[FreeCourseSite.com] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science

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[FreeCourseSite.com] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science

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收录时间：2021-02-05
最近下载：2025-10-07

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文件列表

  • 36 Kernel PCA/264 Kernel PCA in R.mp4 59.3 MB
  • 12 Logistic Regression/089 Logistic Regression in R - Step 5.mp4 54.2 MB
  • 35 Linear Discriminant Analysis (LDA)/261 LDA in R.mp4 53.8 MB
  • 17 Decision Tree Classification/116 Decision Tree Classification in R.mp4 53.7 MB
  • 18 Random Forest Classification/120 Random Forest Classification in R.mp4 51.8 MB
  • 31 Artificial Neural Networks/217 ANN in Python - Step 2.mp4 50.4 MB
  • 39 XGBoost/275 XGBoost in R.mp4 49.6 MB
  • 27 Upper Confidence Bound (UCB)/171 Upper Confidence Bound in R - Step 3.mp4 49.5 MB
  • 18 Random Forest Classification/119 Random Forest Classification in Python.mp4 49.4 MB
  • 32 Convolutional Neural Networks/248 CNN in Python - Step 9.mp4 49.1 MB
  • 07 Support Vector Regression (SVR)/061 SVR in Python.mp4 48.4 MB
  • 35 Linear Discriminant Analysis (LDA)/260 LDA in Python.mp4 47.6 MB
  • 08 Decision Tree Regression/066 Decision Tree Regression in R.mp4 46.5 MB
  • 16 Naive Bayes/106 Bayes Theorem.mp4 46.0 MB
  • 24 Apriori/154 Apriori in R - Step 3.mp4 46.0 MB
  • 38 Model Selection/268 k-Fold Cross Validation in R.mp4 45.8 MB
  • 06 Polynomial Regression/057 Polynomial Regression in R - Step 3.mp4 45.4 MB
  • 28 Thompson Sampling/176 Thompson Sampling in Python - Step 1.mp4 45.2 MB
  • 06 Polynomial Regression/052 Polynomial Regression in Python - Step 3.mp4 45.1 MB
  • 24 Apriori/152 Apriori in R - Step 1.mp4 45.0 MB
  • 32 Convolutional Neural Networks/236 Step 4 - Full Connection.mp4 44.8 MB
  • 12 Logistic Regression/083 Logistic Regression in Python - Step 5.mp4 44.6 MB
  • 15 Kernel SVM/104 Kernel SVM in Python.mp4 43.6 MB
  • 13 K-Nearest Neighbors (K-NN)/094 K-NN in R.mp4 43.4 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/202 Natural Language Processing in R - Step 10.mp4 43.2 MB
  • 27 Upper Confidence Bound (UCB)/167 Upper Confidence Bound in Python - Step 3.mp4 43.1 MB
  • 28 Thompson Sampling/178 Thompson Sampling in R - Step 1.mp4 42.9 MB
  • 02 -------------------- Part 1_ Data Preprocessing --------------------/013 Categorical Data.mp4 42.8 MB
  • 15 Kernel SVM/105 Kernel SVM in R.mp4 42.4 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/193 Natural Language Processing in R - Step 1.mp4 42.3 MB
  • 09 Random Forest Regression/070 Random Forest Regression in R.mp4 42.3 MB
  • 32 Convolutional Neural Networks/234 Step 2 - Pooling.mp4 42.2 MB
  • 21 K-Means Clustering/132 K-Means Clustering in Python.mp4 41.7 MB
  • 05 Multiple Linear Regression/046 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 41.7 MB
  • 05 Multiple Linear Regression/037 Multiple Linear Regression in Python - Step 1.mp4 41.5 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/189 Natural Language Processing in Python - Step 8.mp4 41.4 MB
  • 09 Random Forest Regression/069 Random Forest Regression in Python.mp4 41.4 MB
  • 02 -------------------- Part 1_ Data Preprocessing --------------------/014 Splitting the Dataset into the Training set and Test set.mp4 40.9 MB
  • 31 Artificial Neural Networks/226 ANN in R - Step 1.mp4 40.4 MB
  • 38 Model Selection/269 Grid Search in Python - Step 1.mp4 40.1 MB
  • 24 Apriori/155 Apriori in Python - Step 1.mp4 39.8 MB
  • 04 Simple Linear Regression/029 Simple Linear Regression in R - Step 4.mp4 39.2 MB
  • 16 Naive Bayes/112 Naive Bayes in R.mp4 39.1 MB
  • 28 Thompson Sampling/173 Thompson Sampling Intuition.mp4 39.1 MB
  • 34 Principal Component Analysis (PCA)/258 PCA in R - Step 3.mp4 38.5 MB
  • 38 Model Selection/271 Grid Search in R.mp4 37.3 MB
  • 27 Upper Confidence Bound (UCB)/166 Upper Confidence Bound in Python - Step 2.mp4 37.2 MB
  • 13 K-Nearest Neighbors (K-NN)/093 K-NN in Python.mp4 36.9 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/182 Natural Language Processing in Python - Step 1.mp4 36.9 MB
  • 24 Apriori/150 Apriori Intuition.mp4 36.7 MB
  • 02 -------------------- Part 1_ Data Preprocessing --------------------/015 Feature Scaling.mp4 36.3 MB
  • 08 Decision Tree Regression/065 Decision Tree Regression in Python.mp4 35.2 MB
  • 31 Artificial Neural Networks/229 ANN in R - Step 4 (Last step).mp4 35.1 MB
  • 36 Kernel PCA/263 Kernel PCA in Python.mp4 35.0 MB
  • 32 Convolutional Neural Networks/238 Softmax & Cross-Entropy.mp4 34.8 MB
  • 38 Model Selection/267 k-Fold Cross Validation in Python.mp4 34.4 MB
  • 05 Multiple Linear Regression/040 Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 34.4 MB
  • 05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp4 34.2 MB
  • 14 Support Vector Machine (SVM)/098 SVM in R.mp4 33.8 MB
  • 02 -------------------- Part 1_ Data Preprocessing --------------------/012 Missing Data.mp4 33.7 MB
  • 39 XGBoost/274 XGBoost in Python - Step 2.mp4 33.5 MB
  • 34 Principal Component Analysis (PCA)/253 PCA in Python - Step 1.mp4 33.5 MB
  • 27 Upper Confidence Bound (UCB)/165 Upper Confidence Bound in Python - Step 1.mp4 33.1 MB
  • 30 -------------------- Part 8_ Deep Learning --------------------/205 What is Deep Learning_.mp4 32.8 MB
  • 14 Support Vector Machine (SVM)/097 SVM in Python.mp4 32.7 MB
  • 32 Convolutional Neural Networks/232 Step 1 - Convolution Operation.mp4 32.5 MB
  • 04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 4.mp4 32.3 MB
  • 34 Principal Component Analysis (PCA)/256 PCA in R - Step 1.mp4 32.1 MB
  • 24 Apriori/153 Apriori in R - Step 2.mp4 32.0 MB
  • 27 Upper Confidence Bound (UCB)/162 The Multi-Armed Bandit Problem.mp4 31.7 MB
  • 31 Artificial Neural Networks/207 The Neuron.mp4 31.3 MB
  • 17 Decision Tree Classification/115 Decision Tree Classification in Python.mp4 31.2 MB
  • 31 Artificial Neural Networks/220 ANN in Python - Step 5.mp4 31.0 MB
  • 24 Apriori/156 Apriori in Python - Step 2.mp4 31.0 MB
  • 38 Model Selection/270 Grid Search in Python - Step 2.mp4 30.9 MB
  • 32 Convolutional Neural Networks/231 What are convolutional neural networks_.mp4 30.9 MB
  • 27 Upper Confidence Bound (UCB)/163 Upper Confidence Bound (UCB) Intuition.mp4 30.7 MB
  • 31 Artificial Neural Networks/216 ANN in Python - Step 1 - Installing Theano_ Tensorflow and Keras.mp4 30.7 MB
  • 15 Kernel SVM/101 The Kernel Trick.mp4 30.7 MB
  • 12 Logistic Regression/077 Logistic Regression Intuition.mp4 30.6 MB
  • 34 Principal Component Analysis (PCA)/257 PCA in R - Step 2.mp4 30.4 MB
  • 27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound in R - Step 2.mp4 30.4 MB
  • 21 K-Means Clustering/133 K-Means Clustering in R.mp4 30.4 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/201 Natural Language Processing in R - Step 9.mp4 30.4 MB
  • 31 Artificial Neural Networks/228 ANN in R - Step 3.mp4 30.3 MB
  • 05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 5.mp4 30.2 MB
  • 27 Upper Confidence Bound (UCB)/169 Upper Confidence Bound in R - Step 1.mp4 29.4 MB
  • 16 Naive Bayes/107 Naive Bayes Intuition.mp4 29.1 MB
  • 06 Polynomial Regression/054 Python Regression Template.mp4 28.8 MB
  • 32 Convolutional Neural Networks/243 CNN in Python - Step 4.mp4 28.5 MB
  • 05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 28.5 MB
  • 06 Polynomial Regression/051 Polynomial Regression in Python - Step 2.mp4 28.4 MB
  • 24 Apriori/157 Apriori in Python - Step 3.mp4 28.3 MB
  • 21 K-Means Clustering/128 K-Means Clustering Intuition.mp4 28.2 MB
  • 31 Artificial Neural Networks/210 How do Neural Networks learn_.mp4 27.8 MB
  • 05 Multiple Linear Regression/044 Multiple Linear Regression in R - Step 2.mp4 27.2 MB
  • 07 Support Vector Regression (SVR)/062 SVR in R.mp4 27.1 MB
  • 34 Principal Component Analysis (PCA)/255 PCA in Python - Step 3.mp4 26.7 MB
  • 06 Polynomial Regression/059 R Regression Template.mp4 26.6 MB
  • 32 Convolutional Neural Networks/240 CNN in Python - Step 1.mp4 26.1 MB
  • 06 Polynomial Regression/050 Polynomial Regression in Python - Step 1.mp4 26.1 MB
  • 10 Evaluating Regression Models Performance/074 Interpreting Linear Regression Coefficients.mp4 25.4 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 10.mp4 25.3 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/185 Natural Language Processing in Python - Step 4.mp4 25.2 MB
  • 06 Polynomial Regression/056 Polynomial Regression in R - Step 2.mp4 25.0 MB
  • 31 Artificial Neural Networks/209 How do Neural Networks work_.mp4 24.7 MB
  • 16 Naive Bayes/111 Naive Bayes in Python.mp4 24.5 MB
  • 02 -------------------- Part 1_ Data Preprocessing --------------------/010 Importing the Dataset.mp4 24.4 MB
  • 21 K-Means Clustering/130 K-Means Selecting The Number Of Clusters.mp4 24.3 MB
  • 22 Hierarchical Clustering/136 Hierarchical Clustering Using Dendrograms.mp4 23.9 MB
  • 08 Decision Tree Regression/063 Decision Tree Regression Intuition.mp4 23.8 MB
  • 06 Polynomial Regression/058 Polynomial Regression in R - Step 4.mp4 23.4 MB
  • 34 Principal Component Analysis (PCA)/254 PCA in Python - Step 2.mp4 23.1 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/183 Natural Language Processing in Python - Step 2.mp4 23.0 MB
  • 10 Evaluating Regression Models Performance/073 Evaluating Regression Models Performance - Homework's Final Part.mp4 23.0 MB
  • 04 Simple Linear Regression/022 Simple Linear Regression in Python - Step 1.mp4 22.8 MB
  • 39 XGBoost/273 XGBoost in Python - Step 1.mp4 22.4 MB
  • 02 -------------------- Part 1_ Data Preprocessing --------------------/008 Get the dataset.mp4 22.2 MB
  • 25 Eclat/160 Eclat in R.mp4 21.7 MB
  • 32 Convolutional Neural Networks/249 CNN in Python - Step 10.mp4 21.6 MB
  • 02 -------------------- Part 1_ Data Preprocessing --------------------/016 And here is our Data Preprocessing Template!.mp4 20.6 MB
  • 01 Welcome to the course!/005 Installing Python and Anaconda (MAC & Windows).mp4 20.5 MB
  • 18 Random Forest Classification/117 Random Forest Classification Intuition.mp4 20.4 MB
  • 10 Evaluating Regression Models Performance/072 Adjusted R-Squared Intuition.mp4 20.2 MB
  • 16 Naive Bayes/109 Naive Bayes Intuition (Extras).mp4 19.9 MB
  • 17 Decision Tree Classification/113 Decision Tree Classification Intuition.mp4 19.7 MB
  • 04 Simple Linear Regression/023 Simple Linear Regression in Python - Step 2.mp4 19.7 MB
  • 19 Evaluating Classification Models Performance/124 CAP Curve.mp4 19.6 MB
  • 31 Artificial Neural Networks/211 Gradient Descent.mp4 19.4 MB
  • 31 Artificial Neural Networks/223 ANN in Python - Step 8.mp4 19.0 MB
  • 14 Support Vector Machine (SVM)/095 SVM Intuition.mp4 18.9 MB
  • 05 Multiple Linear Regression/043 Multiple Linear Regression in R - Step 1.mp4 18.8 MB
  • 06 Polynomial Regression/055 Polynomial Regression in R - Step 1.mp4 18.6 MB
  • 01 Welcome to the course!/003 Installing R and R Studio (MAC & Windows).mp4 18.4 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/194 Natural Language Processing in R - Step 2.mp4 18.3 MB
  • 22 Hierarchical Clustering/135 Hierarchical Clustering How Dendrograms Work.mp4 18.3 MB
  • 05 Multiple Linear Regression/047 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 18.1 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/188 Natural Language Processing in Python - Step 7.mp4 17.9 MB
  • 31 Artificial Neural Networks/225 ANN in Python - Step 10.mp4 17.9 MB
  • 31 Artificial Neural Networks/224 ANN in Python - Step 9.mp4 17.7 MB
  • 31 Artificial Neural Networks/212 Stochastic Gradient Descent.mp4 17.6 MB
  • 22 Hierarchical Clustering/134 Hierarchical Clustering Intuition.mp4 17.3 MB
  • 31 Artificial Neural Networks/215 Business Problem Description.mp4 17.2 MB
  • 04 Simple Linear Regression/024 Simple Linear Regression in Python - Step 3.mp4 16.4 MB
  • 21 K-Means Clustering/129 K-Means Random Initialization Trap.mp4 16.1 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/186 Natural Language Processing in Python - Step 5.mp4 15.6 MB
  • 31 Artificial Neural Networks/208 The Activation Function.mp4 15.5 MB
  • 12 Logistic Regression/087 Logistic Regression in R - Step 3.mp4 15.3 MB
  • 04 Simple Linear Regression/027 Simple Linear Regression in R - Step 2.mp4 15.0 MB
  • 05 Multiple Linear Regression/039 Multiple Linear Regression in Python - Step 3.mp4 15.0 MB
  • 05 Multiple Linear Regression/034 Multiple Linear Regression Intuition - Step 3.mp4 15.0 MB
  • 31 Artificial Neural Networks/227 ANN in R - Step 2.mp4 14.9 MB
  • 32 Convolutional Neural Networks/233 Step 1(b) - ReLU Layer.mp4 14.8 MB
  • 28 Thompson Sampling/174 Algorithm Comparison_ UCB vs Thompson Sampling.mp4 14.8 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 9.mp4 14.7 MB
  • 09 Random Forest Regression/067 Random Forest Regression Intuition.mp4 14.5 MB
  • 15 Kernel SVM/100 Mapping to a higher dimension.mp4 14.4 MB
  • 19 Evaluating Classification Models Performance/121 False Positives & False Negatives.mp4 14.3 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/195 Natural Language Processing in R - Step 3.mp4 14.2 MB
  • 06 Polynomial Regression/053 Polynomial Regression in Python - Step 4.mp4 14.2 MB
  • 16 Naive Bayes/108 Naive Bayes Intuition (Challenge Reveal).mp4 13.9 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/200 Natural Language Processing in R - Step 8.mp4 13.9 MB
  • 32 Convolutional Neural Networks/246 CNN in Python - Step 7.mp4 13.6 MB
  • 12 Logistic Regression/079 Logistic Regression in Python - Step 1.mp4 13.6 MB
  • 01 Welcome to the course!/002 Why Machine Learning is the Future.mp4 13.4 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/198 Natural Language Processing in R - Step 6.mp4 13.3 MB
  • 22 Hierarchical Clustering/139 HC in Python - Step 2.mp4 13.3 MB
  • 12 Logistic Regression/085 Logistic Regression in R - Step 1.mp4 13.2 MB
  • 12 Logistic Regression/090 R Classification Template.mp4 13.1 MB
  • 15 Kernel SVM/102 Types of Kernel Functions.mp4 12.9 MB
  • 22 Hierarchical Clustering/140 HC in Python - Step 3.mp4 12.9 MB
  • 12 Logistic Regression/084 Python Classification Template.mp4 12.7 MB
  • 22 Hierarchical Clustering/141 HC in Python - Step 4.mp4 12.6 MB
  • 04 Simple Linear Regression/018 How to get the dataset.mp4 12.3 MB
  • 05 Multiple Linear Regression/030 How to get the dataset.mp4 12.3 MB
  • 06 Polynomial Regression/049 How to get the dataset.mp4 12.3 MB
  • 07 Support Vector Regression (SVR)/060 How to get the dataset.mp4 12.3 MB
  • 08 Decision Tree Regression/064 How to get the dataset.mp4 12.3 MB
  • 09 Random Forest Regression/068 How to get the dataset.mp4 12.3 MB
  • 12 Logistic Regression/078 How to get the dataset.mp4 12.3 MB
  • 13 K-Nearest Neighbors (K-NN)/092 How to get the dataset.mp4 12.3 MB
  • 14 Support Vector Machine (SVM)/096 How to get the dataset.mp4 12.3 MB
  • 15 Kernel SVM/103 How to get the dataset.mp4 12.3 MB
  • 16 Naive Bayes/110 How to get the dataset.mp4 12.3 MB
  • 17 Decision Tree Classification/114 How to get the dataset.mp4 12.3 MB
  • 18 Random Forest Classification/118 How to get the dataset.mp4 12.3 MB
  • 21 K-Means Clustering/131 How to get the dataset.mp4 12.3 MB
  • 22 Hierarchical Clustering/137 How to get the dataset.mp4 12.3 MB
  • 24 Apriori/151 How to get the dataset.mp4 12.3 MB
  • 25 Eclat/159 How to get the dataset.mp4 12.3 MB
  • 27 Upper Confidence Bound (UCB)/164 How to get the dataset.mp4 12.3 MB
  • 28 Thompson Sampling/175 How to get the dataset.mp4 12.3 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/181 How to get the dataset.mp4 12.3 MB
  • 31 Artificial Neural Networks/214 How to get the dataset.mp4 12.3 MB
  • 32 Convolutional Neural Networks/239 How to get the dataset.mp4 12.3 MB
  • 34 Principal Component Analysis (PCA)/252 How to get the dataset.mp4 12.3 MB
  • 35 Linear Discriminant Analysis (LDA)/259 How to get the dataset.mp4 12.3 MB
  • 36 Kernel PCA/262 How to get the dataset.mp4 12.3 MB
  • 38 Model Selection/266 How to get the dataset.mp4 12.3 MB
  • 39 XGBoost/272 How to get the dataset.mp4 12.3 MB
  • 19 Evaluating Classification Models Performance/125 CAP Curve Analysis.mp4 12.1 MB
  • 22 Hierarchical Clustering/144 HC in R - Step 2.mp4 11.7 MB
  • 02 -------------------- Part 1_ Data Preprocessing --------------------/009 Importing the Libraries.mp4 11.6 MB
  • 31 Artificial Neural Networks/213 Backpropagation.mp4 11.5 MB
  • 22 Hierarchical Clustering/138 HC in Python - Step 1.mp4 11.2 MB
  • 25 Eclat/158 Eclat Intuition.mp4 11.2 MB
  • 05 Multiple Linear Regression/045 Multiple Linear Regression in R - Step 3.mp4 10.9 MB
  • 12 Logistic Regression/082 Logistic Regression in Python - Step 4.mp4 10.9 MB
  • 05 Multiple Linear Regression/031 Dataset + Business Problem Description.mp4 10.5 MB
  • 32 Convolutional Neural Networks/244 CNN in Python - Step 5.mp4 10.4 MB
  • 32 Convolutional Neural Networks/245 CNN in Python - Step 6.mp4 10.2 MB
  • 04 Simple Linear Regression/026 Simple Linear Regression in R - Step 1.mp4 10.0 MB
  • 04 Simple Linear Regression/020 Simple Linear Regression Intuition - Step 1.mp4 9.9 MB
  • 06 Polynomial Regression/048 Polynomial Regression Intuition.mp4 9.9 MB
  • 13 K-Nearest Neighbors (K-NN)/091 K-Nearest Neighbor Intuition.mp4 9.7 MB
  • 27 Upper Confidence Bound (UCB)/168 Upper Confidence Bound in Python - Step 4.mp4 9.6 MB
  • 31 Artificial Neural Networks/222 ANN in Python - Step 7.mp4 9.4 MB
  • 10 Evaluating Regression Models Performance/071 R-Squared Intuition.mp4 9.3 MB
  • 04 Simple Linear Regression/028 Simple Linear Regression in R - Step 3.mp4 9.1 MB
  • 28 Thompson Sampling/177 Thompson Sampling in Python - Step 2.mp4 8.8 MB
  • 22 Hierarchical Clustering/142 HC in Python - Step 5.mp4 8.8 MB
  • 31 Artificial Neural Networks/218 ANN in Python - Step 3.mp4 8.8 MB
  • 12 Logistic Regression/080 Logistic Regression in Python - Step 2.mp4 8.6 MB
  • 19 Evaluating Classification Models Performance/122 Confusion Matrix.mp4 8.6 MB
  • 01 Welcome to the course!/001 Applications of Machine Learning.mp4 8.4 MB
  • 32 Convolutional Neural Networks/237 Summary.mp4 8.3 MB
  • 12 Logistic Regression/086 Logistic Regression in R - Step 2.mp4 8.2 MB
  • 22 Hierarchical Clustering/145 HC in R - Step 3.mp4 8.2 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/199 Natural Language Processing in R - Step 7.mp4 7.9 MB
  • 28 Thompson Sampling/179 Thompson Sampling in R - Step 2.mp4 7.8 MB
  • 22 Hierarchical Clustering/146 HC in R - Step 4.mp4 7.8 MB
  • 27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in R - Step 4.mp4 7.8 MB
  • 22 Hierarchical Clustering/143 HC in R - Step 1.mp4 7.7 MB
  • 05 Multiple Linear Regression/038 Multiple Linear Regression in Python - Step 2.mp4 7.6 MB
  • 31 Artificial Neural Networks/221 ANN in Python - Step 6.mp4 7.4 MB
  • 12 Logistic Regression/088 Logistic Regression in R - Step 4.mp4 7.2 MB
  • 22 Hierarchical Clustering/147 HC in R - Step 5.mp4 7.2 MB
  • 32 Convolutional Neural Networks/247 CNN in Python - Step 8.mp4 7.1 MB
  • 04 Simple Linear Regression/019 Dataset + Business Problem Description.mp4 6.9 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/196 Natural Language Processing in R - Step 4.mp4 6.8 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/187 Natural Language Processing in Python - Step 6.mp4 6.8 MB
  • 12 Logistic Regression/081 Logistic Regression in Python - Step 3.mp4 6.3 MB
  • 32 Convolutional Neural Networks/230 Plan of attack.mp4 6.2 MB
  • 31 Artificial Neural Networks/219 ANN in Python - Step 4.mp4 6.2 MB
  • 32 Convolutional Neural Networks/241 CNN in Python - Step 2.mp4 6.1 MB
  • 15 Kernel SVM/099 Kernel SVM Intuition.mp4 6.1 MB
  • 04 Simple Linear Regression/021 Simple Linear Regression Intuition - Step 2.mp4 5.6 MB
  • 31 Artificial Neural Networks/206 Plan of attack.mp4 5.0 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/197 Natural Language Processing in R - Step 5.mp4 4.8 MB
  • 05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 4.mp4 4.7 MB
  • 19 Evaluating Classification Models Performance/123 Accuracy Paradox.mp4 4.0 MB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/184 Natural Language Processing in Python - Step 3.mp4 3.5 MB
  • 32 Convolutional Neural Networks/235 Step 3 - Flattening.mp4 3.4 MB
  • 02 -------------------- Part 1_ Data Preprocessing --------------------/007 Welcome to Part 1 - Data Preprocessing.mp4 3.1 MB
  • 32 Convolutional Neural Networks/242 CNN in Python - Step 3.mp4 2.3 MB
  • 05 Multiple Linear Regression/032 Multiple Linear Regression Intuition - Step 1.mp4 1.9 MB
  • 05 Multiple Linear Regression/033 Multiple Linear Regression Intuition - Step 2.mp4 1.9 MB
  • 25 Eclat/160 Eclat.zip 49.7 kB
  • 16 Naive Bayes/106 Bayes Theorem-subtitle-en.srt 35.3 kB
  • 18 Random Forest Classification/120 Random Forest Classification in R-subtitle-en.srt 33.2 kB
  • 08 Decision Tree Regression/066 Decision Tree Regression in R-subtitle-en.srt 32.9 kB
  • 36 Kernel PCA/264 Kernel PCA in R-subtitle-en.srt 32.8 kB
  • 06 Polynomial Regression/052 Polynomial Regression in Python - Step 3-subtitle-en.srt 32.2 kB
  • 24 Apriori/154 Apriori in R - Step 3-subtitle-en.srt 31.9 kB
  • 24 Apriori/152 Apriori in R - Step 1-subtitle-en.srt 31.8 kB
  • 07 Support Vector Regression (SVR)/061 SVR in Python-subtitle-en.srt 31.6 kB
  • 35 Linear Discriminant Analysis (LDA)/261 LDA in R-subtitle-en.srt 31.6 kB
  • 06 Polynomial Regression/057 Polynomial Regression in R - Step 3-subtitle-en.srt 31.6 kB
  • 18 Random Forest Classification/119 Random Forest Classification in Python-subtitle-en.srt 31.5 kB
  • 32 Convolutional Neural Networks/248 CNN in Python - Step 9-subtitle-en.srt 31.3 kB
  • 31 Artificial Neural Networks/217 ANN in Python - Step 2-subtitle-en.srt 30.9 kB
  • 28 Thompson Sampling/176 Thompson Sampling in Python - Step 1-subtitle-en.srt 30.7 kB
  • 12 Logistic Regression/083 Logistic Regression in Python - Step 5-subtitle-en.srt 30.4 kB
  • 32 Convolutional Neural Networks/236 Step 4 - Full Connection-subtitle-en.srt 30.3 kB
  • 17 Decision Tree Classification/116 Decision Tree Classification in R-subtitle-en.srt 29.8 kB
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  • 09 Random Forest Regression/070 Random Forest Regression in R-subtitle-en.srt 28.8 kB
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  • 24 Apriori/155 Apriori in Python - Step 1-subtitle-en.srt 28.6 kB
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  • 29 -------------------- Part 7_ Natural Language Processing --------------------/202 Natural Language Processing in R - Step 10-subtitle-en.srt 28.0 kB
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  • 27 Upper Confidence Bound (UCB)/166 Upper Confidence Bound in Python - Step 2-subtitle-en.srt 26.9 kB
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  • 29 -------------------- Part 7_ Natural Language Processing --------------------/193 Natural Language Processing in R - Step 1-subtitle-en.srt 25.5 kB
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  • 05 Multiple Linear Regression/037 Multiple Linear Regression in Python - Step 1-subtitle-en.srt 25.0 kB
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  • 04 Simple Linear Regression/029 Simple Linear Regression in R - Step 4-subtitle-en.srt 24.5 kB
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  • 02 -------------------- Part 1_ Data Preprocessing --------------------/012 Missing Data-subtitle-en.srt 24.2 kB
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  • 34 Principal Component Analysis (PCA)/258 PCA in R - Step 3-subtitle-en.srt 21.0 kB
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  • 05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !-subtitle-en.srt 20.2 kB
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  • 30 -------------------- Part 8_ Deep Learning --------------------/205 What is Deep Learning_-subtitle-en.srt 19.3 kB
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  • 06 Polynomial Regression/056 Polynomial Regression in R - Step 2-subtitle-en.srt 15.6 kB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 10-subtitle-en.srt 15.3 kB
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  • 02 -------------------- Part 1_ Data Preprocessing --------------------/016 And here is our Data Preprocessing Template!-subtitle-en.srt 14.5 kB
  • 39 XGBoost/273 XGBoost in Python - Step 1-subtitle-en.srt 14.5 kB
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  • 29 -------------------- Part 7_ Natural Language Processing --------------------/194 Natural Language Processing in R - Step 2-subtitle-en.srt 13.7 kB
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  • 21 K-Means Clustering/129 K-Means Random Initialization Trap-subtitle-en.srt 13.3 kB
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  • 04 Simple Linear Regression/023 Simple Linear Regression in Python - Step 2-subtitle-en.srt 12.6 kB
  • 01 Welcome to the course!/005 Installing Python and Anaconda (MAC & Windows)-subtitle-en.srt 12.6 kB
  • 34 Principal Component Analysis (PCA)/254 PCA in Python - Step 2-subtitle-en.srt 12.6 kB
  • 05 Multiple Linear Regression/047 Multiple Linear Regression in R - Backward Elimination - Homework Solution-subtitle-en.srt 12.1 kB
  • 05 Multiple Linear Regression/043 Multiple Linear Regression in R - Step 1-subtitle-en.srt 12.1 kB
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  • 19 Evaluating Classification Models Performance/121 False Positives & False Negatives-subtitle-en.srt 11.6 kB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/186 Natural Language Processing in Python - Step 5-subtitle-en.srt 11.4 kB
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  • 29 -------------------- Part 7_ Natural Language Processing --------------------/188 Natural Language Processing in Python - Step 7-subtitle-en.srt 10.4 kB
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  • 04 Simple Linear Regression/024 Simple Linear Regression in Python - Step 3-subtitle-en.srt 10.1 kB
  • 01 Welcome to the course!/002 Why Machine Learning is the Future-subtitle-en.srt 9.8 kB
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  • 22 Hierarchical Clustering/139 HC in Python - Step 2-subtitle-en.srt 9.7 kB
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  • 19 Evaluating Classification Models Performance/125 CAP Curve Analysis-subtitle-en.srt 9.5 kB
  • 01 Welcome to the course!/003 Installing R and R Studio (MAC & Windows)-subtitle-en.srt 9.4 kB
  • 12 Logistic Regression/085 Logistic Regression in R - Step 1-subtitle-en.srt 9.1 kB
  • 04 Simple Linear Regression/027 Simple Linear Regression in R - Step 2-subtitle-en.srt 9.1 kB
  • 12 Logistic Regression/079 Logistic Regression in Python - Step 1-subtitle-en.srt 9.0 kB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/198 Natural Language Processing in R - Step 6-subtitle-en.srt 8.9 kB
  • 06 Polynomial Regression/053 Polynomial Regression in Python - Step 4-subtitle-en.srt 8.9 kB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 9-subtitle-en.srt 8.8 kB
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  • 06 Polynomial Regression/048 Polynomial Regression Intuition-subtitle-en.srt 8.0 kB
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  • 04 Simple Linear Regression/026 Simple Linear Regression in R - Step 1-subtitle-en.srt 7.9 kB
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  • 19 Evaluating Classification Models Performance/122 Confusion Matrix-subtitle-en.srt 7.7 kB
  • 12 Logistic Regression/087 Logistic Regression in R - Step 3-subtitle-en.srt 7.6 kB
  • 31 Artificial Neural Networks/213 Backpropagation-subtitle-en.srt 7.5 kB
  • 10 Evaluating Regression Models Performance/071 R-Squared Intuition-subtitle-en.srt 7.3 kB
  • 12 Logistic Regression/082 Logistic Regression in Python - Step 4-subtitle-en.srt 7.3 kB
  • 05 Multiple Linear Regression/045 Multiple Linear Regression in R - Step 3-subtitle-en.srt 7.2 kB
  • 18 Random Forest Classification/117 Random Forest Classification Intuition-subtitle-en.srt 7.2 kB
  • 22 Hierarchical Clustering/142 HC in Python - Step 5-subtitle-en.srt 7.0 kB
  • 12 Logistic Regression/090 R Classification Template-subtitle-en.srt 6.9 kB
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  • 12 Logistic Regression/084 Python Classification Template-subtitle-en.srt 6.2 kB
  • 28 Thompson Sampling/177 Thompson Sampling in Python - Step 2-subtitle-en.srt 5.9 kB
  • 40 Bonus Lectures/276 ___YOUR SPECIAL BONUS___.html 5.9 kB
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  • 29 -------------------- Part 7_ Natural Language Processing --------------------/199 Natural Language Processing in R - Step 7-subtitle-en.srt 5.7 kB
  • 04 Simple Linear Regression/028 Simple Linear Regression in R - Step 3-subtitle-en.srt 5.7 kB
  • 01 Welcome to the course!/001 Applications of Machine Learning-subtitle-en.srt 5.6 kB
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  • 31 Artificial Neural Networks/218 ANN in Python - Step 3-subtitle-en.srt 5.3 kB
  • 27 Upper Confidence Bound (UCB)/168 Upper Confidence Bound in Python - Step 4-subtitle-en.srt 5.3 kB
  • 15 Kernel SVM/102 Types of Kernel Functions-subtitle-en.srt 5.2 kB
  • 04 Simple Linear Regression/018 How to get the dataset-subtitle-en.srt 5.0 kB
  • 05 Multiple Linear Regression/030 How to get the dataset-subtitle-en.srt 5.0 kB
  • 06 Polynomial Regression/049 How to get the dataset-subtitle-en.srt 5.0 kB
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  • 08 Decision Tree Regression/064 How to get the dataset-subtitle-en.srt 5.0 kB
  • 09 Random Forest Regression/068 How to get the dataset-subtitle-en.srt 5.0 kB
  • 12 Logistic Regression/078 How to get the dataset-subtitle-en.srt 5.0 kB
  • 13 K-Nearest Neighbors (K-NN)/092 How to get the dataset-subtitle-en.srt 5.0 kB
  • 14 Support Vector Machine (SVM)/096 How to get the dataset-subtitle-en.srt 5.0 kB
  • 15 Kernel SVM/103 How to get the dataset-subtitle-en.srt 5.0 kB
  • 16 Naive Bayes/110 How to get the dataset-subtitle-en.srt 5.0 kB
  • 17 Decision Tree Classification/114 How to get the dataset-subtitle-en.srt 5.0 kB
  • 18 Random Forest Classification/118 How to get the dataset-subtitle-en.srt 5.0 kB
  • 21 K-Means Clustering/131 How to get the dataset-subtitle-en.srt 5.0 kB
  • 22 Hierarchical Clustering/137 How to get the dataset-subtitle-en.srt 5.0 kB
  • 24 Apriori/151 How to get the dataset-subtitle-en.srt 5.0 kB
  • 25 Eclat/159 How to get the dataset-subtitle-en.srt 5.0 kB
  • 27 Upper Confidence Bound (UCB)/164 How to get the dataset-subtitle-en.srt 5.0 kB
  • 28 Thompson Sampling/175 How to get the dataset-subtitle-en.srt 5.0 kB
  • 29 -------------------- Part 7_ Natural Language Processing --------------------/181 How to get the dataset-subtitle-en.srt 5.0 kB
  • 31 Artificial Neural Networks/214 How to get the dataset-subtitle-en.srt 5.0 kB
  • 32 Convolutional Neural Networks/239 How to get the dataset-subtitle-en.srt 5.0 kB
  • 34 Principal Component Analysis (PCA)/252 How to get the dataset-subtitle-en.srt 5.0 kB
  • 35 Linear Discriminant Analysis (LDA)/259 How to get the dataset-subtitle-en.srt 5.0 kB
  • 36 Kernel PCA/262 How to get the dataset-subtitle-en.srt 5.0 kB
  • 38 Model Selection/266 How to get the dataset-subtitle-en.srt 5.0 kB
  • 39 XGBoost/272 How to get the dataset-subtitle-en.srt 5.0 kB
  • 12 Logistic Regression/080 Logistic Regression in Python - Step 2-subtitle-en.srt 5.0 kB
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  • 15 Kernel SVM/099 Kernel SVM Intuition-subtitle-en.srt 4.7 kB
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  • 04 Simple Linear Regression/021 Simple Linear Regression Intuition - Step 2-subtitle-en.srt 4.4 kB
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  • 29 -------------------- Part 7_ Natural Language Processing --------------------/192 Homework Challenge.html 2.5 kB
  • 33 -------------------- Part 9_ Dimensionality Reduction --------------------/251 Welcome to Part 9 - Dimensionality Reduction.html 2.4 kB
  • 01 Welcome to the course!/004 Update_ Recommended Anaconda Version.html 2.3 kB
  • 01 Welcome to the course!/006 BONUS_ Meet your instructors.html 2.2 kB
  • 37 -------------------- Part 10_ Model Selection & Boosting --------------------/265 Welcome to Part 10 - Model Selection & Boosting.html 2.1 kB
  • 03 -------------------- Part 2_ Regression --------------------/017 Welcome to Part 2 - Regression.html 2.0 kB
  • 30 -------------------- Part 8_ Deep Learning --------------------/204 Welcome to Part 8 - Deep Learning.html 2.0 kB
  • 11 -------------------- Part 3_ Classification --------------------/076 Welcome to Part 3 - Classification.html 2.0 kB
  • 26 -------------------- Part 6_ Reinforcement Learning --------------------/161 Welcome to Part 6 - Reinforcement Learning.html 2.0 kB
  • 20 -------------------- Part 4_ Clustering --------------------/127 Welcome to Part 4 - Clustering.html 1.9 kB
  • 32 Convolutional Neural Networks/242 CNN in Python - Step 3-subtitle-en.srt 1.8 kB
  • 22 Hierarchical Clustering/148 Conclusion of Part 4 - Clustering.html 1.7 kB
  • 05 Multiple Linear Regression/032 Multiple Linear Regression Intuition - Step 1-subtitle-en.srt 1.6 kB
  • 23 -------------------- Part 5_ Association Rule Learning --------------------/149 Welcome to Part 5 - Association Rule Learning.html 1.6 kB
  • 05 Multiple Linear Regression/033 Multiple Linear Regression Intuition - Step 2-subtitle-en.srt 1.5 kB
  • [FreeCourseSite.com].txt 1.1 kB
  • [HaxTech.me].txt 1.1 kB
  • [FreeCourseSite.com].url 127 Bytes
  • [HaxTech.me].url 123 Bytes

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