29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset-pt.srt 5.2 kB31 Artificial Neural Networks/222 How to get the dataset-pt.srt 5.2 kB32 Convolutional Neural Networks/247 How to get the dataset-pt.srt 5.2 kB34 Principal Component Analysis (PCA)/261 How to get the dataset-pt.srt 5.2 kB35 Linear Discriminant Analysis (LDA)/269 How to get the dataset-pt.srt 5.2 kB36 Kernel PCA/272 How to get the dataset-pt.srt 5.2 kB38 Model Selection/276 How to get the dataset-pt.srt 5.2 kB39 XGBoost/282 How to get the dataset-pt.srt 5.2 kB06 Polynomial Regression/055 How to get the dataset-pt.srt 5.1 kB12 Logistic Regression/087 Logistic Regression in Python - Step 2-es.srt 5.1 kB27 Upper Confidence Bound (UCB)/175 Upper Confidence Bound in Python - Step 4-en.srt 5.1 kB31 Artificial Neural Networks/226 ANN in Python - Step 3-en.srt 5.1 kB04 Simple Linear Regression/021 How to get the dataset-it.srt 5.1 kB05 Multiple Linear Regression/033 How to get the dataset-it.srt 5.1 kB06 Polynomial Regression/055 How to get the dataset-it.srt 5.1 kB07 Support Vector Regression (SVR)/066 How to get the dataset-it.srt 5.1 kB08 Decision Tree Regression/071 How to get the dataset-it.srt 5.1 kB09 Random Forest Regression/075 How to get the dataset-it.srt 5.1 kB12 Logistic Regression/085 How to get the dataset-it.srt 5.1 kB13 K-Nearest Neighbors (K-NN)/099 How to get the dataset-it.srt 5.1 kB14 Support Vector Machine (SVM)/103 How to get the dataset-it.srt 5.1 kB15 Kernel SVM/110 How to get the dataset-it.srt 5.1 kB16 Naive Bayes/117 How to get the dataset-it.srt 5.1 kB17 Decision Tree Classification/121 How to get the dataset-it.srt 5.1 kB18 Random Forest Classification/125 How to get the dataset-it.srt 5.1 kB21 K-Means Clustering/138 How to get the dataset-it.srt 5.1 kB22 Hierarchical Clustering/144 How to get the dataset-it.srt 5.1 kB24 Apriori/158 How to get the dataset-it.srt 5.1 kB25 Eclat/166 How to get the dataset-it.srt 5.1 kB27 Upper Confidence Bound (UCB)/171 How to get the dataset-it.srt 5.1 kB28 Thompson Sampling/182 How to get the dataset-it.srt 5.1 kB29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset-it.srt 5.1 kB31 Artificial Neural Networks/222 How to get the dataset-it.srt 5.1 kB32 Convolutional Neural Networks/247 How to get the dataset-it.srt 5.1 kB34 Principal Component Analysis (PCA)/261 How to get the dataset-it.srt 5.1 kB35 Linear Discriminant Analysis (LDA)/269 How to get the dataset-it.srt 5.1 kB36 Kernel PCA/272 How to get the dataset-it.srt 5.1 kB38 Model Selection/276 How to get the dataset-it.srt 5.1 kB39 XGBoost/282 How to get the dataset-it.srt 5.1 kB15 Kernel SVM/109 Types of Kernel Functions-en.srt 5.1 kB12 Logistic Regression/093 Logistic Regression in R - Step 2-ja.srt 5.0 kB12 Logistic Regression/087 Logistic Regression in Python - Step 2-pt.srt 5.0 kB12 Logistic Regression/087 Logistic Regression in Python - Step 2-it.srt 5.0 kB27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4-es.srt 5.0 kB32 Convolutional Neural Networks/255 CNN in Python - Step 8-pt.srt 5.0 kB27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4-it.srt 5.0 kB32 Convolutional Neural Networks/255 CNN in Python - Step 8-it.srt 5.0 kB32 Convolutional Neural Networks/255 CNN in Python - Step 8-es.srt 5.0 kB04 Simple Linear Regression/021 How to get the dataset-tr.srt 5.0 kB05 Multiple Linear Regression/033 How to get the dataset-tr.srt 5.0 kB06 Polynomial Regression/055 How to get the dataset-tr.srt 5.0 kB07 Support Vector Regression (SVR)/066 How to get the dataset-tr.srt 5.0 kB08 Decision Tree Regression/071 How to get the dataset-tr.srt 5.0 kB09 Random Forest Regression/075 How to get the dataset-tr.srt 5.0 kB12 Logistic Regression/085 How to get the dataset-tr.srt 5.0 kB13 K-Nearest Neighbors (K-NN)/099 How to get the dataset-tr.srt 5.0 kB14 Support Vector Machine (SVM)/103 How to get the dataset-tr.srt 5.0 kB15 Kernel SVM/110 How to get the dataset-tr.srt 5.0 kB16 Naive Bayes/117 How to get the dataset-tr.srt 5.0 kB17 Decision Tree Classification/121 How to get the dataset-tr.srt 5.0 kB18 Random Forest Classification/125 How to get the dataset-tr.srt 5.0 kB21 K-Means Clustering/138 How to get the dataset-tr.srt 5.0 kB22 Hierarchical Clustering/144 How to get the dataset-tr.srt 5.0 kB24 Apriori/158 How to get the dataset-tr.srt 5.0 kB25 Eclat/166 How to get the dataset-tr.srt 5.0 kB27 Upper Confidence Bound (UCB)/171 How to get the dataset-tr.srt 5.0 kB28 Thompson Sampling/182 How to get the dataset-tr.srt 5.0 kB29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset-tr.srt 5.0 kB31 Artificial Neural Networks/222 How to get the dataset-tr.srt 5.0 kB32 Convolutional Neural Networks/247 How to get the dataset-tr.srt 5.0 kB34 Principal Component Analysis (PCA)/261 How to get the dataset-tr.srt 5.0 kB35 Linear Discriminant Analysis (LDA)/269 How to get the dataset-tr.srt 5.0 kB36 Kernel PCA/272 How to get the dataset-tr.srt 5.0 kB38 Model Selection/276 How to get the dataset-tr.srt 5.0 kB39 XGBoost/282 How to get the dataset-tr.srt 5.0 kB29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4-es.srt 4.9 kB22 Hierarchical Clustering/152 HC in R - Step 3-it.srt 4.9 kB27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4-pt.srt 4.9 kB22 Hierarchical Clustering/152 HC in R - Step 3-es.srt 4.9 kB29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6-es.srt 4.9 kB22 Hierarchical Clustering/152 HC in R - Step 3-pt.srt 4.9 kB29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4-it.srt 4.9 kB12 Logistic Regression/087 Logistic Regression in Python - Step 2-tr.srt 4.9 kB32 Convolutional Neural Networks/249 CNN in Python - Step 2-it.srt 4.9 kB15 Kernel SVM/106 Kernel SVM Intuition-es.srt 4.9 kB04 Simple Linear Regression/021 How to get the dataset-en.srt 4.9 kB05 Multiple Linear Regression/033 How to get the dataset-en.srt 4.9 kB06 Polynomial Regression/055 How to get the dataset-en.srt 4.9 kB07 Support Vector Regression (SVR)/066 How to get the dataset-en.srt 4.9 kB08 Decision Tree Regression/071 How to get the dataset-en.srt 4.9 kB09 Random Forest Regression/075 How to get the dataset-en.srt 4.9 kB12 Logistic Regression/085 How to get the dataset-en.srt 4.9 kB13 K-Nearest Neighbors (K-NN)/099 How to get the dataset-en.srt 4.9 kB14 Support Vector Machine (SVM)/103 How to get the dataset-en.srt 4.9 kB15 Kernel SVM/110 How to get the dataset-en.srt 4.9 kB16 Naive Bayes/117 How to get the dataset-en.srt 4.9 kB17 Decision Tree Classification/121 How to get the dataset-en.srt 4.9 kB18 Random Forest Classification/125 How to get the dataset-en.srt 4.9 kB21 K-Means Clustering/138 How to get the dataset-en.srt 4.9 kB22 Hierarchical Clustering/144 How to get the dataset-en.srt 4.9 kB24 Apriori/158 How to get the dataset-en.srt 4.9 kB25 Eclat/166 How to get the dataset-en.srt 4.9 kB27 Upper Confidence Bound (UCB)/171 How to get the dataset-en.srt 4.9 kB28 Thompson Sampling/182 How to get the dataset-en.srt 4.9 kB29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset-en.srt 4.9 kB31 Artificial Neural Networks/222 How to get the dataset-en.srt 4.9 kB32 Convolutional Neural Networks/247 How to get the dataset-en.srt 4.9 kB34 Principal Component Analysis (PCA)/261 How to get the dataset-en.srt 4.9 kB35 Linear Discriminant Analysis (LDA)/269 How to get the dataset-en.srt 4.9 kB36 Kernel PCA/272 How to get the dataset-en.srt 4.9 kB38 Model Selection/276 How to get the dataset-en.srt 4.9 kB39 XGBoost/282 How to get the dataset-en.srt 4.9 kB04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2-ja.srt 4.9 kB12 Logistic Regression/087 Logistic Regression in Python - Step 2-en.srt 4.8 kB29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4-pt.srt 4.8 kB15 Kernel SVM/106 Kernel SVM Intuition-it.srt 4.8 kB05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2-ja.srt 4.8 kB15 Kernel SVM/106 Kernel SVM Intuition-pt.srt 4.8 kB32 Convolutional Neural Networks/249 CNN in Python - Step 2-es.srt 4.8 kB29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6-pt.srt 4.8 kB12 Logistic Regression/088 Logistic Regression in Python - Step 3-ja.srt 4.8 kB29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4-tr.srt 4.8 kB29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6-it.srt 4.8 kB32 Convolutional Neural Networks/249 CNN in Python - Step 2-pt.srt 4.8 kB27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4-tr.srt 4.7 kB22 Hierarchical Clustering/152 HC in R - Step 3-tr.srt 4.7 kB31 Artificial Neural Networks/227 ANN in Python - Step 4-ja.srt 4.7 kB32 Convolutional Neural Networks/255 CNN in Python - Step 8-tr.srt 4.7 kB31 Artificial Neural Networks/229 ANN in Python - Step 6-it.srt 4.7 kB32 Convolutional Neural Networks/255 CNN in Python - Step 8-en.srt 4.7 kB22 Hierarchical Clustering/152 HC in R - Step 3-en.srt 4.7 kB22 Hierarchical Clustering/154 HC in R - Step 5-ja.srt 4.7 kB19 Evaluating Classification Models Performance/133 Conclusion of Part 3 - Classification.html 4.6 kB22 Hierarchical Clustering/153 HC in R - Step 4-ja.srt 4.6 kB31 Artificial Neural Networks/229 ANN in Python - Step 6-es.srt 4.6 kB12 Logistic Regression/093 Logistic Regression in R - Step 2-es.srt 4.6 kB31 Artificial Neural Networks/229 ANN in Python - Step 6-pt.srt 4.6 kB29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4-en.srt 4.6 kB12 Logistic Regression/093 Logistic Regression in R - Step 2-pt.srt 4.6 kB32 Convolutional Neural Networks/249 CNN in Python - Step 2-en.srt 4.6 kB29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6-tr.srt 4.6 kB32 Convolutional Neural Networks/249 CNN in Python - Step 2-tr.srt 4.6 kB15 Kernel SVM/106 Kernel SVM Intuition-tr.srt 4.5 kB27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4-en.srt 4.5 kB15 Kernel SVM/106 Kernel SVM Intuition-en.srt 4.5 kB12 Logistic Regression/093 Logistic Regression in R - Step 2-it.srt 4.5 kB04 Simple Linear Regression/022 Dataset Business Problem Description-ja.srt 4.5 kB12 Logistic Regression/095 Logistic Regression in R - Step 4-ja.srt 4.5 kB29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6-en.srt 4.5 kB31 Artificial Neural Networks/229 ANN in Python - Step 6-tr.srt 4.5 kB04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2-pt.srt 4.4 kB04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2-es.srt 4.4 kB31 Artificial Neural Networks/229 ANN in Python - Step 6-en.srt 4.4 kB04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2-it.srt 4.4 kB12 Logistic Regression/093 Logistic Regression in R - Step 2-tr.srt 4.4 kB22 Hierarchical Clustering/154 HC in R - Step 5-es.srt 4.3 kB12 Logistic Regression/093 Logistic Regression in R - Step 2-en.srt 4.3 kB04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2-en.srt 4.3 kB12 Logistic Regression/088 Logistic Regression in Python - Step 3-es.srt 4.3 kB01 Welcome to the course/003 Important notes tips tricks for this course.html 4.3 kB05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2-it.srt 4.2 kB05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2-es.srt 4.2 kB04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2-tr.srt 4.2 kB31 Artificial Neural Networks/214 Plan of attack-es.srt 4.2 kB22 Hierarchical Clustering/153 HC in R - Step 4-es.srt 4.2 kB22 Hierarchical Clustering/154 HC in R - Step 5-it.srt 4.2 kB05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2-pt.srt 4.2 kB04 Simple Linear Regression/022 Dataset Business Problem Description-pt.srt 4.2 kB22 Hierarchical Clustering/154 HC in R - Step 5-pt.srt 4.2 kB10 Evaluating Regression Models Performance/082 Conclusion of Part 2 - Regression.html 4.2 kB12 Logistic Regression/088 Logistic Regression in Python - Step 3-pt.srt 4.2 kB31 Artificial Neural Networks/214 Plan of attack-tr.srt 4.1 kB31 Artificial Neural Networks/214 Plan of attack-pt.srt 4.1 kB22 Hierarchical Clustering/153 HC in R - Step 4-pt.srt 4.1 kB04 Simple Linear Regression/022 Dataset Business Problem Description-es.srt 4.1 kB12 Logistic Regression/088 Logistic Regression in Python - Step 3-it.srt 4.1 kB22 Hierarchical Clustering/153 HC in R - Step 4-it.srt 4.1 kB31 Artificial Neural Networks/214 Plan of attack-en.srt 4.1 kB31 Artificial Neural Networks/214 Plan of attack-it.srt 4.1 kB12 Logistic Regression/095 Logistic Regression in R - Step 4-es.srt 4.1 kB22 Hierarchical Clustering/154 HC in R - Step 5-tr.srt 4.1 kB12 Logistic Regression/088 Logistic Regression in Python - Step 3-tr.srt 4.0 kB04 Simple Linear Regression/022 Dataset Business Problem Description-en.srt 4.0 kB12 Logistic Regression/088 Logistic Regression in Python - Step 3-en.srt 4.0 kB12 Logistic Regression/095 Logistic Regression in R - Step 4-pt.srt 4.0 kB05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2-en.srt 4.0 kB04 Simple Linear Regression/022 Dataset Business Problem Description-it.srt 4.0 kB22 Hierarchical Clustering/153 HC in R - Step 4-tr.srt 4.0 kB22 Hierarchical Clustering/154 HC in R - Step 5-en.srt 4.0 kB31 Artificial Neural Networks/227 ANN in Python - Step 4-it.srt 4.0 kB31 Artificial Neural Networks/227 ANN in Python - Step 4-pt.srt 4.0 kB31 Artificial Neural Networks/227 ANN in Python - Step 4-es.srt 4.0 kB29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5-ja.srt 4.0 kB04 Simple Linear Regression/022 Dataset Business Problem Description-tr.srt 4.0 kB12 Logistic Regression/095 Logistic Regression in R - Step 4-it.srt 3.9 kB12 Logistic Regression/095 Logistic Regression in R - Step 4-en.srt 3.9 kB31 Artificial Neural Networks/227 ANN in Python - Step 4-tr.srt 3.9 kB05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2-tr.srt 3.9 kB12 Logistic Regression/095 Logistic Regression in R - Step 4-tr.srt 3.8 kB05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4-ja.srt 3.8 kB31 Artificial Neural Networks/227 ANN in Python - Step 4-en.srt 3.8 kB22 Hierarchical Clustering/153 HC in R - Step 4-en.srt 3.8 kB05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4-pt.srt 3.6 kB29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5-es.srt 3.6 kB05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4-es.srt 3.5 kB19 Evaluating Classification Models Performance/130 Accuracy Paradox-ja.srt 3.5 kB29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5-tr.srt 3.5 kB05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4-it.srt 3.5 kB29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5-pt.srt 3.5 kB05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4-en.srt 3.5 kB29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5-it.srt 3.4 kB05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4-tr.srt 3.4 kB29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5-en.srt 3.3 kB19 Evaluating Classification Models Performance/130 Accuracy Paradox-es.srt 3.3 kB19 Evaluating Classification Models Performance/130 Accuracy Paradox-it.srt 3.3 kB32 Convolutional Neural Networks/258 CNN in R.html 3.3 kB19 Evaluating Classification Models Performance/130 Accuracy Paradox-pt.srt 3.3 kB19 Evaluating Classification Models Performance/130 Accuracy Paradox-en.srt 3.2 kB19 Evaluating Classification Models Performance/130 Accuracy Paradox-tr.srt 3.2 kB05 Multiple Linear Regression/047 Multiple Linear Regression in Python - Automatic Backward Elimination.html 3.1 kB29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3-ja.srt 3.0 kB32 Convolutional Neural Networks/243 Step 3 - Flattening-ja.srt 3.0 kB02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-ja.srt 3.0 kB32 Convolutional Neural Networks/243 Step 3 - Flattening-es.srt 2.8 kB32 Convolutional Neural Networks/243 Step 3 - Flattening-tr.srt 2.8 kB29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3-es.srt 2.7 kB32 Convolutional Neural Networks/243 Step 3 - Flattening-pt.srt 2.7 kB29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3-it.srt 2.7 kB02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-pt.srt 2.7 kB29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3-pt.srt 2.7 kB32 Convolutional Neural Networks/243 Step 3 - Flattening-it.srt 2.7 kB02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-es.srt 2.7 kB02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-it.srt 2.6 kB29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3-tr.srt 2.6 kB29 -------------------- Part 7 Natural Language Processing --------------------/187 Welcome to Part 7 - Natural Language Processing.html 2.6 kB32 Convolutional Neural Networks/243 Step 3 - Flattening-en.srt 2.6 kB02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-tr.srt 2.6 kB29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3-en.srt 2.5 kB02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-en.srt 2.5 kB01 Welcome to the course/004 This PDF resource will help you a lot.html 2.4 kB02 -------------------- Part 1 Data Preprocessing --------------------/013 For Python learners summary of Object-oriented programming classes objects.html 2.4 kB29 -------------------- Part 7 Natural Language Processing --------------------/211 Homework Challenge.html 2.3 kB29 -------------------- Part 7 Natural Language Processing --------------------/200 Homework Challenge.html 2.3 kB01 Welcome to the course/006 Update Recommended Anaconda Version.html 2.2 kB33 -------------------- Part 9 Dimensionality Reduction --------------------/259 Welcome to Part 9 - Dimensionality Reduction.html 2.2 kB32 Convolutional Neural Networks/250 CNN in Python - Step 3-ja.srt 2.0 kB01 Welcome to the course/008 BONUS Meet your instructors.html 1.9 kB37 -------------------- Part 10 Model Selection Boosting --------------------/275 Welcome to Part 10 - Model Selection Boosting.html 1.8 kB32 Convolutional Neural Networks/250 CNN in Python - Step 3-tr.srt 1.8 kB32 Convolutional Neural Networks/250 CNN in Python - Step 3-es.srt 1.8 kB32 Convolutional Neural Networks/250 CNN in Python - Step 3-it.srt 1.8 kB32 Convolutional Neural Networks/250 CNN in Python - Step 3-pt.srt 1.7 kB30 -------------------- Part 8 Deep Learning --------------------/212 Welcome to Part 8 - Deep Learning.html 1.7 kB03 -------------------- Part 2 Regression --------------------/020 Welcome to Part 2 - Regression.html 1.7 kB11 -------------------- Part 3 Classification --------------------/083 Welcome to Part 3 - Classification.html 1.7 kB32 Convolutional Neural Networks/250 CNN in Python - Step 3-en.srt 1.7 kB26 -------------------- Part 6 Reinforcement Learning --------------------/168 Welcome to Part 6 - Reinforcement Learning.html 1.7 kB02 -------------------- Part 1 Data Preprocessing --------------------/016 WARNING - Update.html 1.6 kB05 Multiple Linear Regression/053 Multiple Linear Regression in R - Automatic Backward Elimination.html 1.6 kB05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-ja.srt 1.6 kB05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-es.srt 1.6 kB05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-ja.srt 1.6 kB20 -------------------- Part 4 Clustering --------------------/134 Welcome to Part 4 - Clustering.html 1.6 kB05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-tr.srt 1.6 kB05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-pt.srt 1.6 kB05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-en.srt 1.6 kB05 Multiple Linear Regression/039 Prerequisites What is the P-Value.html 1.5 kB05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-it.srt 1.5 kB05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-pt.srt 1.5 kB05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-tr.srt 1.5 kB05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-es.srt 1.5 kB05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-it.srt 1.4 kB05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-en.srt 1.4 kB22 Hierarchical Clustering/155 Conclusion of Part 4 - Clustering.html 1.4 kB23 -------------------- Part 5 Association Rule Learning --------------------/156 Welcome to Part 5 - Association Rule Learning.html 1.3 kB
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