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[FreeCourseSite.com] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science
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文件列表
1. Welcome to the course!/6.1 Machine_Learning_A-Z_New.zip.zip
239.5 MB
36. Kernel PCA/3. Kernel PCA in R.mp4
59.3 MB
1. Welcome to the course!/7. Updates on Udemy Reviews.vtt
55.5 MB
1. Welcome to the course!/7. Updates on Udemy Reviews.mp4
55.5 MB
39. XGBoost/5. THANK YOU bonus video.mp4
54.8 MB
12. Logistic Regression/13. Logistic Regression in R - Step 5.mp4
54.2 MB
35. Linear Discriminant Analysis (LDA)/4. LDA in R.mp4
53.8 MB
17. Decision Tree Classification/4. Decision Tree Classification in R.mp4
53.7 MB
18. Random Forest Classification/4. Random Forest Classification in R.mp4
51.8 MB
31. Artificial Neural Networks/13. ANN in Python - Step 2.mp4
50.4 MB
39. XGBoost/4. XGBoost in R.mp4
49.6 MB
27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.mp4
49.5 MB
18. Random Forest Classification/3. Random Forest Classification in Python.mp4
49.4 MB
32. Convolutional Neural Networks/20. CNN in Python - Step 9.mp4
49.1 MB
7. Support Vector Regression (SVR)/2. SVR Intuition.mp4
48.9 MB
7. Support Vector Regression (SVR)/3. SVR in Python.mp4
48.4 MB
35. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4
47.6 MB
8. Decision Tree Regression/4. Decision Tree Regression in R.mp4
46.5 MB
16. Naive Bayes/1. Bayes Theorem.mp4
46.0 MB
24. Apriori/5. Apriori in R - Step 3.mp4
46.0 MB
38. Model Selection/3. k-Fold Cross Validation in R.mp4
45.8 MB
6. Polynomial Regression/10. Polynomial Regression in R - Step 3.mp4
45.4 MB
28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp4
45.2 MB
6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.vtt
45.1 MB
6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp4
45.1 MB
24. Apriori/3. Apriori in R - Step 1.vtt
45.0 MB
24. Apriori/3. Apriori in R - Step 1.mp4
45.0 MB
32. Convolutional Neural Networks/7. Step 4 - Full Connection.mp4
44.8 MB
12. Logistic Regression/7. Logistic Regression in Python - Step 5.mp4
44.6 MB
15. Kernel SVM/6. Kernel SVM in Python.mp4
43.6 MB
13. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp4
43.4 MB
29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.mp4
43.2 MB
27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp4
43.1 MB
28. Thompson Sampling/6. Thompson Sampling in R - Step 1.mp4
42.9 MB
2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.mp4
42.8 MB
15. Kernel SVM/7. Kernel SVM in R.mp4
42.4 MB
29. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.mp4
42.3 MB
29. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.vtt
42.3 MB
9. Random Forest Regression/4. Random Forest Regression in R.mp4
42.3 MB
32. Convolutional Neural Networks/5. Step 2 - Pooling.mp4
42.2 MB
21. K-Means Clustering/5. K-Means Clustering in Python.mp4
41.7 MB
5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4
41.7 MB
5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp4
41.5 MB
29. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 8.mp4
41.4 MB
9. Random Forest Regression/3. Random Forest Regression in Python.mp4
41.4 MB
2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.mp4
40.9 MB
31. Artificial Neural Networks/22. ANN in R - Step 1.mp4
40.4 MB
38. Model Selection/4. Grid Search in Python - Step 1.mp4
40.1 MB
24. Apriori/6. Apriori in Python - Step 1.mp4
39.8 MB
4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp4
39.2 MB
16. Naive Bayes/7. Naive Bayes in R.mp4
39.1 MB
28. Thompson Sampling/1. Thompson Sampling Intuition.mp4
39.1 MB
34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.vtt
38.5 MB
34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.mp4
38.5 MB
38. Model Selection/6. Grid Search in R.mp4
37.3 MB
27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp4
37.2 MB
13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp4
36.9 MB
29. -------------------- Part 7 Natural Language Processing --------------------/4. Natural Language Processing in Python - Step 1.mp4
36.9 MB
24. Apriori/1. Apriori Intuition.mp4
36.7 MB
2. -------------------- Part 1 Data Preprocessing --------------------/10. Feature Scaling.mp4
36.3 MB
8. Decision Tree Regression/3. Decision Tree Regression in Python.mp4
35.2 MB
31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).mp4
35.1 MB
36. Kernel PCA/2. Kernel PCA in Python.mp4
35.0 MB
32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp4
34.8 MB
38. Model Selection/2. k-Fold Cross Validation in Python.mp4
34.4 MB
5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp4
34.2 MB
14. Support Vector Machine (SVM)/4. SVM in R.mp4
33.8 MB
2. -------------------- Part 1 Data Preprocessing --------------------/6. Missing Data.mp4
33.7 MB
34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp4
33.7 MB
39. XGBoost/3. XGBoost in Python - Step 2.vtt
33.6 MB
39. XGBoost/3. XGBoost in Python - Step 2.mp4
33.5 MB
34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp4
33.5 MB
27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp4
33.1 MB
30. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.mp4
32.8 MB
14. Support Vector Machine (SVM)/3. SVM in Python.mp4
32.7 MB
32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp4
32.5 MB
4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.mp4
32.3 MB
34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.mp4
32.1 MB
24. Apriori/4. Apriori in R - Step 2.mp4
32.0 MB
27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp4
31.7 MB
31. Artificial Neural Networks/2. The Neuron.mp4
31.3 MB
17. Decision Tree Classification/3. Decision Tree Classification in Python.mp4
31.2 MB
29. -------------------- Part 7 Natural Language Processing --------------------/2. Natural Language Processing Intuition.mp4
31.1 MB
31. Artificial Neural Networks/16. ANN in Python - Step 5.mp4
31.0 MB
38. Model Selection/5. Grid Search in Python - Step 2.mp4
31.0 MB
24. Apriori/7. Apriori in Python - Step 2.mp4
31.0 MB
32. Convolutional Neural Networks/2. What are convolutional neural networks.mp4
30.9 MB
27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp4
30.8 MB
27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.vtt
30.8 MB
31. Artificial Neural Networks/12. ANN in Python - Step 1.mp4
30.7 MB
15. Kernel SVM/3. The Kernel Trick.mp4
30.7 MB
12. Logistic Regression/1. Logistic Regression Intuition.mp4
30.6 MB
34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.mp4
30.4 MB
27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.mp4
30.4 MB
21. K-Means Clustering/6. K-Means Clustering in R.mp4
30.4 MB
29. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.mp4
30.4 MB
31. Artificial Neural Networks/24. ANN in R - Step 3.mp4
30.3 MB
5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.mp4
30.2 MB
27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.mp4
29.4 MB
16. Naive Bayes/2. Naive Bayes Intuition.mp4
29.1 MB
6. Polynomial Regression/7. Python Regression Template.mp4
28.8 MB
32. Convolutional Neural Networks/15. CNN in Python - Step 4.mp4
28.5 MB
5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4
28.5 MB
6. Polynomial Regression/4. Polynomial Regression in Python - Step 2.mp4
28.4 MB
24. Apriori/8. Apriori in Python - Step 3.vtt
28.3 MB
35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp4
28.3 MB
24. Apriori/8. Apriori in Python - Step 3.mp4
28.3 MB
21. K-Means Clustering/1. K-Means Clustering Intuition.mp4
28.2 MB
31. Artificial Neural Networks/5. How do Neural Networks learn.mp4
27.8 MB
5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.mp4
27.2 MB
7. Support Vector Regression (SVR)/4. SVR in R.mp4
27.1 MB
34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.mp4
26.7 MB
6. Polynomial Regression/12. R Regression Template.mp4
26.6 MB
32. Convolutional Neural Networks/12. CNN in Python - Step 1.mp4
26.1 MB
6. Polynomial Regression/3. Polynomial Regression in Python - Step 1.mp4
26.1 MB
10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.mp4
25.4 MB
29. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - Step 10.mp4
25.3 MB
29. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 4.mp4
25.2 MB
6. Polynomial Regression/9. Polynomial Regression in R - Step 2.mp4
25.0 MB
5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4
25.0 MB
31. Artificial Neural Networks/4. How do Neural Networks work.mp4
24.7 MB
16. Naive Bayes/6. Naive Bayes in Python.mp4
24.5 MB
2. -------------------- Part 1 Data Preprocessing --------------------/4. Importing the Dataset.mp4
24.4 MB
21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp4
24.3 MB
22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.mp4
23.9 MB
8. Decision Tree Regression/1. Decision Tree Regression Intuition.mp4
23.8 MB
6. Polynomial Regression/11. Polynomial Regression in R - Step 4.vtt
23.5 MB
6. Polynomial Regression/11. Polynomial Regression in R - Step 4.mp4
23.4 MB
34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp4
23.1 MB
29. -------------------- Part 7 Natural Language Processing --------------------/5. Natural Language Processing in Python - Step 2.mp4
23.0 MB
10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.mp4
23.0 MB
4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.mp4
22.8 MB
39. XGBoost/2. XGBoost in Python - Step 1.mp4
22.4 MB
2. -------------------- Part 1 Data Preprocessing --------------------/2. Get the dataset.mp4
22.2 MB
25. Eclat/3. Eclat in R.mp4
21.7 MB
32. Convolutional Neural Networks/21. CNN in Python - Step 10.mp4
21.6 MB
2. -------------------- Part 1 Data Preprocessing --------------------/11. And here is our Data Preprocessing Template!.mp4
20.6 MB
1. Welcome to the course!/8. Installing Python and Anaconda (Mac, Linux & Windows).mp4
20.5 MB
18. Random Forest Classification/1. Random Forest Classification Intuition.mp4
20.4 MB
10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp4
20.2 MB
16. Naive Bayes/4. Naive Bayes Intuition (Extras).mp4
19.9 MB
17. Decision Tree Classification/1. Decision Tree Classification Intuition.vtt
19.7 MB
17. Decision Tree Classification/1. Decision Tree Classification Intuition.mp4
19.7 MB
4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.mp4
19.7 MB
19. Evaluating Classification Models Performance/4. CAP Curve.mp4
19.6 MB
31. Artificial Neural Networks/6. Gradient Descent.mp4
19.4 MB
31. Artificial Neural Networks/19. ANN in Python - Step 8.vtt
19.1 MB
31. Artificial Neural Networks/19. ANN in Python - Step 8.mp4
19.1 MB
14. Support Vector Machine (SVM)/1. SVM Intuition.mp4
18.9 MB
5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.mp4
18.8 MB
6. Polynomial Regression/8. Polynomial Regression in R - Step 1.mp4
18.6 MB
1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).mp4
18.4 MB
29. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.mp4
18.3 MB
22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.mp4
18.3 MB
5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4
18.1 MB
29. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 7.mp4
17.9 MB
31. Artificial Neural Networks/21. ANN in Python - Step 10.mp4
17.9 MB
31. Artificial Neural Networks/20. ANN in Python - Step 9.mp4
17.7 MB
31. Artificial Neural Networks/7. Stochastic Gradient Descent.mp4
17.6 MB
22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.mp4
17.3 MB
22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.vtt
17.3 MB
31. Artificial Neural Networks/10. Business Problem Description.mp4
17.2 MB
4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.mp4
16.4 MB
21. K-Means Clustering/2. K-Means Random Initialization Trap.mp4
16.1 MB
29. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 5.mp4
15.6 MB
31. Artificial Neural Networks/3. The Activation Function.mp4
15.5 MB
12. Logistic Regression/11. Logistic Regression in R - Step 3.mp4
15.3 MB
4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp4
15.1 MB
5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp4
15.0 MB
5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.mp4
15.0 MB
31. Artificial Neural Networks/23. ANN in R - Step 2.mp4
14.9 MB
32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp4
14.8 MB
28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp4
14.8 MB
29. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 9.mp4
14.7 MB
9. Random Forest Regression/1. Random Forest Regression Intuition.mp4
14.5 MB
15. Kernel SVM/2. Mapping to a higher dimension.mp4
14.4 MB
19. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp4
14.3 MB
6. Polynomial Regression/6. Polynomial Regression in Python - Step 4.vtt
14.2 MB
29. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.mp4
14.2 MB
6. Polynomial Regression/6. Polynomial Regression in Python - Step 4.mp4
14.2 MB
16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp4
13.9 MB
29. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.mp4
13.9 MB
32. Convolutional Neural Networks/18. CNN in Python - Step 7.mp4
13.6 MB
12. Logistic Regression/3. Logistic Regression in Python - Step 1.mp4
13.6 MB
1. Welcome to the course!/3. Why Machine Learning is the Future.mp4
13.4 MB
29. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.mp4
13.4 MB
22. Hierarchical Clustering/6. HC in Python - Step 2.mp4
13.3 MB
12. Logistic Regression/9. Logistic Regression in R - Step 1.mp4
13.2 MB
12. Logistic Regression/14. R Classification Template.mp4
13.1 MB
15. Kernel SVM/4. Types of Kernel Functions.mp4
12.9 MB
22. Hierarchical Clustering/7. HC in Python - Step 3.mp4
12.9 MB
12. Logistic Regression/8. Python Classification Template.mp4
12.7 MB
22. Hierarchical Clustering/8. HC in Python - Step 4.mp4
12.6 MB
14. Support Vector Machine (SVM)/2. How to get the dataset.mp4
12.3 MB
22. Hierarchical Clustering/4. How to get the dataset.mp4
12.3 MB
24. Apriori/2. How to get the dataset.mp4
12.3 MB
25. Eclat/2. How to get the dataset.mp4
12.3 MB
28. Thompson Sampling/3. How to get the dataset.mp4
12.3 MB
38. Model Selection/1. How to get the dataset.mp4
12.3 MB
38. Model Selection/1. How to get the dataset.vtt
12.3 MB
9. Random Forest Regression/2. How to get the dataset.mp4
12.3 MB
12. Logistic Regression/2. How to get the dataset.mp4
12.3 MB
13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.mp4
12.3 MB
15. Kernel SVM/5. How to get the dataset.mp4
12.3 MB
16. Naive Bayes/5. How to get the dataset.mp4
12.3 MB
17. Decision Tree Classification/2. How to get the dataset.mp4
12.3 MB
18. Random Forest Classification/2. How to get the dataset.mp4
12.3 MB
21. K-Means Clustering/4. How to get the dataset.mp4
12.3 MB
27. Upper Confidence Bound (UCB)/3. How to get the dataset.mp4
12.3 MB
29. -------------------- Part 7 Natural Language Processing --------------------/3. How to get the dataset.mp4
12.3 MB
31. Artificial Neural Networks/9. How to get the dataset.mp4
12.3 MB
32. Convolutional Neural Networks/10. How to get the dataset.mp4
12.3 MB
34. Principal Component Analysis (PCA)/2. How to get the dataset.mp4
12.3 MB
35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.mp4
12.3 MB
36. Kernel PCA/1. How to get the dataset.mp4
12.3 MB
39. XGBoost/1. How to get the dataset.mp4
12.3 MB
4. Simple Linear Regression/1. How to get the dataset.mp4
12.3 MB
5. Multiple Linear Regression/1. How to get the dataset.mp4
12.3 MB
6. Polynomial Regression/2. How to get the dataset.mp4
12.3 MB
7. Support Vector Regression (SVR)/1. How to get the dataset.mp4
12.3 MB
8. Decision Tree Regression/2. How to get the dataset.mp4
12.3 MB
19. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp4
12.1 MB
22. Hierarchical Clustering/11. HC in R - Step 2.mp4
11.7 MB
2. -------------------- Part 1 Data Preprocessing --------------------/3. Importing the Libraries.mp4
11.6 MB
31. Artificial Neural Networks/8. Backpropagation.mp4
11.5 MB
22. Hierarchical Clustering/5. HC in Python - Step 1.mp4
11.2 MB
25. Eclat/1. Eclat Intuition.mp4
11.2 MB
5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.mp4
10.9 MB
12. Logistic Regression/6. Logistic Regression in Python - Step 4.mp4
10.9 MB
5. Multiple Linear Regression/2. Dataset + Business Problem Description.mp4
10.5 MB
32. Convolutional Neural Networks/16. CNN in Python - Step 5.mp4
10.4 MB
32. Convolutional Neural Networks/17. CNN in Python - Step 6.mp4
10.2 MB
4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp4
10.0 MB
4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.mp4
9.9 MB
6. Polynomial Regression/1. Polynomial Regression Intuition.mp4
9.9 MB
13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp4
9.7 MB
27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp4
9.6 MB
31. Artificial Neural Networks/18. ANN in Python - Step 7.mp4
9.4 MB
10. Evaluating Regression Models Performance/1. R-Squared Intuition.mp4
9.3 MB
4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp4
9.1 MB
28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp4
8.8 MB
22. Hierarchical Clustering/9. HC in Python - Step 5.mp4
8.8 MB
31. Artificial Neural Networks/14. ANN in Python - Step 3.mp4
8.8 MB
12. Logistic Regression/4. Logistic Regression in Python - Step 2.mp4
8.6 MB
19. Evaluating Classification Models Performance/2. Confusion Matrix.mp4
8.6 MB
1. Welcome to the course!/1. Applications of Machine Learning.mp4
8.4 MB
32. Convolutional Neural Networks/8. Summary.mp4
8.3 MB
12. Logistic Regression/10. Logistic Regression in R - Step 2.mp4
8.2 MB
22. Hierarchical Clustering/12. HC in R - Step 3.mp4
8.2 MB
29. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.mp4
7.9 MB
28. Thompson Sampling/7. Thompson Sampling in R - Step 2.mp4
7.8 MB
22. Hierarchical Clustering/13. HC in R - Step 4.mp4
7.8 MB
27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.mp4
7.8 MB
22. Hierarchical Clustering/10. HC in R - Step 1.mp4
7.7 MB
5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp4
7.6 MB
31. Artificial Neural Networks/17. ANN in Python - Step 6.mp4
7.4 MB
12. Logistic Regression/12. Logistic Regression in R - Step 4.mp4
7.2 MB
22. Hierarchical Clustering/14. HC in R - Step 5.mp4
7.2 MB
32. Convolutional Neural Networks/19. CNN in Python - Step 8.mp4
7.1 MB
4. Simple Linear Regression/2. Dataset + Business Problem Description.mp4
7.0 MB
29. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.mp4
6.8 MB
29. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 6.mp4
6.8 MB
12. Logistic Regression/5. Logistic Regression in Python - Step 3.mp4
6.3 MB
32. Convolutional Neural Networks/1. Plan of attack.mp4
6.2 MB
31. Artificial Neural Networks/15. ANN in Python - Step 4.mp4
6.2 MB
32. Convolutional Neural Networks/13. CNN in Python - Step 2.mp4
6.1 MB
15. Kernel SVM/1. Kernel SVM Intuition.mp4
6.1 MB
4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.mp4
5.6 MB
31. Artificial Neural Networks/1. Plan of attack.mp4
5.0 MB
29. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.mp4
4.8 MB
5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.mp4
4.7 MB
19. Evaluating Classification Models Performance/3. Accuracy Paradox.mp4
4.0 MB
29. -------------------- Part 7 Natural Language Processing --------------------/6. Natural Language Processing in Python - Step 3.mp4
3.6 MB
29. -------------------- Part 7 Natural Language Processing --------------------/6. Natural Language Processing in Python - Step 3.vtt
3.6 MB
32. Convolutional Neural Networks/6. Step 3 - Flattening.mp4
3.4 MB
2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.mp4
3.1 MB
1. Welcome to the course!/5.1 Machine_Learning_A_Z_Q_A.pdf.pdf
2.4 MB
12. Logistic Regression/3. Logistic Regression in Python - Step 1.vtt
2.4 MB
32. Convolutional Neural Networks/14. CNN in Python - Step 3.mp4
2.3 MB
5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.mp4
1.9 MB
5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.mp4
1.9 MB
25. Eclat/3.1 Eclat.zip.zip
49.7 kB
16. Naive Bayes/1. Bayes Theorem.vtt
31.4 kB
18. Random Forest Classification/4. Random Forest Classification in R.vtt
29.5 kB
8. Decision Tree Regression/4. Decision Tree Regression in R.vtt
29.2 kB
24. Apriori/5. Apriori in R - Step 3.vtt
28.4 kB
7. Support Vector Regression (SVR)/3. SVR in Python.vtt
28.1 kB
6. Polynomial Regression/10. Polynomial Regression in R - Step 3.vtt
28.1 kB
18. Random Forest Classification/3. Random Forest Classification in Python.vtt
28.1 kB
36. Kernel PCA/3. Kernel PCA in R.vtt
27.3 kB
12. Logistic Regression/7. Logistic Regression in Python - Step 5.vtt
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12. Logistic Regression/13. Logistic Regression in R - Step 5.vtt
26.6 kB
17. Decision Tree Classification/4. Decision Tree Classification in R.vtt
26.5 kB
22. Hierarchical Clustering/16.1 Clustering-Pros-Cons.pdf.pdf
26.4 kB
35. Linear Discriminant Analysis (LDA)/4. LDA in R.vtt
26.2 kB
32. Convolutional Neural Networks/20. CNN in Python - Step 9.vtt
26.1 kB
21. K-Means Clustering/5. K-Means Clustering in Python.vtt
25.8 kB
28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.vtt
25.8 kB
9. Random Forest Regression/4. Random Forest Regression in R.vtt
25.7 kB
32. Convolutional Neural Networks/7. Step 4 - Full Connection.vtt
25.7 kB
15. Kernel SVM/6. Kernel SVM in Python.vtt
25.6 kB
24. Apriori/6. Apriori in Python - Step 1.vtt
25.5 kB
31. Artificial Neural Networks/13. ANN in Python - Step 2.vtt
25.4 kB
5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.vtt
25.2 kB
9. Random Forest Regression/3. Random Forest Regression in Python.vtt
25.0 kB
28. Thompson Sampling/6. Thompson Sampling in R - Step 1.vtt
24.9 kB
38. Model Selection/3. k-Fold Cross Validation in R.vtt
24.8 kB
28. Thompson Sampling/1. Thompson Sampling Intuition.vtt
24.7 kB
2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.vtt
24.5 kB
2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.vtt
24.4 kB
27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.vtt
23.9 kB
35. Linear Discriminant Analysis (LDA)/3. LDA in Python.vtt
23.6 kB
31. Artificial Neural Networks/22. ANN in R - Step 1.vtt
23.6 kB
29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.vtt
23.4 kB
15. Kernel SVM/7. Kernel SVM in R.vtt
23.2 kB
24. Apriori/1. Apriori Intuition.vtt
23.1 kB
39. XGBoost/4. XGBoost in R.vtt
23.1 kB
32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.vtt
22.7 kB
27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.vtt
22.5 kB
31. Artificial Neural Networks/2. The Neuron.vtt
22.4 kB
27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.vtt
22.3 kB
5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.vtt
22.0 kB
4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.vtt
21.7 kB
8. Decision Tree Regression/3. Decision Tree Regression in Python.vtt
21.6 kB
5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.vtt
21.6 kB
21. K-Means Clustering/1. K-Means Clustering Intuition.vtt
21.4 kB
12. Logistic Regression/1. Logistic Regression Intuition.vtt
21.4 kB
16. Naive Bayes/2. Naive Bayes Intuition.vtt
21.4 kB
29. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 8.vtt
21.3 kB
2. -------------------- Part 1 Data Preprocessing --------------------/10. Feature Scaling.vtt
21.3 kB
13. K-Nearest Neighbors (K-NN)/4. K-NN in R.vtt
21.2 kB
24. Apriori/4. Apriori in R - Step 2.vtt
21.1 kB
32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.vtt
20.9 kB
24. Apriori/7. Apriori in Python - Step 2.vtt
20.6 kB
4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.vtt
20.5 kB
16. Naive Bayes/7. Naive Bayes in R.vtt
19.9 kB
27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.vtt
19.9 kB
2. -------------------- Part 1 Data Preprocessing --------------------/6. Missing Data.vtt
19.9 kB
32. Convolutional Neural Networks/2. What are convolutional neural networks.vtt
19.8 kB
38. Model Selection/4. Grid Search in Python - Step 1.vtt
19.8 kB
27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.vtt
19.7 kB
27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.vtt
19.5 kB
36. Kernel PCA/2. Kernel PCA in Python.vtt
19.2 kB
13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.vtt
19.2 kB
32. Convolutional Neural Networks/5. Step 2 - Pooling.vtt
18.8 kB
38. Model Selection/6. Grid Search in R.vtt
18.7 kB
31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).vtt
18.4 kB
27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.vtt
18.3 kB
38. Model Selection/2. k-Fold Cross Validation in Python.vtt
18.0 kB
5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.vtt
18.0 kB
31. Artificial Neural Networks/12. ANN in Python - Step 1.vtt
17.8 kB
21. K-Means Clustering/6. K-Means Clustering in R.vtt
17.8 kB
17. Decision Tree Classification/3. Decision Tree Classification in Python.vtt
17.6 kB
29. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.vtt
17.6 kB
31. Artificial Neural Networks/16. ANN in Python - Step 5.vtt
17.5 kB
14. Support Vector Machine (SVM)/3. SVM in Python.vtt
17.3 kB
32. Convolutional Neural Networks/15. CNN in Python - Step 4.vtt
17.3 kB
31. Artificial Neural Networks/4. How do Neural Networks work.vtt
17.2 kB
6. Polynomial Regression/12. R Regression Template.vtt
17.1 kB
7. Support Vector Regression (SVR)/4. SVR in R.vtt
17.0 kB
2. -------------------- Part 1 Data Preprocessing --------------------/4. Importing the Dataset.vtt
17.0 kB
21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.vtt
16.9 kB
31. Artificial Neural Networks/5. How do Neural Networks learn.vtt
16.9 kB
31. Artificial Neural Networks/24. ANN in R - Step 3.vtt
16.8 kB
14. Support Vector Machine (SVM)/4. SVM in R.vtt
16.8 kB
34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.vtt
16.8 kB
32. Convolutional Neural Networks/12. CNN in Python - Step 1.vtt
16.6 kB
29. -------------------- Part 7 Natural Language Processing --------------------/4. Natural Language Processing in Python - Step 1.vtt
16.3 kB
30. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.vtt
16.3 kB
22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.vtt
16.2 kB
6. Polynomial Regression/3. Polynomial Regression in Python - Step 1.vtt
16.1 kB
34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.vtt
15.8 kB
6. Polynomial Regression/4. Polynomial Regression in Python - Step 2.vtt
15.7 kB
8. Decision Tree Regression/1. Decision Tree Regression Intuition.vtt
15.6 kB
29. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 4.vtt
15.5 kB
6. Polynomial Regression/7. Python Regression Template.vtt
15.0 kB
34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.vtt
15.0 kB
19. Evaluating Classification Models Performance/4. CAP Curve.vtt
14.9 kB
15. Kernel SVM/3. The Kernel Trick.vtt
14.8 kB
16. Naive Bayes/4. Naive Bayes Intuition (Extras).vtt
14.6 kB
14. Support Vector Machine (SVM)/1. SVM Intuition.vtt
14.5 kB
25. Eclat/3. Eclat in R.vtt
14.4 kB
4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.vtt
14.2 kB
5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.vtt
14.2 kB
29. -------------------- Part 7 Natural Language Processing --------------------/5. Natural Language Processing in Python - Step 2.vtt
14.1 kB
6. Polynomial Regression/9. Polynomial Regression in R - Step 2.vtt
14.0 kB
38. Model Selection/5. Grid Search in Python - Step 2.vtt
13.6 kB
5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.vtt
13.4 kB
10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.vtt
13.3 kB
34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.vtt
13.3 kB
22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.vtt
13.1 kB
5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.vtt
13.0 kB
2. -------------------- Part 1 Data Preprocessing --------------------/11. And here is our Data Preprocessing Template!.vtt
13.0 kB
6. Polynomial Regression/8. Polynomial Regression in R - Step 1.vtt
13.0 kB
29. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - Step 10.vtt
12.8 kB
31. Artificial Neural Networks/6. Gradient Descent.vtt
12.6 kB
16. Naive Bayes/6. Naive Bayes in Python.vtt
12.5 kB
39. XGBoost/2. XGBoost in Python - Step 1.vtt
12.3 kB
10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.vtt
12.3 kB
21. K-Means Clustering/2. K-Means Random Initialization Trap.vtt
11.9 kB
10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.vtt
11.9 kB
29. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.vtt
11.6 kB
32. Convolutional Neural Networks/21. CNN in Python - Step 10.vtt
11.6 kB
4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.vtt
11.4 kB
1. Welcome to the course!/8. Installing Python and Anaconda (Mac, Linux & Windows).vtt
11.2 kB
31. Artificial Neural Networks/7. Stochastic Gradient Descent.vtt
11.0 kB
5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.vtt
10.8 kB
31. Artificial Neural Networks/3. The Activation Function.vtt
10.8 kB
5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.vtt
10.8 kB
34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.vtt
10.6 kB
19. Evaluating Classification Models Performance/1. False Positives & False Negatives.vtt
10.4 kB
7. Support Vector Regression (SVR)/2. SVR Intuition.vtt
10.4 kB
28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.vtt
10.1 kB
5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.vtt
9.9 kB
2. -------------------- Part 1 Data Preprocessing --------------------/2. Get the dataset.vtt
9.6 kB
15. Kernel SVM/2. Mapping to a higher dimension.vtt
9.5 kB
9. Random Forest Regression/1. Random Forest Regression Intuition.vtt
9.5 kB
29. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 5.vtt
9.5 kB
31. Artificial Neural Networks/21. ANN in Python - Step 10.vtt
9.2 kB
4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.vtt
9.1 kB
31. Artificial Neural Networks/23. ANN in R - Step 2.vtt
9.1 kB
29. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.vtt
9.0 kB
29. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 7.vtt
8.8 kB
22. Hierarchical Clustering/6. HC in Python - Step 2.vtt
8.8 kB
16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).vtt
8.8 kB
19. Evaluating Classification Models Performance/5. CAP Curve Analysis.vtt
8.5 kB
14. Support Vector Machine (SVM)/4.1 SVM.zip.zip
8.5 kB
31. Artificial Neural Networks/20. ANN in Python - Step 9.vtt
8.4 kB
1. Welcome to the course!/3. Why Machine Learning is the Future.vtt
8.3 kB
32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.vtt
8.3 kB
32. Convolutional Neural Networks/18. CNN in Python - Step 7.vtt
8.2 kB
4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.vtt
8.2 kB
1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).vtt
8.1 kB
12. Logistic Regression/9. Logistic Regression in R - Step 1.vtt
8.1 kB
4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.vtt
7.7 kB
5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.vtt
7.6 kB
22. Hierarchical Clustering/11. HC in R - Step 2.vtt
7.5 kB
29. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.vtt
7.5 kB
29. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 9.vtt
7.4 kB
13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.vtt
7.4 kB
25. Eclat/1. Eclat Intuition.vtt
7.3 kB
6. Polynomial Regression/1. Polynomial Regression Intuition.vtt
7.2 kB
2. -------------------- Part 1 Data Preprocessing --------------------/3. Importing the Libraries.vtt
7.1 kB
29. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.vtt
7.1 kB
22. Hierarchical Clustering/7. HC in Python - Step 3.vtt
7.1 kB
4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.vtt
7.0 kB
22. Hierarchical Clustering/5. HC in Python - Step 1.vtt
7.0 kB
19. Evaluating Classification Models Performance/2. Confusion Matrix.vtt
6.9 kB
32. Convolutional Neural Networks/17. CNN in Python - Step 6.vtt
6.9 kB
12. Logistic Regression/11. Logistic Regression in R - Step 3.vtt
6.8 kB
32. Convolutional Neural Networks/16. CNN in Python - Step 5.vtt
6.7 kB
31. Artificial Neural Networks/10. Business Problem Description.vtt
6.6 kB
10. Evaluating Regression Models Performance/1. R-Squared Intuition.vtt
6.6 kB
18. Random Forest Classification/1. Random Forest Classification Intuition.vtt
6.6 kB
12. Logistic Regression/6. Logistic Regression in Python - Step 4.vtt
6.5 kB
31. Artificial Neural Networks/8. Backpropagation.vtt
6.5 kB
5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.vtt
6.4 kB
29. -------------------- Part 7 Natural Language Processing --------------------/2. Natural Language Processing Intuition.vtt
6.4 kB
22. Hierarchical Clustering/9. HC in Python - Step 5.vtt
6.3 kB
12. Logistic Regression/14. R Classification Template.vtt
6.2 kB
22. Hierarchical Clustering/8. HC in Python - Step 4.vtt
6.0 kB
22. Hierarchical Clustering/10. HC in R - Step 1.vtt
5.8 kB
12. Logistic Regression/8. Python Classification Template.vtt
5.6 kB
32. Convolutional Neural Networks/8. Summary.vtt
5.5 kB
28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.vtt
5.3 kB
31. Artificial Neural Networks/18. ANN in Python - Step 7.vtt
5.3 kB
5. Multiple Linear Regression/2. Dataset + Business Problem Description.vtt
5.2 kB
29. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.vtt
5.1 kB
4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.vtt
5.1 kB
28. Thompson Sampling/7. Thompson Sampling in R - Step 2.vtt
4.9 kB
1. Welcome to the course!/1. Applications of Machine Learning.vtt
4.8 kB
32. Convolutional Neural Networks/1. Plan of attack.vtt
4.7 kB
31. Artificial Neural Networks/14. ANN in Python - Step 3.vtt
4.7 kB
40. Bonus Lectures/1. YOUR SPECIAL BONUS.html
4.6 kB
35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.vtt
4.6 kB
34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.vtt
4.6 kB
12. Logistic Regression/4. Logistic Regression in Python - Step 2.vtt
4.5 kB
27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.vtt
4.5 kB
15. Kernel SVM/4. Types of Kernel Functions.vtt
4.5 kB
22. Hierarchical Clustering/12. HC in R - Step 3.vtt
4.4 kB
12. Logistic Regression/2. How to get the dataset.vtt
4.3 kB
13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.vtt
4.3 kB
14. Support Vector Machine (SVM)/2. How to get the dataset.vtt
4.3 kB
15. Kernel SVM/5. How to get the dataset.vtt
4.3 kB
16. Naive Bayes/5. How to get the dataset.vtt
4.3 kB
17. Decision Tree Classification/2. How to get the dataset.vtt
4.3 kB
18. Random Forest Classification/2. How to get the dataset.vtt
4.3 kB
21. K-Means Clustering/4. How to get the dataset.vtt
4.3 kB
22. Hierarchical Clustering/4. How to get the dataset.vtt
4.3 kB
24. Apriori/2. How to get the dataset.vtt
4.3 kB
25. Eclat/2. How to get the dataset.vtt
4.3 kB
27. Upper Confidence Bound (UCB)/3. How to get the dataset.vtt
4.3 kB
28. Thompson Sampling/3. How to get the dataset.vtt
4.3 kB
29. -------------------- Part 7 Natural Language Processing --------------------/3. How to get the dataset.vtt
4.3 kB
31. Artificial Neural Networks/9. How to get the dataset.vtt
4.3 kB
32. Convolutional Neural Networks/10. How to get the dataset.vtt
4.3 kB
34. Principal Component Analysis (PCA)/2. How to get the dataset.vtt
4.3 kB
35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.vtt
4.3 kB
36. Kernel PCA/1. How to get the dataset.vtt
4.3 kB
39. XGBoost/1. How to get the dataset.vtt
4.3 kB
4. Simple Linear Regression/1. How to get the dataset.vtt
4.3 kB
5. Multiple Linear Regression/1. How to get the dataset.vtt
4.3 kB
6. Polynomial Regression/2. How to get the dataset.vtt
4.3 kB
7. Support Vector Regression (SVR)/1. How to get the dataset.vtt
4.3 kB
8. Decision Tree Regression/2. How to get the dataset.vtt
4.3 kB
9. Random Forest Regression/2. How to get the dataset.vtt
4.3 kB
29. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.vtt
4.3 kB
31. Artificial Neural Networks/17. ANN in Python - Step 6.vtt
4.1 kB
4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.vtt
4.0 kB
32. Convolutional Neural Networks/13. CNN in Python - Step 2.vtt
4.0 kB
12. Logistic Regression/10. Logistic Regression in R - Step 2.vtt
4.0 kB
15. Kernel SVM/1. Kernel SVM Intuition.vtt
4.0 kB
32. Convolutional Neural Networks/19. CNN in Python - Step 8.vtt
4.0 kB
27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.vtt
4.0 kB
29. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 6.vtt
4.0 kB
19. Evaluating Classification Models Performance/6. Conclusion of Part 3 - Classification.html
3.8 kB
4. Simple Linear Regression/2. Dataset + Business Problem Description.vtt
3.8 kB
22. Hierarchical Clustering/14. HC in R - Step 5.vtt
3.7 kB
12. Logistic Regression/5. Logistic Regression in Python - Step 3.vtt
3.7 kB
5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.vtt
3.7 kB
12. Logistic Regression/12. Logistic Regression in R - Step 4.vtt
3.6 kB
31. Artificial Neural Networks/1. Plan of attack.vtt
3.6 kB
22. Hierarchical Clustering/13. HC in R - Step 4.vtt
3.6 kB
31. Artificial Neural Networks/15. ANN in Python - Step 4.vtt
3.5 kB
1. Welcome to the course!/4. Important notes, tips & tricks for this course.html
3.3 kB
5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.vtt
3.2 kB
19. Evaluating Classification Models Performance/3. Accuracy Paradox.vtt
3.0 kB
10. Evaluating Regression Models Performance/5. Conclusion of Part 2 - Regression.html
3.0 kB
2. -------------------- Part 1 Data Preprocessing --------------------/8. WARNING - Update.html
2.9 kB
29. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.vtt
2.9 kB
32. Convolutional Neural Networks/22. CNN in R.html
2.4 kB
1. Welcome to the course!/2. BONUS Learning Paths.html
2.4 kB
2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.vtt
2.3 kB
32. Convolutional Neural Networks/6. Step 3 - Flattening.vtt
2.3 kB
5. Multiple Linear Regression/15. Multiple Linear Regression in Python - Automatic Backward Elimination.html
2.2 kB
39. XGBoost/5. THANK YOU bonus video.vtt
2.1 kB
1. Welcome to the course!/13. FAQBot!.html
1.8 kB
29. -------------------- Part 7 Natural Language Processing --------------------/1. Welcome to Part 7 - Natural Language Processing.html
1.7 kB
32. Convolutional Neural Networks/14. CNN in Python - Step 3.vtt
1.6 kB
1. Welcome to the course!/5. This PDF resource will help you a lot.html
1.5 kB
5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.vtt
1.5 kB
31. Artificial Neural Networks/11. Installing Keras.html
1.4 kB
29. -------------------- Part 7 Natural Language Processing --------------------/25. Homework Challenge.html
1.4 kB
29. -------------------- Part 7 Natural Language Processing --------------------/14. Homework Challenge.html
1.4 kB
5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.vtt
1.4 kB
1. Welcome to the course!/9. Update Recommended Anaconda Version.html
1.4 kB
33. -------------------- Part 9 Dimensionality Reduction --------------------/1. Welcome to Part 9 - Dimensionality Reduction.html
1.3 kB
1. Welcome to the course!/11. BONUS Meet your instructors.html
1.1 kB
1. Welcome to the course!/6. The whole code folder of the course.html
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32. Convolutional Neural Networks/11. Installing Keras.html
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37. -------------------- Part 10 Model Selection & Boosting --------------------/1. Welcome to Part 10 - Model Selection & Boosting.html
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3. -------------------- Part 2 Regression --------------------/1. Welcome to Part 2 - Regression.html
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30. -------------------- Part 8 Deep Learning --------------------/1. Welcome to Part 8 - Deep Learning.html
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11. -------------------- Part 3 Classification --------------------/1. Welcome to Part 3 - Classification.html
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26. -------------------- Part 6 Reinforcement Learning --------------------/1. Welcome to Part 6 - Reinforcement Learning.html
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20. -------------------- Part 4 Clustering --------------------/1. Welcome to Part 4 - Clustering.html
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5. Multiple Linear Regression/21. Multiple Linear Regression in R - Automatic Backward Elimination.html
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5. Multiple Linear Regression/7. Prerequisites What is the P-Value.html
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1. Welcome to the course!/12. Some Additional Resources.html
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22. Hierarchical Clustering/16. Conclusion of Part 4 - Clustering.html
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23. -------------------- Part 5 Association Rule Learning --------------------/1. Welcome to Part 5 - Association Rule Learning.html
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0. Websites you may like/[FCS Forum].url
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0. Websites you may like/[FreeCourseSite.com].url
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0. Websites you may like/[CourseClub.ME].url
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12. Logistic Regression/15. Logistic Regression.html
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13. K-Nearest Neighbors (K-NN)/5. K-Nearest Neighbor.html
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2. -------------------- Part 1 Data Preprocessing --------------------/12. Data Preprocessing.html
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21. K-Means Clustering/7. K-Means Clustering.html
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22. Hierarchical Clustering/15. Hierarchical Clustering.html
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4. Simple Linear Regression/13. Simple Linear Regression.html
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5. Multiple Linear Regression/22. Multiple Linear Regression.html
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