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| [FreeCourseSite.com].url |
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| 001 Apriori Intuition_en.srt |
31.76KB |
| 001 Apriori Intuition.mp4 |
56.18MB |
| 001 Bayes Theorem_en.srt |
33.15KB |
| 001 Bayes Theorem.mp4 |
145.60MB |
| 001 dataset.zip |
221.28MB |
| 001 Dataset + Business Problem Description_en.srt |
5.47KB |
| 001 Dataset + Business Problem Description.mp4 |
14.09MB |
| 001 Decision Tree Classification Intuition_en.srt |
12.40KB |
| 001 Decision Tree Classification Intuition.mp4 |
17.77MB |
| 001 Decision Tree Regression Intuition_en.srt |
16.42KB |
| 001 Decision Tree Regression Intuition.mp4 |
23.24MB |
| 001 Eclat Intuition_en.srt |
10.13KB |
| 001 Eclat Intuition.mp4 |
24.27MB |
| 001 Evaluating Regression Models Performance - Homework's Final Part_en.srt |
12.44KB |
| 001 Evaluating Regression Models Performance - Homework's Final Part.mp4 |
27.71MB |
| 001 False Positives & False Negatives_en.srt |
10.89KB |
| 001 False Positives & False Negatives.mp4 |
19.69MB |
| 001 Getting Started_en.srt |
3.05KB |
| 001 Getting Started.mp4 |
4.06MB |
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10.64KB |
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10.78MB |
| 001 Hierarchical Clustering Intuition_en.srt |
14.06KB |
| 001 Hierarchical Clustering Intuition.mp4 |
36.21MB |
| 001 Kernel PCA in Python_en.srt |
19.45KB |
| 001 Kernel PCA in Python.mp4 |
56.92MB |
| 001 Kernel SVM Intuition_en.srt |
5.75KB |
| 001 Kernel SVM Intuition.mp4 |
6.89MB |
| 001 k-Fold Cross Validation in Python_en.srt |
32.63KB |
| 001 k-Fold Cross Validation in Python.mp4 |
62.05MB |
| 001 K-Nearest Neighbor Intuition_en.srt |
7.74KB |
| 001 K-Nearest Neighbor Intuition.mp4 |
10.46MB |
| 001 Linear Discriminant Analysis (LDA) Intuition_en.srt |
6.57KB |
| 001 Linear Discriminant Analysis (LDA) Intuition.mp4 |
15.06MB |
| 001 Logistic Regression Intuition_en.srt |
31.48KB |
| 001 Logistic Regression Intuition.mp4 |
32.45MB |
| 001 Machine-Learning-A-Z-Model-Selection.zip |
161.91KB |
| 001 Machine-Learning-A-Z-Model-Selection.zip |
160.01KB |
| 001 Make sure you have this Model Selection folder ready.html |
973B |
| 001 Make sure you have this Model Selection folder ready.html |
985B |
| 001 OUR SPECIAL OFFER.html |
4.53KB |
| 001 Plan of attack_en.srt |
5.03KB |
| 001 Plan of attack_en.srt |
6.96KB |
| 001 Plan of attack.mp4 |
4.79MB |
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6.23MB |
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7.54KB |
| 001 Polynomial Regression Intuition.mp4 |
8.59MB |
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6.43KB |
| 001 Principal Component Analysis (PCA) Intuition.mp4 |
21.00MB |
| 001 Random Forest Classification Intuition_en.srt |
6.81KB |
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41.56MB |
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9.88KB |
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35.79MB |
| 001 R-Squared Intuition_en.srt |
8.07KB |
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16.54MB |
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4.08KB |
| 001 Simple Linear Regression Intuition.mp4 |
4.97MB |
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15.17KB |
| 001 SVM Intuition.mp4 |
20.12MB |
| 001 SVR Intuition (Updated!)_en.srt |
14.74KB |
| 001 SVR Intuition (Updated!).mp4 |
36.83MB |
| 001 The Multi-Armed Bandit Problem_en.srt |
27.19KB |
| 001 The Multi-Armed Bandit Problem.mp4 |
96.44MB |
| 001 Thompson Sampling Intuition_en.srt |
34.99KB |
| 001 Thompson Sampling Intuition.mp4 |
48.70MB |
| 001 Welcome Challenge!.html |
3.02KB |
| 001 Welcome to Part 10 - Model Selection & Boosting.html |
921B |
| 001 Welcome to Part 1 - Data Preprocessing.html |
531B |
| 001 Welcome to Part 2 - Regression.html |
829B |
| 001 Welcome to Part 3 - Classification.html |
887B |
| 001 Welcome to Part 4 - Clustering.html |
789B |
| 001 Welcome to Part 5 - Association Rule Learning.html |
477B |
| 001 Welcome to Part 6 - Reinforcement Learning.html |
1.52KB |
| 001 Welcome to Part 7 - Natural Language Processing.html |
1.70KB |
| 001 Welcome to Part 8 - Deep Learning.html |
874B |
| 001 Welcome to Part 9 - Dimensionality Reduction.html |
1.32KB |
| 001 What is Classification_en.srt |
4.55KB |
| 001 What is Classification.mp4 |
5.58MB |
| 001 What is Clustering (Supervised vs Unsupervised Learning)_en.srt |
6.53KB |
| 001 What is Clustering (Supervised vs Unsupervised Learning).mp4 |
15.45MB |
| 001 XGBoost in Python_en.srt |
25.35KB |
| 001 XGBoost in Python.mp4 |
84.25MB |
| 002 Accuracy Paradox_en.srt |
3.12KB |
| 002 Accuracy Paradox.mp4 |
4.21MB |
| 002 Adjusted R-Squared Intuition_en.srt |
9.10KB |
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11.57MB |
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14.01KB |
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17.24MB |
| 002 Apriori in Python - Step 1_en.srt |
17.43KB |
| 002 Apriori in Python - Step 1.mp4 |
58.33MB |
| 002 Confusion Matrix & Accuracy Ratios_en.srt |
8.37KB |
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28.70MB |
| 002 Dataset Description_en.srt |
3.84KB |
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6.42MB |
| 002 Decision Tree Classification in Python - Step 1_en.srt |
11.26KB |
| 002 Decision Tree Classification in Python - Step 1.mp4 |
37.83MB |
| 002 Decision Tree Regression in Python - Step 1a_en.srt |
8.57KB |
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9.32MB |
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22.55KB |
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56.20MB |
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35.12MB |
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38.47KB |
| 002 Grid Search in Python.mp4 |
114.43MB |
| 002 Heads-up on non-linear SVR_en.srt |
7.31KB |
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19.76MB |
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13.81KB |
| 002 Hierarchical Clustering How Dendrograms Work.mp4 |
16.44MB |
| 002 Interpreting Linear Regression Coefficients_en.srt |
12.85KB |
| 002 Interpreting Linear Regression Coefficients.mp4 |
52.60MB |
| 002 Kernel PCA in R_en.srt |
37.28KB |
| 002 Kernel PCA in R.mp4 |
228.81MB |
| 002 K-Means Clustering Intuition_en.srt |
5.00KB |
| 002 K-Means Clustering Intuition.mp4 |
4.09MB |
| 002 K-NN in Python - Step 1_en.srt |
10.70KB |
| 002 K-NN in Python - Step 1.mp4 |
35.04MB |
| 002 LDA in Python_en.srt |
26.37KB |
| 002 LDA in Python.mp4 |
75.41MB |
| 002 Logistic Regression Intuition_en.srt |
8.73KB |
| 002 Logistic Regression Intuition.mp4 |
24.75MB |
| 002 Machine Learning Demo - Get Excited!_en.srt |
9.10KB |
| 002 Machine Learning Demo - Get Excited!.mp4 |
50.77MB |
| 002 Mapping to a higher dimension_en.srt |
14.52KB |
| 002 Mapping to a higher dimension.mp4 |
31.86MB |
| 002 Model Selection and Boosting Additional Content.html |
1.13KB |
| 002 Multiple Linear Regression Intuition_en.srt |
4.46KB |
| 002 Multiple Linear Regression Intuition.mp4 |
8.41MB |
| 002 Naive Bayes Intuition_en.srt |
22.49KB |
| 002 Naive Bayes Intuition.mp4 |
57.39MB |
| 002 NLP Intuition_en.srt |
5.46KB |
| 002 NLP Intuition.mp4 |
5.19MB |
| 002 Ordinary Least Squares_en.srt |
5.80KB |
| 002 Ordinary Least Squares.mp4 |
12.73MB |
| 002 PCA in Python - Step 1_en.srt |
30.11KB |
| 002 PCA in Python - Step 1.mp4 |
85.98MB |
| 002 Polynomial Regression in Python - Step 1a_en.srt |
8.37KB |
| 002 Polynomial Regression in Python - Step 1a.mp4 |
7.46MB |
| 002 Preparation of the Regression Code Templates - Step 1_en.srt |
8.51KB |
| 002 Preparation of the Regression Code Templates - Step 1.mp4 |
10.40MB |
| 002 Random Forest Classification in Python - Step 1_en.srt |
10.67KB |
| 002 Random Forest Classification in Python - Step 1.mp4 |
34.92MB |
| 002 Random Forest Regression in Python - Step 1_en.srt |
11.01KB |
| 002 Random Forest Regression in Python - Step 1.mp4 |
17.50MB |
| 002 SVM in Python - Step 1_en.srt |
10.88KB |
| 002 SVM in Python - Step 1.mp4 |
51.51MB |
| 002 The Machine Learning process_en.srt |
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| 002 The Machine Learning process.mp4 |
7.98MB |
| 002 The Neuron_en.srt |
31.18KB |
| 002 The Neuron.mp4 |
44.10MB |
| 002 Upper Confidence Bound (UCB) Intuition_en.srt |
26.89KB |
| 002 Upper Confidence Bound (UCB) Intuition.mp4 |
79.23MB |
| 002 What are convolutional neural networks_en.srt |
28.74KB |
| 002 What are convolutional neural networks.mp4 |
71.07MB |
| 002 What is Deep Learning_en.srt |
24.29KB |
| 002 What is Deep Learning.mp4 |
102.91MB |
| 003 Apriori in Python - Step 2_en.srt |
31.11KB |
| 003 Apriori in Python - Step 2.mp4 |
82.25MB |
| 003 Assumptions of Linear Regression_en.srt |
8.77KB |
| 003 Assumptions of Linear Regression.mp4 |
24.61MB |
| 003 CAP Curve_en.srt |
15.60KB |
| 003 CAP Curve.mp4 |
18.98MB |
| 003 Conclusion of Part 2 - Regression.html |
1.71KB |
| 003 Decision Tree Classification in Python - Step 2_en.srt |
11.96KB |
| 003 Decision Tree Classification in Python - Step 2.mp4 |
33.69MB |
| 003 Decision Tree Regression in Python - Step 1b_en.srt |
7.32KB |
| 003 Decision Tree Regression in Python - Step 1b.mp4 |
11.06MB |
| 003 Download-the-PDF.url |
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| 003 Eclat.zip |
48.54KB |
| 003 Eclat in R_en.srt |
15.19KB |
| 003 Eclat in R.mp4 |
65.30MB |
| 003 Get all the Datasets, Codes and Slides here.html |
442B |
| 003 Hierarchical Clustering Using Dendrograms_en.srt |
16.98KB |
| 003 Hierarchical Clustering Using Dendrograms.mp4 |
25.19MB |
| 003 Importing the Dataset_en.srt |
4.89KB |
| 003 Importing the Dataset.mp4 |
6.91MB |
| 003 Importing the Libraries_en.srt |
7.05KB |
| 003 Importing the Libraries.mp4 |
7.45MB |
| 003 k-Fold Cross Validation in R_en.srt |
35.34KB |
| 003 k-Fold Cross Validation in R.mp4 |
53.26MB |
| 003 K-NN in Python - Step 2_en.srt |
12.34KB |
| 003 K-NN in Python - Step 2.mp4 |
33.58MB |
| 003 LDA in R_en.srt |
35.50KB |
| 003 LDA in R.mp4 |
93.68MB |
| 003 Maximum Likelihood_en.srt |
6.78KB |
| 003 Maximum Likelihood.mp4 |
7.11MB |
| 003 Naive Bayes Intuition (Challenge Reveal)_en.srt |
9.16KB |
| 003 Naive Bayes Intuition (Challenge Reveal).mp4 |
11.81MB |
| 003 PCA in Python - Step 2_en.srt |
10.44KB |
| 003 PCA in Python - Step 2.mp4 |
20.75MB |
| 003 Polynomial Regression in Python - Step 1b_en.srt |
11.30KB |
| 003 Polynomial Regression in Python - Step 1b.mp4 |
19.74MB |
| 003 Preparation of the Regression Code Templates - Step 2_en.srt |
10.97KB |
| 003 Preparation of the Regression Code Templates - Step 2.mp4 |
21.93MB |
| 003 Random Forest Classification in Python - Step 2_en.srt |
11.60KB |
| 003 Random Forest Classification in Python - Step 2.mp4 |
32.83MB |
| 003 Random Forest Regression in Python - Step 2_en.srt |
10.71KB |
| 003 Random Forest Regression in Python - Step 2.mp4 |
29.17MB |
| 003 Regression-Bonus.zip |
364.49KB |
| 003 Simple Linear Regression in Python - Step 1a_en.srt |
11.22KB |
| 003 Simple Linear Regression in Python - Step 1a.mp4 |
8.69MB |
| 003 Splitting the data into a Training and Test set_en.srt |
3.48KB |
| 003 Splitting the data into a Training and Test set.mp4 |
5.36MB |
| 003 Step 1 - Convolution Operation_en.srt |
29.42KB |
| 003 Step 1 - Convolution Operation.mp4 |
65.62MB |
| 003 SVM in Python - Step 2_en.srt |
11.29KB |
| 003 SVM in Python - Step 2.mp4 |
37.68MB |
| 003 SVR in Python - Step 1a_en.srt |
10.62KB |
| 003 SVR in Python - Step 1a.mp4 |
12.06MB |
| 003 The Activation Function_en.srt |
15.50KB |
| 003 The Activation Function.mp4 |
17.25MB |
| 003 The Elbow Method_en.srt |
7.74KB |
| 003 The Elbow Method.mp4 |
7.50MB |
| 003 The Kernel Trick_en.srt |
20.32KB |
| 003 The Kernel Trick.mp4 |
33.55MB |
| 003 Thompson Sampling in Python - Step 1_en.srt |
11.64KB |
| 003 Thompson Sampling in Python - Step 1.mp4 |
12.93MB |
| 003 Types of Natural Language Processing_en.srt |
6.94KB |
| 003 Types of Natural Language Processing.mp4 |
8.14MB |
| 003 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 1_en.srt |
11.75KB |
| 003 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 1.mp4 |
21.03MB |
| 003 Upper Confidence Bound in Python - Step 1_en.srt |
25.55KB |
| 003 Upper Confidence Bound in Python - Step 1.mp4 |
44.65MB |
| 003 XGBoost in R_en.srt |
31.07KB |
| 003 XGBoost in R.mp4 |
69.37MB |
| 004 Apriori in Python - Step 3_en.srt |
23.34KB |
| 004 Apriori in Python - Step 3.mp4 |
39.45MB |
| 004 CAP Curve Analysis_en.srt |
8.91KB |
| 004 CAP Curve Analysis.mp4 |
14.41MB |
| 004 Classical vs Deep Learning Models_en.srt |
19.42KB |
| 004 Classical vs Deep Learning Models.mp4 |
83.96MB |
| 004 Decision Tree Classification in R - Step 1_en.srt |
10.42KB |
| 004 Decision Tree Classification in R - Step 1.mp4 |
57.77MB |
| 004 Decision Tree Regression in Python - Step 2_en.srt |
9.77KB |
| 004 Decision Tree Regression in Python - Step 2.mp4 |
12.15MB |
| 004 Feature Scaling_en.srt |
11.96KB |
| 004 Feature Scaling.mp4 |
14.02MB |
| 004 Grid Search in R_en.srt |
25.99KB |
| 004 Grid Search in R.mp4 |
50.05MB |
| 004 Hierarchical Clustering in Python - Step 1_en.srt |
11.08KB |
| 004 Hierarchical Clustering in Python - Step 1.mp4 |
20.44MB |
| 004 How do Neural Networks work_en.srt |
24.59KB |
| 004 How do Neural Networks work.mp4 |
67.19MB |
| 004 How to use the ML A-Z folder & Google Colab_en.srt |
11.74KB |
| 004 How to use the ML A-Z folder & Google Colab.mp4 |
25.62MB |
| 004 Importing the Dataset - Step 1_en.srt |
10.26KB |
| 004 Importing the Dataset - Step 1.mp4 |
12.55MB |
| 004 K-Means++_en.srt |
8.59KB |
| 004 K-Means++.mp4 |
18.73MB |
| 004 K-NN in Python - Step 3_en.srt |
11.12KB |
| 004 K-NN in Python - Step 3.mp4 |
34.31MB |
| 004 Logistic Regression in Python - Step 1a_en.srt |
10.17KB |
| 004 Logistic Regression in Python - Step 1a.mp4 |
11.90MB |
| 004 Multiple Linear Regression Intuition - Step 3_en.srt |
10.33KB |
| 004 Multiple Linear Regression Intuition - Step 3.mp4 |
19.01MB |
| 004 Naive Bayes Intuition (Extras)_en.srt |
15.35KB |
| 004 Naive Bayes Intuition (Extras).mp4 |
16.11MB |
| 004 PCA in R - Step 1_en.srt |
23.95KB |
| 004 PCA in R - Step 1.mp4 |
100.61MB |
| 004 Polynomial Regression in Python - Step 2a_en.srt |
11.38KB |
| 004 Polynomial Regression in Python - Step 2a.mp4 |
16.46MB |
| 004 Preparation of the Regression Code Templates - Step 3_en.srt |
8.05KB |
| 004 Preparation of the Regression Code Templates - Step 3.mp4 |
13.75MB |
| 004 Random Forest Classification in R - Step 1_en.srt |
11.52KB |
| 004 Random Forest Classification in R - Step 1.mp4 |
24.03MB |
| 004 Random Forest Regression in R - Step 1_en.srt |
11.07KB |
| 004 Random Forest Regression in R - Step 1.mp4 |
20.10MB |
| 004 Simple Linear Regression in Python - Step 1b_en.srt |
11.23KB |
| 004 Simple Linear Regression in Python - Step 1b.mp4 |
14.54MB |
| 004 Step 1(b) - ReLU Layer_en.srt |
11.32KB |
| 004 Step 1(b) - ReLU Layer.mp4 |
20.62MB |
| 004 SVM in Python - Step 3_en.srt |
5.87KB |
| 004 SVM in Python - Step 3.mp4 |
11.75MB |
| 004 SVR in Python - Step 1b_en.srt |
7.00KB |
| 004 SVR in Python - Step 1b.mp4 |
9.49MB |
| 004 Taking care of Missing Data_en.srt |
10.60KB |
| 004 Taking care of Missing Data.mp4 |
21.44MB |
| 004 Thompson Sampling in Python - Step 2_en.srt |
22.35KB |
| 004 Thompson Sampling in Python - Step 2.mp4 |
34.28MB |
| 004 Types of Kernel Functions_en.srt |
6.68KB |
| 004 Types of Kernel Functions.mp4 |
10.53MB |
| 004 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 2_en.srt |
11.44KB |
| 004 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 2.mp4 |
33.00MB |
| 004 Upper Confidence Bound in Python - Step 2_en.srt |
7.76KB |
| 004 Upper Confidence Bound in Python - Step 2.mp4 |
9.01MB |
| 005 Apriori in Python - Step 4_en.srt |
35.74KB |
| 005 Apriori in Python - Step 4.mp4 |
116.74MB |
| 005 Bag-Of-Words Model_en.srt |
30.23KB |
| 005 Bag-Of-Words Model.mp4 |
37.97MB |
| 005 Classification-Pros-Cons.pdf |
29.25KB |
| 005 Conclusion of Part 3 - Classification.html |
3.35KB |
| 005 Decision Tree Classification in R - Step 2_en.srt |
10.11KB |
| 005 Decision Tree Classification in R - Step 2.mp4 |
42.85MB |
| 005 Decision Tree Regression in Python - Step 3_en.srt |
6.16KB |
| 005 Decision Tree Regression in Python - Step 3.mp4 |
8.38MB |
| 005 Encoding Categorical Data_en.srt |
10.23KB |
| 005 Encoding Categorical Data.mp4 |
63.89MB |
| 005 Hierarchical Clustering in Python - Step 2a_en.srt |
8.61KB |
| 005 Hierarchical Clustering in Python - Step 2a.mp4 |
10.88MB |
| 005 How do Neural Networks learn_en.srt |
24.15KB |
| 005 How do Neural Networks learn.mp4 |
43.33MB |
| 005 Importing the Dataset - Step 2_en.srt |
8.53KB |
| 005 Importing the Dataset - Step 2.mp4 |
9.80MB |
| 005 Installing R and R Studio (Mac, Linux & Windows)_en.srt |
10.41KB |
| 005 Installing R and R Studio (Mac, Linux & Windows).mp4 |
33.59MB |
| 005 K-Means Clustering in Python - Step 1a_en.srt |
9.21KB |
| 005 K-Means Clustering in Python - Step 1a.mp4 |
10.62MB |
| 005 K-NN in R - Step 1_en.srt |
10.32KB |
| 005 K-NN in R - Step 1.mp4 |
40.57MB |
| 005 Logistic Regression in Python - Step 1b_en.srt |
8.02KB |
| 005 Logistic Regression in Python - Step 1b.mp4 |
9.27MB |
| 005 Multiple Linear Regression Intuition - Step 4_en.srt |
3.38KB |
| 005 Multiple Linear Regression Intuition - Step 4.mp4 |
16.46MB |
| 005 Naive Bayes in Python - Step 1_en.srt |
10.63KB |
| 005 Naive Bayes in Python - Step 1.mp4 |
52.57MB |
| 005 Non-Linear Kernel SVR (Advanced)_en.srt |
20.47KB |
| 005 Non-Linear Kernel SVR (Advanced).mp4 |
27.49MB |
| 005 PCA in R - Step 2_en.srt |
20.95KB |
| 005 PCA in R - Step 2.mp4 |
46.51MB |
| 005 Polynomial Regression in Python - Step 2b_en.srt |
10.65KB |
| 005 Polynomial Regression in Python - Step 2b.mp4 |
17.97MB |
| 005 Preparation of the Regression Code Templates - Step 4_en.srt |
7.50KB |
| 005 Preparation of the Regression Code Templates - Step 4.mp4 |
24.93MB |
| 005 Random Forest Classification in R - Step 2_en.srt |
10.90KB |
| 005 Random Forest Classification in R - Step 2.mp4 |
38.98MB |
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| 006 BONUS Use ChatGPT to Boost your ML Skills.html |
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| 006 Polynomial Regression in Python - Step 3a_en.srt |
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| 006 Random Forest Classification in R - Step 3_en.srt |
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| 006 Simple Linear Regression in Python - Step 2b_en.srt |
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| 007 Kernel SVM in Python - Step 2_en.srt |
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| 007 Polynomial Regression in Python - Step 3b_en.srt |
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| 007 Simple Linear Regression in Python - Step 3_en.srt |
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| 008 Kernel SVM in R - Step 1_en.srt |
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| 011 Logistic Regression in Python - Step 4b_en.srt |
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| 011 Polynomial Regression in R - Step 1b_en.srt |
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| 011 Simple Linear Regression in R - Step 1_en.srt |
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| 015 Polynomial Regression in R - Step 3b_en.srt |
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| 015 Polynomial Regression in R - Step 3b.mp4 |
19.55MB |
| 015 Simple Linear Regression in R - Step 4b_en.srt |
9.40KB |
| 015 Simple Linear Regression in R - Step 4b.mp4 |
20.68MB |
| 015 Splitting the dataset into the Training set and Test set - Step 3_en.srt |
6.73KB |
| 015 Splitting the dataset into the Training set and Test set - Step 3.mp4 |
11.64MB |
| 015 Warning - Update.html |
760B |
| 016 ANN in R - Step 2_en.srt |
12.39KB |
| 016 ANN in R - Step 2.mp4 |
24.97MB |
| 016 Deep Learning Additional Content #2.html |
923B |
| 016 Feature Scaling - Step 1_en.srt |
10.88KB |
| 016 Feature Scaling - Step 1.mp4 |
13.07MB |
| 016 K-Means Clustering in R - Step 1_en.srt |
11.34KB |
| 016 K-Means Clustering in R - Step 1.mp4 |
15.13MB |
| 016 Logistic Regression in Python - Step 7b_en.srt |
6.79KB |
| 016 Logistic Regression in Python - Step 7b.mp4 |
24.02MB |
| 016 Multiple Linear Regression in Python - Backward Elimination.html |
3.54KB |
| 016 Natural Language Processing in R - Step 2_en.srt |
17.37KB |
| 016 Natural Language Processing in R - Step 2.mp4 |
23.76MB |
| 016 Polynomial Regression in R - Step 3c_en.srt |
10.33KB |
| 016 Polynomial Regression in R - Step 3c.mp4 |
16.17MB |
| 017 ANN in R - Step 3_en.srt |
23.13KB |
| 017 ANN in R - Step 3.mp4 |
115.66MB |
| 017 Feature Scaling - Step 2_en.srt |
8.71KB |
| 017 Feature Scaling - Step 2.mp4 |
11.71MB |
| 017 K-Means Clustering in R - Step 2_en.srt |
11.17KB |
| 017 K-Means Clustering in R - Step 2.mp4 |
27.66MB |
| 017 Logistic Regression in Python - Step 7c_en.srt |
5.76KB |
| 017 Logistic Regression in Python - Step 7c.mp4 |
20.16MB |
| 017 Multiple Linear Regression in Python - EXTRA CONTENT.html |
1.19KB |
| 017 Natural Language Processing in R - Step 3_en.srt |
12.07KB |
| 017 Natural Language Processing in R - Step 3.mp4 |
18.61MB |
| 017 Polynomial Regression in R - Step 4a_en.srt |
6.74KB |
| 017 Polynomial Regression in R - Step 4a.mp4 |
14.99MB |
| 018 ANN in R - Step 4 (Last step)_en.srt |
25.02KB |
| 018 ANN in R - Step 4 (Last step).mp4 |
54.58MB |
| 018 Feature Scaling - Step 3_en.srt |
7.13KB |
| 018 Feature Scaling - Step 3.mp4 |
11.23MB |
| 018 Logistic Regression in Python - Step 7 (Colour-blind friendly image).html |
706B |
| 018 Multiple Linear Regression in R - Step 1a_en.srt |
6.76KB |
| 018 Multiple Linear Regression in R - Step 1a.mp4 |
10.60MB |
| 018 Natural Language Processing in R - Step 4_en.srt |
5.43KB |
| 018 Natural Language Processing in R - Step 4.mp4 |
8.80MB |
| 018 Polynomial Regression in R - Step 4b_en.srt |
7.49KB |
| 018 Polynomial Regression in R - Step 4b.mp4 |
14.25MB |
| 019 Deep Learning Additional Content.html |
1005B |
| 019 Feature Scaling - Step 4_en.srt |
11.12KB |
| 019 Feature Scaling - Step 4.mp4 |
16.87MB |
| 019 Logistic Regression in R - Step 1_en.srt |
8.55KB |
| 019 Logistic Regression in R - Step 1.mp4 |
19.25MB |
| 019 Multiple Linear Regression in R - Step 1b_en.srt |
6.39KB |
| 019 Multiple Linear Regression in R - Step 1b.mp4 |
14.67MB |
| 019 Natural Language Processing in R - Step 5_en.srt |
3.90KB |
| 019 Natural Language Processing in R - Step 5.mp4 |
6.16MB |
| 019 R Regression Template - Step 1_en.srt |
11.00KB |
| 019 R Regression Template - Step 1.mp4 |
20.57MB |
| 020 EXTRA CONTENT ANN Case Study.html |
544B |
| 020 Logistic Regression in R - Step 2_en.srt |
4.19KB |
| 020 Logistic Regression in R - Step 2.mp4 |
12.90MB |
| 020 Multiple Linear Regression in R - Step 2a_en.srt |
9.53KB |
| 020 Multiple Linear Regression in R - Step 2a.mp4 |
26.69MB |
| 020 Natural Language Processing in R - Step 6_en.srt |
10.46KB |
| 020 Natural Language Processing in R - Step 6.mp4 |
17.39MB |
| 020 R Regression Template - Step 2_en.srt |
9.64KB |
| 020 R Regression Template - Step 2.mp4 |
13.55MB |
| 021 Logistic Regression in R - Step 3_en.srt |
7.14KB |
| 021 Logistic Regression in R - Step 3.mp4 |
27.03MB |
| 021 Multiple Linear Regression in R - Step 2b_en.srt |
7.73KB |
| 021 Multiple Linear Regression in R - Step 2b.mp4 |
17.74MB |
| 021 Natural Language Processing in R - Step 7_en.srt |
6.83KB |
| 021 Natural Language Processing in R - Step 7.mp4 |
10.55MB |
| 022 Logistic Regression in R - Step 4_en.srt |
3.82KB |
| 022 Logistic Regression in R - Step 4.mp4 |
32.59MB |
| 022 Multiple Linear Regression in R - Step 3_en.srt |
6.78KB |
| 022 Multiple Linear Regression in R - Step 3.mp4 |
14.31MB |
| 022 Natural Language Processing in R - Step 8_en.srt |
10.12KB |
| 022 Natural Language Processing in R - Step 8.mp4 |
16.70MB |
| 023 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !_en.srt |
26.47KB |
| 023 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 |
64.53MB |
| 023 Natural Language Processing in R - Step 9_en.srt |
24.12KB |
| 023 Natural Language Processing in R - Step 9.mp4 |
39.29MB |
| 023 Warning - Update.html |
1.84KB |
| 024 Logistic Regression in R - Step 5a_en.srt |
10.47KB |
| 024 Logistic Regression in R - Step 5a.mp4 |
28.65MB |
| 024 Multiple Linear Regression in R - Backward Elimination - Homework Solution_en.srt |
11.40KB |
| 024 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 |
32.47MB |
| 024 Natural Language Processing in R - Step 10_en.srt |
31.78KB |
| 024 Natural Language Processing in R - Step 10.mp4 |
66.50MB |
| 025 Homework Challenge.html |
1.46KB |
| 025 Logistic Regression in R - Step 5b_en.srt |
10.99KB |
| 025 Logistic Regression in R - Step 5b.mp4 |
24.70MB |
| 025 Multiple Linear Regression in R - Automatic Backward Elimination.html |
752B |
| 026 Logistic Regression in R - Step 5c_en.srt |
8.87KB |
| 026 Logistic Regression in R - Step 5c.mp4 |
37.40MB |
| 027 Logistic Regression in R - Step 5 (Colour-blind friendly image).html |
706B |
| 028 R Classification Template_en.srt |
11.08KB |
| 028 R Classification Template.mp4 |
25.48MB |
| 029 Machine Learning Regression and Classification BONUS.html |
807B |
| 030 EXTRA CONTENT Logistic Regression Practical Case Study.html |
619B |
| external-links.txt |
70B |