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文件列表
37. Convolutional Neural Networks/10.1 Section 40 - Convolutional Neural Networks (CNN).zip
234.9 MB
26. K-Means Clustering/6. K-Means Clustering in Python - Step 2.srt
175.4 MB
29. Apriori/6. Apriori in Python - Step 4.mp4
172.3 MB
37. Convolutional Neural Networks/16. CNN in Python - FINAL DEMO!.mp4
160.2 MB
43. Model Selection/3. Grid Search in Python.mp4
159.2 MB
17. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp4
153.7 MB
22. Random Forest Classification/3. Random Forest Classification in Python.srt
149.4 MB
23. Classification Model Selection in Python/2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!.mp4
142.6 MB
27. Hierarchical Clustering/7. Hierarchical Clustering in Python - Step 2.mp4
142.5 MB
13. Regression Model Selection in Python/2. Preparation of the Regression Code Templates.mp4
129.6 MB
26. K-Means Clustering/9. K-Means Clustering in Python - Step 5.mp4
126.4 MB
16. Logistic Regression/9. Logistic Regression in Python - Step 7.mp4
124.4 MB
37. Convolutional Neural Networks/13. CNN in Python - Step 3.mp4
124.3 MB
39. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp4
118.4 MB
43. Model Selection/2. k-Fold Cross Validation in Python.mp4
117.8 MB
36. Artificial Neural Networks/13. ANN in Python - Step 2.mp4
116.4 MB
21. Decision Tree Classification/3. Decision Tree Classification in Python.mp4
113.3 MB
29. Apriori/4. Apriori in Python - Step 2.mp4
112.9 MB
37. Convolutional Neural Networks/12. CNN in Python - Step 2.mp4
112.1 MB
18. Support Vector Machine (SVM)/4. SVM in Python.mp4
109.9 MB
34/5. Bag-Of-Words Model.mp4
108.5 MB
40. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4
107.0 MB
3. Data Preprocessing in Python/9. Feature Scaling.mp4
106.7 MB
36. Artificial Neural Networks/16. ANN in Python - Step 5.mp4
106.3 MB
20. Naive Bayes/6. Naive Bayes in Python.mp4
105.3 MB
37. Convolutional Neural Networks/15. CNN in Python - Step 5.mp4
102.4 MB
22. Random Forest Classification/3. Random Forest Classification in Python.mp4
101.4 MB
1. Welcome to the course!/9. Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder.mp4
99.4 MB
16. Logistic Regression/15. Logistic Regression in R - Step 5.mp4
98.3 MB
9. Support Vector Regression (SVR)/8. SVR in Python - Step 5.mp4
98.2 MB
44. XGBoost/2. XGBoost in Python.mp4
94.4 MB
34/11. Natural Language Processing in Python - Step 5.mp4
94.0 MB
3. Data Preprocessing in Python/7. Encoding Categorical Data.mp4
92.9 MB
19. Kernel SVM/7. Kernel SVM in Python.mp4
92.7 MB
9. Support Vector Regression (SVR)/5. SVR in Python - Step 2.mp4
91.1 MB
4. Data Preprocessing in R/8. Splitting the dataset into the Training set and Test set.mp4
90.7 MB
32. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp4
89.5 MB
16. Logistic Regression/4. Logistic Regression in Python - Step 2.mp4
88.8 MB
34/4. Classical vs Deep Learning Models.mp4
88.0 MB
20. Naive Bayes/7. Naive Bayes in R.srt
86.3 MB
26. K-Means Clustering/7. K-Means Clustering in Python - Step 3.mp4
85.3 MB
4. Data Preprocessing in R/9. Feature Scaling.mp4
82.7 MB
33. Thompson Sampling/6. Thompson Sampling in Python - Step 3.mp4
82.5 MB
8. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp4
81.6 MB
41. Kernel PCA/2. Kernel PCA in Python.mp4
81.3 MB
30. Eclat/3. Eclat in Python.mp4
79.2 MB
27. Hierarchical Clustering/8. Hierarchical Clustering in Python - Step 3.mp4
78.9 MB
36. Artificial Neural Networks/14. ANN in Python - Step 3.mp4
78.7 MB
6. Simple Linear Regression/7. Simple Linear Regression in Python - Step 4.mp4
78.2 MB
11. Random Forest Regression/3. Random Forest Regression in Python.mp4
78.0 MB
7. Multiple Linear Regression/12. Multiple Linear Regression in Python - Step 4.mp4
76.0 MB
3. Data Preprocessing in Python/4. Importing the Dataset.mp4
75.3 MB
37. Convolutional Neural Networks/11. CNN in Python - Step 1.mp4
74.2 MB
33. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp4
73.4 MB
29. Apriori/3. Apriori in Python - Step 1.mp4
73.2 MB
8. Polynomial Regression/4. Polynomial Regression in Python - Step 2.mp4
72.7 MB
29. Apriori/5. Apriori in Python - Step 3.mp4
72.6 MB
3. Data Preprocessing in Python/6. Taking care of Missing Data.mp4
72.4 MB
21. Decision Tree Classification/4. Decision Tree Classification in R.mp4
71.5 MB
3. Data Preprocessing in Python/8. Splitting the dataset into the Training set and Test set.mp4
70.9 MB
36. Artificial Neural Networks/11. ANN in Python - Step 1.mp4
69.7 MB
19. Kernel SVM/5. Non-Linear Kernel SVR (Advanced).mp4
68.8 MB
36. Artificial Neural Networks/15. ANN in Python - Step 4.mp4
68.5 MB
18. Support Vector Machine (SVM)/5. SVM in R.mp4
68.5 MB
22. Random Forest Classification/4. Random Forest Classification in R.mp4
67.2 MB
7. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp4
65.4 MB
34/9. Natural Language Processing in Python - Step 3.mp4
63.5 MB
34/10. Natural Language Processing in Python - Step 4.mp4
63.0 MB
32. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp4
61.6 MB
8. Polynomial Regression/3. Polynomial Regression in Python - Step 1.mp4
61.1 MB
7. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp4
61.0 MB
32. Upper Confidence Bound (UCB)/13. Upper Confidence Bound in R - Step 3.mp4
60.7 MB
4. Data Preprocessing in R/7. Encoding Categorical Data.mp4
60.1 MB
13. Regression Model Selection in Python/3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION!.mp4
59.5 MB
41. Kernel PCA/3. Kernel PCA in R.mp4
59.3 MB
29. Apriori/9. Apriori in R - Step 3.mp4
59.3 MB
7. Multiple Linear Regression/6. Understanding the P-Value.mp4
59.2 MB
10. Decision Tree Regression/7. Decision Tree Regression in R.mp4
59.0 MB
17. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp4
58.5 MB
8. Polynomial Regression/9. Polynomial Regression in R - Step 3.mp4
57.5 MB
10. Decision Tree Regression/6. Decision Tree Regression in Python - Step 4.mp4
57.4 MB
3. Data Preprocessing in Python/2. Getting Started.mp4
57.0 MB
34/24. Natural Language Processing in R - Step 10.mp4
56.8 MB
26. K-Means Clustering/6. K-Means Clustering in Python - Step 2.mp4
56.7 MB
16. Logistic Regression/8. Logistic Regression in Python - Step 6.mp4
55.5 MB
34/12. Natural Language Processing in Python - Step 6.mp4
55.5 MB
29. Apriori/7. Apriori in R - Step 1.mp4
55.4 MB
19. Kernel SVM/8. Kernel SVM in R.mp4
55.4 MB
44. XGBoost/5. THANK YOU Bonus Video.mp4
54.8 MB
11. Random Forest Regression/4. Random Forest Regression in R.mp4
54.4 MB
40. Linear Discriminant Analysis (LDA)/4. LDA in R.mp4
53.8 MB
34/15. Natural Language Processing in R - Step 1.mp4
53.7 MB
33. Thompson Sampling/9. Thompson Sampling in R - Step 1.mp4
53.5 MB
7. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp4
53.4 MB
7. Multiple Linear Regression/18. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4
53.3 MB
4. Data Preprocessing in R/10. Data Preprocessing Template.mp4
53.2 MB
20. Naive Bayes/1. Bayes Theorem.mp4
52.9 MB
36. Artificial Neural Networks/17. ANN in R - Step 1.mp4
52.3 MB
20. Naive Bayes/7. Naive Bayes in R.mp4
52.2 MB
6. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp4
51.5 MB
6. Simple Linear Regression/4. Simple Linear Regression in Python - Step 1.mp4
51.0 MB
44. XGBoost/4. XGBoost in R.mp4
49.6 MB
9. Support Vector Regression (SVR)/7. SVR in Python - Step 4.mp4
48.5 MB
7. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 2.mp4
47.4 MB
16. Logistic Regression/6. Logistic Regression in Python - Step 4.mp4
47.4 MB
32. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in Python - Step 6.mp4
47.1 MB
33. Thompson Sampling/7. Thompson Sampling in Python - Step 4.mp4
46.8 MB
16. Logistic Regression/3. Logistic Regression in Python - Step 1.mp4
46.8 MB
36. Artificial Neural Networks/20. ANN in R - Step 4 (Last step).mp4
45.9 MB
43. Model Selection/4. k-Fold Cross Validation in R.mp4
45.8 MB
32. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in Python - Step 7.mp4
45.4 MB
16. Logistic Regression/5. Logistic Regression in Python - Step 3.mp4
45.1 MB
37. Convolutional Neural Networks/7. Step 4 - Full Connection.mp4
44.8 MB
9. Support Vector Regression (SVR)/4. SVR in Python - Step 1.mp4
44.6 MB
10. Decision Tree Regression/3. Decision Tree Regression in Python - Step 1.mp4
44.5 MB
39. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp4
42.8 MB
34/8. Natural Language Processing in Python - Step 2.mp4
42.4 MB
37. Convolutional Neural Networks/5. Step 2 - Pooling.mp4
42.2 MB
27. Hierarchical Clustering/6. Hierarchical Clustering in Python - Step 1.mp4
42.2 MB
37. Convolutional Neural Networks/14. CNN in Python - Step 4.mp4
42.0 MB
6. Simple Linear Regression/5. Simple Linear Regression in Python - Step 2.mp4
41.8 MB
4. Data Preprocessing in R/6. Taking care of Missing Data.mp4
41.7 MB
29. Apriori/8. Apriori in R - Step 2.mp4
40.7 MB
8. Polynomial Regression/6. Polynomial Regression in Python - Step 4.mp4
40.7 MB
32. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp4
40.3 MB
26. K-Means Clustering/5. K-Means Clustering in Python - Step 1.mp4
39.9 MB
36. Artificial Neural Networks/19. ANN in R - Step 3.mp4
39.7 MB
34/23. Natural Language Processing in R - Step 9.mp4
39.5 MB
33. Thompson Sampling/1. Thompson Sampling Intuition.mp4
39.1 MB
26. K-Means Clustering/10. K-Means Clustering in R.mp4
38.7 MB
9. Support Vector Regression (SVR)/1. SVR Intuition (Updated!).mp4
38.6 MB
39. Principal Component Analysis (PCA)/7. PCA in R - Step 3.mp4
38.5 MB
43. Model Selection/5. Grid Search in R.mp4
37.3 MB
26. K-Means Clustering/8. K-Means Clustering in Python - Step 4.mp4
36.8 MB
29. Apriori/1. Apriori Intuition.mp4
36.7 MB
9. Support Vector Regression (SVR)/6. SVR in Python - Step 3.mp4
36.5 MB
19. Kernel SVM/3. The Kernel Trick.mp4
36.4 MB
32. Upper Confidence Bound (UCB)/12. Upper Confidence Bound in R - Step 2.mp4
35.8 MB
34/7. Natural Language Processing in Python - Step 1.mp4
35.7 MB
32. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 1.mp4
35.7 MB
9. Support Vector Regression (SVR)/9. SVR in R.mp4
35.4 MB
37. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp4
34.9 MB
7. Multiple Linear Regression/7. Multiple Linear Regression Intuition - Step 5.mp4
34.4 MB
32. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in Python - Step 5.mp4
34.0 MB
8. Polynomial Regression/8. Polynomial Regression in R - Step 2.mp4
33.9 MB
18. Support Vector Machine (SVM)/4. SVM in Python.srt
33.8 MB
39. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp4
33.7 MB
8. Polynomial Regression/11. R Regression Template.mp4
32.9 MB
35. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.mp4
32.8 MB
20. Naive Bayes/2. Naive Bayes Intuition.mp4
32.6 MB
37. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp4
32.5 MB
39. Principal Component Analysis (PCA)/5. PCA in R - Step 1.mp4
32.1 MB
33. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp4
32.1 MB
16. Logistic Regression/7. Logistic Regression in Python - Step 5.mp4
32.1 MB
32. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp4
31.7 MB
26. K-Means Clustering/1. K-Means Clustering Intuition.mp4
31.4 MB
36. Artificial Neural Networks/2. The Neuron.mp4
31.3 MB
37. Convolutional Neural Networks/2. What are convolutional neural networks.mp4
30.9 MB
32. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp4
30.8 MB
36. Artificial Neural Networks/9. Business Problem Description.mp4
30.7 MB
16. Logistic Regression/1. Logistic Regression Intuition.mp4
30.6 MB
39. Principal Component Analysis (PCA)/6. PCA in R - Step 2.mp4
30.4 MB
8. Polynomial Regression/10. Polynomial Regression in R - Step 4.mp4
29.9 MB
14. Regression Model Selection in R/1. Evaluating Regression Models Performance - Homework's Final Part.mp4
29.7 MB
6. Simple Linear Regression/6. Simple Linear Regression in Python - Step 3.mp4
29.6 MB
16. Logistic Regression/12. Logistic Regression in R - Step 3.mp4
28.8 MB
14. Regression Model Selection in R/2. Interpreting Linear Regression Coefficients.mp4
28.7 MB
40. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp4
28.3 MB
36. Artificial Neural Networks/5. How do Neural Networks learn.mp4
27.9 MB
10. Decision Tree Regression/4. Decision Tree Regression in Python - Step 2.srt
27.5 MB
10. Decision Tree Regression/4. Decision Tree Regression in Python - Step 2.mp4
27.5 MB
26. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp4
26.9 MB
22. Random Forest Classification/1. Random Forest Classification Intuition.mp4
26.9 MB
10. Decision Tree Regression/1. Decision Tree Regression Intuition.mp4
26.6 MB
30. Eclat/4. Eclat in R.mp4
26.5 MB
6. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp4
26.1 MB
36. Artificial Neural Networks/4. How do Neural Networks work.mp4
24.7 MB
7. Multiple Linear Regression/15. Multiple Linear Regression in R - Step 1.mp4
24.6 MB
1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).mp4
24.3 MB
27. Hierarchical Clustering/4. Hierarchical Clustering Using Dendrograms.mp4
23.9 MB
34/3. Types of Natural Language Processing.mp4
23.6 MB
6. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.srt
23.5 MB
7. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4
23.0 MB
34/16. Natural Language Processing in R - Step 2.mp4
22.7 MB
21. Decision Tree Classification/1. Decision Tree Classification Intuition.mp4
22.7 MB
12. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp4
22.5 MB
8. Polynomial Regression/7. Polynomial Regression in R - Step 1.mp4
22.2 MB
24. Evaluating Classification Models Performance/4. CAP Curve.mp4
21.3 MB
18. Support Vector Machine (SVM)/2. SVM Intuition.mp4
20.9 MB
9. Support Vector Regression (SVR)/2. Heads-up on non-linear SVR.mp4
20.7 MB
10. Decision Tree Regression/5. Decision Tree Regression in Python - Step 3.mp4
20.4 MB
20. Naive Bayes/4. Naive Bayes Intuition (Extras).mp4
19.9 MB
36. Artificial Neural Networks/6. Gradient Descent.mp4
19.4 MB
36. Artificial Neural Networks/18. ANN in R - Step 2.mp4
19.1 MB
32. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp4
18.6 MB
16. Logistic Regression/16. R Classification Template.mp4
18.4 MB
27. Hierarchical Clustering/3. Hierarchical Clustering How Dendrograms Work.mp4
18.3 MB
34/22. Natural Language Processing in R - Step 8.mp4
18.1 MB
34/17. Natural Language Processing in R - Step 3.mp4
17.7 MB
36. Artificial Neural Networks/7. Stochastic Gradient Descent.mp4
17.6 MB
7. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 3.mp4
17.4 MB
27. Hierarchical Clustering/2. Hierarchical Clustering Intuition.mp4
17.3 MB
4. Data Preprocessing in R/5. Importing the Dataset.mp4
17.2 MB
34/20. Natural Language Processing in R - Step 6.mp4
16.9 MB
3. Data Preprocessing in Python/3. Importing the Libraries.mp4
16.7 MB
19. Kernel SVM/4. Types of Kernel Functions.mp4
16.5 MB
16. Logistic Regression/10. Logistic Regression in R - Step 1.mp4
16.5 MB
11. Random Forest Regression/1. Random Forest Regression Intuition.mp4
16.4 MB
19. Kernel SVM/2. Mapping to a higher dimension.mp4
16.2 MB
26. K-Means Clustering/2. K-Means Random Initialization Trap.mp4
16.1 MB
24. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp4
15.9 MB
16. Logistic Regression/11. Logistic Regression in R - Step 2.mp4
15.6 MB
36. Artificial Neural Networks/3. The Activation Function.mp4
15.5 MB
1. Welcome to the course!/5. Why Machine Learning is the Future.mp4
15.2 MB
37. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp4
14.8 MB
33. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp4
14.8 MB
27. Hierarchical Clustering/10. Hierarchical Clustering in R - Step 2.mp4
14.6 MB
7. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 3.mp4
14.5 MB
27. Hierarchical Clustering/13. Hierarchical Clustering in R - Step 5.mp4
14.3 MB
20. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp4
13.9 MB
24. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp4
13.6 MB
34/2. NLP Intuition.mp4
13.3 MB
7. Multiple Linear Regression/1. Dataset + Business Problem Description.mp4
13.2 MB
4. Data Preprocessing in R/4. Dataset Description.mp4
12.4 MB
16. Logistic Regression/13. Logistic Regression in R - Step 4.mp4
12.3 MB
6. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp4
12.1 MB
6. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp4
12.0 MB
36. Artificial Neural Networks/8. Backpropagation.mp4
11.5 MB
30. Eclat/1. Eclat Intuition.mp4
11.2 MB
6. Simple Linear Regression/1. Simple Linear Regression Intuition - Step 1.mp4
11.0 MB
17. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp4
11.0 MB
27. Hierarchical Clustering/12. Hierarchical Clustering in R - Step 4.mp4
10.7 MB
27. Hierarchical Clustering/11. Hierarchical Clustering in R - Step 3.mp4
10.4 MB
1. Welcome to the course!/1. Applications of Machine Learning.mp4
10.3 MB
4. Data Preprocessing in R/2. Getting Started.mp4
10.3 MB
12. Evaluating Regression Models Performance/1. R-Squared Intuition.mp4
10.3 MB
34/21. Natural Language Processing in R - Step 7.mp4
10.1 MB
33. Thompson Sampling/10. Thompson Sampling in R - Step 2.mp4
10.0 MB
32. Upper Confidence Bound (UCB)/14. Upper Confidence Bound in R - Step 4.mp4
10.0 MB
8. Polynomial Regression/1. Polynomial Regression Intuition.mp4
9.9 MB
24. Evaluating Classification Models Performance/2. Confusion Matrix.mp4
9.3 MB
27. Hierarchical Clustering/9. Hierarchical Clustering in R - Step 1.srt
9.0 MB
27. Hierarchical Clustering/9. Hierarchical Clustering in R - Step 1.mp4
9.0 MB
34/18. Natural Language Processing in R - Step 4.mp4
8.6 MB
37. Convolutional Neural Networks/8. Summary.mp4
8.3 MB
19. Kernel SVM/1. Kernel SVM Intuition.mp4
6.7 MB
6. Simple Linear Regression/2. Simple Linear Regression Intuition - Step 2.mp4
6.3 MB
37. Convolutional Neural Networks/1. Plan of attack.mp4
6.2 MB
34/19. Natural Language Processing in R - Step 5.mp4
6.1 MB
7. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 4.mp4
5.6 MB
1. Welcome to the course!/8.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
11. Random Forest Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
17. K-Nearest Neighbors (K-NN)/2.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
19. Kernel SVM/6.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
20. Naive Bayes/5.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
21. Decision Tree Classification/2.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
22. Random Forest Classification/2.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
27. Hierarchical Clustering/5.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
3. Data Preprocessing in Python/1.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
30. Eclat/2.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
32. Upper Confidence Bound (UCB)/3.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
34/6.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
36. Artificial Neural Networks/10.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
41. Kernel PCA/1.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
43. Model Selection/1.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
44. XGBoost/1.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
6. Simple Linear Regression/3.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
10. Decision Tree Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
16. Logistic Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
18. Support Vector Machine (SVM)/3.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
26. K-Means Clustering/4.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
29. Apriori/2.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
33. Thompson Sampling/3.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
39. Principal Component Analysis (PCA)/2.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
40. Linear Discriminant Analysis (LDA)/2.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
7. Multiple Linear Regression/8.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
8. Polynomial Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
9. Support Vector Regression (SVR)/3.1 Machine Learning A-Z (Codes and Datasets).zip
5.5 MB
36. Artificial Neural Networks/1. Plan of attack.mp4
5.0 MB
24. Evaluating Classification Models Performance/3. Accuracy Paradox.mp4
4.4 MB
37. Convolutional Neural Networks/6. Step 3 - Flattening.mp4
3.4 MB
1. Welcome to the course!/7.1 Machine_Learning_A_Z_Q_A.pdf
2.4 MB
7. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 2.mp4
2.1 MB
7. Multiple Linear Regression/2. Multiple Linear Regression Intuition - Step 1.mp4
2.1 MB
13. Regression Model Selection in Python/4.1 Regression_Bonus.zip
373.2 kB
14. Regression Model Selection in R/3.1 Regression_Bonus.zip
373.2 kB
13. Regression Model Selection in Python/1.1 Machine Learning A-Z (Model Selection).zip
163.8 kB
23. Classification Model Selection in Python/1.1 Machine Learning A-Z (Model Selection).zip
163.8 kB
30. Eclat/4.1 Eclat.zip
49.7 kB
37. Convolutional Neural Networks/16. CNN in Python - FINAL DEMO!.srt
39.7 kB
43. Model Selection/3. Grid Search in Python.srt
35.4 kB
20. Naive Bayes/1. Bayes Theorem.srt
35.3 kB
23. Classification Model Selection in Python/2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!.srt
35.3 kB
22. Random Forest Classification/4. Random Forest Classification in R.srt
33.2 kB
10. Decision Tree Regression/7. Decision Tree Regression in R.srt
32.9 kB
29. Apriori/6. Apriori in Python - Step 4.srt
32.0 kB
29. Apriori/9. Apriori in R - Step 3.srt
31.9 kB
29. Apriori/7. Apriori in R - Step 1.srt
31.8 kB
36. Artificial Neural Networks/13. ANN in Python - Step 2.srt
31.7 kB
8. Polynomial Regression/9. Polynomial Regression in R - Step 3.srt
31.6 kB
41. Kernel PCA/3. Kernel PCA in R.srt
31.5 kB
17. K-Nearest Neighbors (K-NN)/3. K-NN in Python.srt
31.5 kB
3. Data Preprocessing in Python/9. Feature Scaling.srt
31.0 kB
13. Regression Model Selection in Python/2. Preparation of the Regression Code Templates.srt
30.9 kB
40. Linear Discriminant Analysis (LDA)/4. LDA in R.srt
30.4 kB
24. Evaluating Classification Models Performance/6.1 Classification_Pros_Cons.pdf
30.0 kB
21. Decision Tree Classification/4. Decision Tree Classification in R.srt
29.8 kB
16. Logistic Regression/15. Logistic Regression in R - Step 5.srt
29.8 kB
26. K-Means Clustering/9. K-Means Clustering in Python - Step 5.srt
29.8 kB
37. Convolutional Neural Networks/12. CNN in Python - Step 2.srt
29.7 kB
37. Convolutional Neural Networks/13. CNN in Python - Step 3.srt
29.6 kB
43. Model Selection/2. k-Fold Cross Validation in Python.srt
29.3 kB
37. Convolutional Neural Networks/7. Step 4 - Full Connection.srt
29.3 kB
34/5. Bag-Of-Words Model.srt
29.0 kB
1. Welcome to the course!/9. Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder.srt
28.9 kB
11. Random Forest Regression/4. Random Forest Regression in R.srt
28.8 kB
43. Model Selection/4. k-Fold Cross Validation in R.srt
28.6 kB
33. Thompson Sampling/9. Thompson Sampling in R - Step 1.srt
28.5 kB
33. Thompson Sampling/1. Thompson Sampling Intuition.srt
28.2 kB
7. Multiple Linear Regression/18. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.srt
28.1 kB
36. Artificial Neural Networks/17. ANN in R - Step 1.srt
27.4 kB
39. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.srt
27.1 kB
29. Apriori/4. Apriori in Python - Step 2.srt
27.0 kB
34/11. Natural Language Processing in Python - Step 5.srt
27.0 kB
34/24. Natural Language Processing in R - Step 10.srt
26.9 kB
27. Hierarchical Clustering/7. Hierarchical Clustering in Python - Step 2.srt
26.8 kB
44. XGBoost/4. XGBoost in R.srt
26.6 kB
29. Apriori/1. Apriori Intuition.srt
26.5 kB
27. Hierarchical Clustering/15.1 Clustering-Pros-Cons.pdf
26.4 kB
36. Artificial Neural Networks/16. ANN in Python - Step 5.srt
26.4 kB
19. Kernel SVM/8. Kernel SVM in R.srt
26.1 kB
32. Upper Confidence Bound (UCB)/13. Upper Confidence Bound in R - Step 3.srt
25.9 kB
37. Convolutional Neural Networks/9. Softmax & Cross-Entropy.srt
25.9 kB
32. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.srt
25.7 kB
36. Artificial Neural Networks/2. The Neuron.srt
25.6 kB
3. Data Preprocessing in Python/4. Importing the Dataset.srt
24.7 kB
34/15. Natural Language Processing in R - Step 1.srt
24.6 kB
16. Logistic Regression/1. Logistic Regression Intuition.srt
24.5 kB
6. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.srt
24.5 kB
26. K-Means Clustering/7. K-Means Clustering in Python - Step 3.srt
24.2 kB
7. Multiple Linear Regression/7. Multiple Linear Regression Intuition - Step 5.srt
24.1 kB
36. Artificial Neural Networks/14. ANN in Python - Step 3.srt
24.0 kB
40. Linear Discriminant Analysis (LDA)/3. LDA in Python.srt
24.0 kB
17. K-Nearest Neighbors (K-NN)/4. K-NN in R.srt
23.9 kB
26. K-Means Clustering/1. K-Means Clustering Intuition.srt
23.9 kB
20. Naive Bayes/2. Naive Bayes Intuition.srt
23.9 kB
37. Convolutional Neural Networks/3. Step 1 - Convolution Operation.srt
23.8 kB
44. XGBoost/2. XGBoost in Python.srt
23.6 kB
29. Apriori/8. Apriori in R - Step 2.srt
23.6 kB
16. Logistic Regression/9. Logistic Regression in Python - Step 7.srt
23.1 kB
37. Convolutional Neural Networks/15. CNN in Python - Step 5.srt
23.0 kB
32. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.srt
22.8 kB
9. Support Vector Regression (SVR)/8. SVR in Python - Step 5.srt
22.8 kB
21. Decision Tree Classification/3. Decision Tree Classification in Python.srt
22.8 kB
20. Naive Bayes/6. Naive Bayes in Python.srt
22.8 kB
32. Upper Confidence Bound (UCB)/12. Upper Confidence Bound in R - Step 2.srt
22.7 kB
9. Support Vector Regression (SVR)/5. SVR in Python - Step 2.srt
22.7 kB
37. Convolutional Neural Networks/2. What are convolutional neural networks.srt
22.6 kB
3. Data Preprocessing in Python/7. Encoding Categorical Data.srt
22.5 kB
32. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.srt
22.4 kB
16. Logistic Regression/4. Logistic Regression in Python - Step 2.srt
21.9 kB
11. Random Forest Regression/3. Random Forest Regression in Python.srt
21.6 kB
37. Convolutional Neural Networks/5. Step 2 - Pooling.srt
21.5 kB
43. Model Selection/5. Grid Search in R.srt
21.4 kB
8. Polynomial Regression/3. Polynomial Regression in Python - Step 1.srt
21.3 kB
36. Artificial Neural Networks/20. ANN in R - Step 4 (Last step).srt
21.2 kB
32. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.srt
21.1 kB
32. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 1.srt
21.0 kB
33. Thompson Sampling/6. Thompson Sampling in Python - Step 3.srt
21.0 kB
19. Kernel SVM/7. Kernel SVM in Python.srt
20.9 kB
36. Artificial Neural Networks/15. ANN in Python - Step 4.srt
20.7 kB
3. Data Preprocessing in Python/8. Splitting the dataset into the Training set and Test set.srt
20.5 kB
7. Multiple Linear Regression/12. Multiple Linear Regression in Python - Step 4.srt
20.5 kB
8. Polynomial Regression/5. Polynomial Regression in Python - Step 3.srt
20.4 kB
6. Simple Linear Regression/4. Simple Linear Regression in Python - Step 1.srt
20.2 kB
39. Principal Component Analysis (PCA)/7. PCA in R - Step 3.srt
20.2 kB
34/23. Natural Language Processing in R - Step 9.srt
20.1 kB
7. Multiple Linear Regression/6. Understanding the P-Value.srt
20.0 kB
26. K-Means Clustering/10. K-Means Clustering in R.srt
19.9 kB
6. Simple Linear Regression/7. Simple Linear Regression in Python - Step 4.srt
19.9 kB
29. Apriori/5. Apriori in Python - Step 3.srt
19.7 kB
34/9. Natural Language Processing in Python - Step 3.srt
19.6 kB
36. Artificial Neural Networks/4. How do Neural Networks work.srt
19.6 kB
36. Artificial Neural Networks/5. How do Neural Networks learn.srt
19.4 kB
36. Artificial Neural Networks/19. ANN in R - Step 3.srt
19.3 kB
30. Eclat/3. Eclat in Python.srt
19.3 kB
39. Principal Component Analysis (PCA)/5. PCA in R - Step 1.srt
19.1 kB
9. Support Vector Regression (SVR)/9. SVR in R.srt
19.1 kB
8. Polynomial Regression/11. R Regression Template.srt
19.1 kB
26. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.srt
18.9 kB
18. Support Vector Machine (SVM)/5. SVM in R.srt
18.8 kB
37. Convolutional Neural Networks/11. CNN in Python - Step 1.srt
18.7 kB
27. Hierarchical Clustering/8. Hierarchical Clustering in Python - Step 3.srt
18.6 kB
35. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.srt
18.6 kB
3. Data Preprocessing in Python/6. Taking care of Missing Data.srt
18.5 kB
33. Thompson Sampling/5. Thompson Sampling in Python - Step 2.srt
18.3 kB
8. Polynomial Regression/4. Polynomial Regression in Python - Step 2.srt
18.0 kB
27. Hierarchical Clustering/4. Hierarchical Clustering Using Dendrograms.srt
18.0 kB
41. Kernel PCA/2. Kernel PCA in Python.srt
17.9 kB
36. Artificial Neural Networks/11. ANN in Python - Step 1.srt
17.8 kB
10. Decision Tree Regression/1. Decision Tree Regression Intuition.srt
17.5 kB
39. Principal Component Analysis (PCA)/6. PCA in R - Step 2.srt
17.3 kB
34/10. Natural Language Processing in Python - Step 4.srt
17.2 kB
3. Data Preprocessing in Python/2. Getting Started.srt
17.1 kB
7. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.srt
17.0 kB
19. Kernel SVM/3. The Kernel Trick.srt
16.9 kB
24. Evaluating Classification Models Performance/4. CAP Curve.srt
16.6 kB
34/4. Classical vs Deep Learning Models.srt
16.5 kB
19. Kernel SVM/5. Non-Linear Kernel SVR (Advanced).srt
16.4 kB
20. Naive Bayes/4. Naive Bayes Intuition (Extras).srt
16.3 kB
30. Eclat/4. Eclat in R.srt
16.2 kB
18. Support Vector Machine (SVM)/2. SVM Intuition.srt
16.1 kB
10. Decision Tree Regression/6. Decision Tree Regression in Python - Step 4.srt
15.8 kB
8. Polynomial Regression/10. Polynomial Regression in R - Step 4.srt
15.8 kB
7. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 2.srt
15.8 kB
8. Polynomial Regression/8. Polynomial Regression in R - Step 2.srt
15.6 kB
34/12. Natural Language Processing in Python - Step 6.srt
15.4 kB
7. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.srt
15.2 kB
4. Data Preprocessing in R/8. Splitting the dataset into the Training set and Test set.srt
15.2 kB
27. Hierarchical Clustering/2. Hierarchical Clustering Intuition.srt
14.9 kB
16. Logistic Regression/3. Logistic Regression in Python - Step 1.srt
14.8 kB
12. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.srt
14.8 kB
27. Hierarchical Clustering/3. Hierarchical Clustering How Dendrograms Work.srt
14.7 kB
29. Apriori/3. Apriori in Python - Step 1.srt
14.6 kB
8. Polynomial Regression/7. Polynomial Regression in R - Step 1.srt
14.5 kB
36. Artificial Neural Networks/6. Gradient Descent.srt
14.4 kB
9. Support Vector Regression (SVR)/4. SVR in Python - Step 1.srt
14.3 kB
16. Logistic Regression/8. Logistic Regression in Python - Step 6.srt
14.0 kB
13. Regression Model Selection in Python/3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION!.srt
13.9 kB
14. Regression Model Selection in R/2. Interpreting Linear Regression Coefficients.srt
13.6 kB
10. Decision Tree Regression/3. Decision Tree Regression in Python - Step 1.srt
13.6 kB
7. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.srt
13.5 kB
4. Data Preprocessing in R/9. Feature Scaling.srt
13.4 kB
26. K-Means Clustering/2. K-Means Random Initialization Trap.srt
13.3 kB
14. Regression Model Selection in R/1. Evaluating Regression Models Performance - Homework's Final Part.srt
13.2 kB
26. K-Means Clustering/5. K-Means Clustering in Python - Step 1.srt
13.2 kB
34/16. Natural Language Processing in R - Step 2.srt
13.2 kB
21. Decision Tree Classification/1. Decision Tree Classification Intuition.srt
13.2 kB
8. Polynomial Regression/6. Polynomial Regression in Python - Step 4.srt
12.6 kB
36. Artificial Neural Networks/7. Stochastic Gradient Descent.srt
12.4 kB
36. Artificial Neural Networks/3. The Activation Function.srt
12.3 kB
9. Support Vector Regression (SVR)/7. SVR in Python - Step 4.srt
12.1 kB
7. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - Homework Solution.srt
12.1 kB
7. Multiple Linear Regression/15. Multiple Linear Regression in R - Step 1.srt
12.1 kB
6. Simple Linear Regression/5. Simple Linear Regression in Python - Step 2.srt
12.1 kB
32. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in Python - Step 7.srt
11.9 kB
9. Support Vector Regression (SVR)/1. SVR Intuition (Updated!).srt
11.9 kB
37. Convolutional Neural Networks/14. CNN in Python - Step 4.srt
11.7 kB
33. Thompson Sampling/7. Thompson Sampling in Python - Step 4.srt
11.6 kB
24. Evaluating Classification Models Performance/1. False Positives & False Negatives.srt
11.6 kB
32. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in Python - Step 6.srt
11.5 kB
16. Logistic Regression/6. Logistic Regression in Python - Step 4.srt
11.5 kB
33. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.srt
11.4 kB
34/7. Natural Language Processing in Python - Step 1.srt
11.4 kB
32. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.srt
11.3 kB
16. Logistic Regression/5. Logistic Regression in Python - Step 3.srt
11.1 kB
34/8. Natural Language Processing in Python - Step 2.srt
11.0 kB
7. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 3.srt
11.0 kB
27. Hierarchical Clustering/6. Hierarchical Clustering in Python - Step 1.srt
10.8 kB
19. Kernel SVM/2. Mapping to a higher dimension.srt
10.8 kB
11. Random Forest Regression/1. Random Forest Regression Intuition.srt
10.5 kB
34/17. Natural Language Processing in R - Step 3.srt
10.4 kB
36. Artificial Neural Networks/18. ANN in R - Step 2.srt
10.4 kB
33. Thompson Sampling/4. Thompson Sampling in Python - Step 1.srt
10.0 kB
9. Support Vector Regression (SVR)/6. SVR in Python - Step 3.srt
9.9 kB
16. Logistic Regression/7. Logistic Regression in Python - Step 5.srt
9.7 kB
20. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).srt
9.7 kB
32. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in Python - Step 5.srt
9.7 kB
26. K-Means Clustering/8. K-Means Clustering in Python - Step 4.srt
9.6 kB
24. Evaluating Classification Models Performance/5. CAP Curve Analysis.srt
9.5 kB
1. Welcome to the course!/5. Why Machine Learning is the Future.srt
9.5 kB
37. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.srt
9.4 kB
39. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.srt
9.4 kB
1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).srt
9.4 kB
4. Data Preprocessing in R/6. Taking care of Missing Data.srt
9.3 kB
16. Logistic Regression/10. Logistic Regression in R - Step 1.srt
9.1 kB
4. Data Preprocessing in R/7. Encoding Categorical Data.srt
8.7 kB
34/20. Natural Language Processing in R - Step 6.srt
8.6 kB
4. Data Preprocessing in R/10. Data Preprocessing Template.srt
8.5 kB
6. Simple Linear Regression/1. Simple Linear Regression Intuition - Step 1.srt
8.5 kB
18. Support Vector Machine (SVM)/5.1 SVM.zip
8.5 kB
27. Hierarchical Clustering/10. Hierarchical Clustering in R - Step 2.srt
8.3 kB
30. Eclat/1. Eclat Intuition.srt
8.3 kB
17. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.srt
8.2 kB
34/22. Natural Language Processing in R - Step 8.srt
8.2 kB
8. Polynomial Regression/1. Polynomial Regression Intuition.srt
8.0 kB
6. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.srt
7.9 kB
24. Evaluating Classification Models Performance/2. Confusion Matrix.srt
7.7 kB
16. Logistic Regression/12. Logistic Regression in R - Step 3.srt
7.6 kB
6. Simple Linear Regression/6. Simple Linear Regression in Python - Step 3.srt
7.5 kB
36. Artificial Neural Networks/9. Business Problem Description.srt
7.5 kB
12. Evaluating Regression Models Performance/1. R-Squared Intuition.srt
7.3 kB
36. Artificial Neural Networks/8. Backpropagation.srt
7.3 kB
7. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 3.srt
7.2 kB
22. Random Forest Classification/1. Random Forest Classification Intuition.srt
7.2 kB
16. Logistic Regression/16. R Classification Template.srt
6.9 kB
32. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.srt
6.5 kB
37. Convolutional Neural Networks/8. Summary.srt
6.2 kB
34/3. Types of Natural Language Processing.srt
6.1 kB
9. Support Vector Regression (SVR)/2. Heads-up on non-linear SVR.srt
6.1 kB
7. Multiple Linear Regression/1. Dataset + Business Problem Description.srt
5.8 kB
3. Data Preprocessing in Python/3. Importing the Libraries.srt
5.8 kB
34/21. Natural Language Processing in R - Step 7.srt
5.7 kB
6. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.srt
5.6 kB
1. Welcome to the course!/1. Applications of Machine Learning.srt
5.4 kB
33. Thompson Sampling/10. Thompson Sampling in R - Step 2.srt
5.4 kB
37. Convolutional Neural Networks/1. Plan of attack.srt
5.4 kB
40. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.srt
5.2 kB
39. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.srt
5.2 kB
19. Kernel SVM/4. Types of Kernel Functions.srt
5.1 kB
10. Decision Tree Regression/5. Decision Tree Regression in Python - Step 3.srt
5.0 kB
45. Bonus Lectures/1. YOUR SPECIAL BONUS.html
4.8 kB
27. Hierarchical Clustering/11. Hierarchical Clustering in R - Step 3.srt
4.8 kB
34/18. Natural Language Processing in R - Step 4.srt
4.8 kB
34/2. NLP Intuition.srt
4.7 kB
4. Data Preprocessing in R/5. Importing the Dataset.srt
4.6 kB
32. Upper Confidence Bound (UCB)/14. Upper Confidence Bound in R - Step 4.srt
4.5 kB
19. Kernel SVM/1. Kernel SVM Intuition.srt
4.5 kB
16. Logistic Regression/11. Logistic Regression in R - Step 2.srt
4.5 kB
6. Simple Linear Regression/2. Simple Linear Regression Intuition - Step 2.srt
4.4 kB
27. Hierarchical Clustering/13. Hierarchical Clustering in R - Step 5.srt
4.1 kB
36. Artificial Neural Networks/1. Plan of attack.srt
4.1 kB
16. Logistic Regression/13. Logistic Regression in R - Step 4.srt
4.1 kB
27. Hierarchical Clustering/12. Hierarchical Clustering in R - Step 4.srt
3.9 kB
1. Welcome to the course!/14. Your Shortcut To Becoming A Better Data Scientist!.html
3.8 kB
7. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 4.srt
3.6 kB
7. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination.html
3.6 kB
24. Evaluating Classification Models Performance/6. Conclusion of Part 3 - Classification.html
3.4 kB
1. Welcome to the course!/6. Important notes, tips & tricks for this course.html
3.4 kB
34/19. Natural Language Processing in R - Step 5.srt
3.3 kB
24. Evaluating Classification Models Performance/3. Accuracy Paradox.srt
3.3 kB
4. Data Preprocessing in R/4. Dataset Description.srt
3.3 kB
1. Welcome to the course!/13. FAQBot!.html
3.1 kB
37. Convolutional Neural Networks/6. Step 3 - Flattening.srt
2.6 kB
4. Data Preprocessing in R/2. Getting Started.srt
2.5 kB
44. XGBoost/5. THANK YOU Bonus Video.srt
2.4 kB
33. Thompson Sampling/8. Additional Resource for this Section.html
2.3 kB
1. Welcome to the course!/8. GET ALL THE CODES AND DATASETS HERE!.html
1.9 kB
13. Regression Model Selection in Python/4. Conclusion of Part 2 - Regression.html
1.8 kB
14. Regression Model Selection in R/3. Conclusion of Part 2 - Regression.html
1.8 kB
34/1. Welcome to Part 7 - Natural Language Processing.html
1.7 kB
7. Multiple Linear Regression/2. Multiple Linear Regression Intuition - Step 1.srt
1.6 kB
31. -------------------- Part 6 Reinforcement Learning --------------------/1. Welcome to Part 6 - Reinforcement Learning.html
1.6 kB
1. Welcome to the course!/7. This PDF resource will help you a lot!.html
1.5 kB
3. Data Preprocessing in Python/5. For Python learners, summary of Object-oriented programming classes & objects.html
1.5 kB
7. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 2.srt
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34/25. Homework Challenge.html
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1. Welcome to the course!/2. BONUS #1 Learning Paths.html
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34/14. Homework Challenge.html
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16. Logistic Regression/14. Warning - Update.html
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38. -------------------- Part 9 Dimensionality Reduction --------------------/1. Welcome to Part 9 - Dimensionality Reduction.html
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7. Multiple Linear Regression/14. Multiple Linear Regression in Python - BONUS.html
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44. XGBoost/3. Model Selection and Boosting BONUS.html
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6. Simple Linear Regression/8. Simple Linear Regression in Python - BONUS.html
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1. Welcome to the course!/11. BONUS Meet your instructors.html
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34/13. Natural Language Processing in Python - BONUS.html
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36. Artificial Neural Networks/21. Deep Learning BONUS #1.html
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23. Classification Model Selection in Python/1. Make sure you have this Model Selection folder ready.html
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13. Regression Model Selection in Python/1. Make sure you have this Model Selection folder ready.html
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37. Convolutional Neural Networks/17. Deep Learning BONUS #2.html
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34/26. BONUS NLP BERT.html
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42/1. Welcome to Part 10 - Model Selection & Boosting.html
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5. -------------------- Part 2 Regression --------------------/1. Welcome to Part 2 - Regression.html
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35. -------------------- Part 8 Deep Learning --------------------/1. Welcome to Part 8 - Deep Learning.html
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15. -------------------- Part 3 Classification --------------------/1. Welcome to Part 3 - Classification.html
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16. Logistic Regression/17. Machine Learning Regression and Classification BONUS.html
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37. Convolutional Neural Networks/10. Make sure you have your dataset ready.html
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10. Decision Tree Regression/2. Make sure you have your Machine Learning A-Z folder ready.html
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11. Random Forest Regression/2. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
16. Logistic Regression/2. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
17. K-Nearest Neighbors (K-NN)/2. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
18. Support Vector Machine (SVM)/3. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
19. Kernel SVM/6. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
20. Naive Bayes/5. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
21. Decision Tree Classification/2. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
22. Random Forest Classification/2. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
26. K-Means Clustering/4. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
27. Hierarchical Clustering/5. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
29. Apriori/2. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
30. Eclat/2. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
32. Upper Confidence Bound (UCB)/3. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
33. Thompson Sampling/3. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
34/6. Make sure you have your Machine Learning A-Z folder ready.html
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36. Artificial Neural Networks/10. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
39. Principal Component Analysis (PCA)/2. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
40. Linear Discriminant Analysis (LDA)/2. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
41. Kernel PCA/1. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
43. Model Selection/1. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
44. XGBoost/1. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
6. Simple Linear Regression/3. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
7. Multiple Linear Regression/8. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
8. Polynomial Regression/2. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
9. Support Vector Regression (SVR)/3. Make sure you have your Machine Learning A-Z folder ready.html
776 Bytes
25. -------------------- Part 4 Clustering --------------------/1. Welcome to Part 4 - Clustering.html
734 Bytes
7. Multiple Linear Regression/20. Multiple Linear Regression in R - Automatic Backward Elimination.html
726 Bytes
3. Data Preprocessing in Python/1. Make sure you have your Machine Learning A-Z folder ready.html
664 Bytes
16. Logistic Regression/19. BONUS Logistic Regression Practical Case Study.html
619 Bytes
4. Data Preprocessing in R/1. Welcome.html
608 Bytes
1. Welcome to the course!/12. Some Additional Resources.html
553 Bytes
36. Artificial Neural Networks/22. BONUS ANN Case Study.html
544 Bytes
36. Artificial Neural Networks/12. Check out our free course on ANN for Regression.html
533 Bytes
2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.html
531 Bytes
27. Hierarchical Clustering/15. Conclusion of Part 4 - Clustering.html
516 Bytes
1. Welcome to the course!/4. BONUS #3 Regression Types.html
511 Bytes
1. Welcome to the course!/3. BONUS #2 ML vs. DL vs. AI - What’s the Difference.html
499 Bytes
4. Data Preprocessing in R/3. Make sure you have your dataset ready.html
465 Bytes
28. -------------------- Part 5 Association Rule Learning --------------------/1. Welcome to Part 5 - Association Rule Learning.html
425 Bytes
[Tutorialsplanet.NET].url
128 Bytes
16. Logistic Regression/18. Logistic Regression.html
125 Bytes
18. Support Vector Machine (SVM)/1. K-Nearest Neighbor.html
125 Bytes
27. Hierarchical Clustering/1. K-Means Clustering.html
125 Bytes
27. Hierarchical Clustering/14. Hierarchical Clustering.html
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6. Simple Linear Regression/13. Simple Linear Regression.html
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7. Multiple Linear Regression/21. Multiple Linear Regression.html
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