[GigaCourse.com] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science - 2048BT - 全球最新最全bt种子资源在线搜索库，实时索引全球磁力链接资源

BT种子名称

分享给好友

BT种子基本信息

种子哈希：e856ffe88ac9036f6f9ff4831afcadf5d1d45518
文档大小：6.4 GB
文档个数：575个文档
下载次数：2846次
下载速度：极快
收录时间：2020-04-11
最近下载：2024-08-11
DMCA/屏蔽：DMCA/屏蔽

下载磁力链接

magnet:?xt=urn:btih:E856FFE88AC9036F6F9FF4831AFCADF5D1D45518
复制磁力链接到utorrent、Bitcomet、迅雷、115、百度网盘、PIKPAK等下载工具进行下载。

下载BT种子

磁力链接种子下载迅雷下载二维码含羞草 51品茶 91视频逼哩逼哩欲漫涩草榴社区抖阴破解版成人快手暗网禁区缅北禁地 TikTok成人版暗网解密

[GigaCourse.com] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science的二维码

文档列表

1. Welcome to the course!/6.1 Machine_Learning_A-Z_New.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.mp4 55.5 MB
39. XGBoost/5. THANK YOU bonus video.mp4 54.8 MB
12. Logistic Regression/14. 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/11. 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/6. Polynomial Regression in Python - Step 3.mp4 45.1 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
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.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.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
24. Apriori/7. Apriori in Python - Step 2.mp4 31.0 MB
38. Model Selection/5. Grid Search in Python - Step 2.mp4 30.9 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 30.7 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/8. 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/5. Polynomial Regression in Python - Step 2.mp4 28.4 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/13. R Regression Template.mp4 26.6 MB
32. Convolutional Neural Networks/12. CNN in Python - Step 1.mp4 26.1 MB
6. Polynomial Regression/4. 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/10. 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/12. 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.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.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/9. 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
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 15.0 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
29. Part 7 Natural Language Processing/17. Natural Language Processing in R - Step 3.mp4 14.2 MB
6. Polynomial Regression/7. 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/15. 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
16. Naive Bayes/5. 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
22. Hierarchical Clustering/4. 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
38. Model Selection/1. How to get the dataset.mp4 12.3 MB
4. Simple Linear Regression/1. How to get the dataset.mp4 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
14. Support Vector Machine (SVM)/2. How to get the dataset.mp4 12.3 MB
15. Kernel SVM/5. How to get the dataset.mp4 12.3 MB
17. Decision Tree Classification/2. 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
27. Upper Confidence Bound (UCB)/3. How to get the dataset.mp4 12.3 MB
28. Thompson Sampling/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
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
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 7.8 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 6.9 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 3.5 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 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 49.7 kB
16. Naive Bayes/1. Bayes Theorem.srt 35.3 kB
18. Random Forest Classification/4. Random Forest Classification in R.srt 33.2 kB
8. Decision Tree Regression/4. Decision Tree Regression in R.srt 32.9 kB
6. Polynomial Regression/6. Polynomial Regression in Python - Step 3.srt 32.2 kB
24. Apriori/5. Apriori in R - Step 3.srt 31.9 kB
24. Apriori/3. Apriori in R - Step 1.srt 31.8 kB
7. Support Vector Regression (SVR)/3. SVR in Python.srt 31.6 kB
6. Polynomial Regression/11. Polynomial Regression in R - Step 3.srt 31.6 kB
36. Kernel PCA/3. Kernel PCA in R.srt 31.5 kB
18. Random Forest Classification/3. Random Forest Classification in Python.srt 31.5 kB
12. Logistic Regression/7. Logistic Regression in Python - Step 5.srt 30.4 kB
35. Linear Discriminant Analysis (LDA)/4. LDA in R.srt 30.4 kB
32. Convolutional Neural Networks/20. CNN in Python - Step 9.srt 30.1 kB
17. Decision Tree Classification/4. Decision Tree Classification in R.srt 29.8 kB
12. Logistic Regression/14. Logistic Regression in R - Step 5.srt 29.8 kB
31. Artificial Neural Networks/13. ANN in Python - Step 2.srt 29.6 kB
28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.srt 29.6 kB
32. Convolutional Neural Networks/7. Step 4 - Full Connection.srt 29.3 kB
15. Kernel SVM/6. Kernel SVM in Python.srt 28.9 kB
21. K-Means Clustering/5. K-Means Clustering in Python.srt 28.9 kB
9. Random Forest Regression/4. Random Forest Regression in R.srt 28.8 kB
24. Apriori/6. Apriori in Python - Step 1.srt 28.6 kB
38. Model Selection/3. k-Fold Cross Validation in R.srt 28.6 kB
28. Thompson Sampling/6. Thompson Sampling in R - Step 1.srt 28.5 kB
9. Random Forest Regression/3. Random Forest Regression in Python.srt 28.2 kB
28. Thompson Sampling/1. Thompson Sampling Intuition.srt 28.2 kB
5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.srt 28.1 kB
2. Part 1 Data Preprocessing/7. Categorical Data.srt 27.7 kB
27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.srt 27.6 kB
2. Part 1 Data Preprocessing/9. Splitting the Dataset into the Training set and Test set.srt 27.6 kB
31. Artificial Neural Networks/22. ANN in R - Step 1.srt 27.4 kB
35. Linear Discriminant Analysis (LDA)/3. LDA in Python.srt 27.1 kB
29. Part 7 Natural Language Processing/24. Natural Language Processing in R - Step 10.srt 26.9 kB
39. XGBoost/4. XGBoost in R.srt 26.6 kB
24. Apriori/1. Apriori Intuition.srt 26.5 kB
22. Hierarchical Clustering/16.1 Clustering-Pros-Cons.pdf 26.4 kB
15. Kernel SVM/7. Kernel SVM in R.srt 26.1 kB
27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.srt 25.9 kB
32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.srt 25.9 kB
27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.srt 25.9 kB
31. Artificial Neural Networks/2. The Neuron.srt 25.6 kB
5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.srt 25.0 kB
29. Part 7 Natural Language Processing/15. Natural Language Processing in R - Step 1.srt 24.6 kB
12. Logistic Regression/1. Logistic Regression Intuition.srt 24.5 kB
4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.srt 24.5 kB
29. Part 7 Natural Language Processing/11. Natural Language Processing in Python - Step 8.srt 24.4 kB
8. Decision Tree Regression/3. Decision Tree Regression in Python.srt 24.3 kB
5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.srt 24.1 kB
2. Part 1 Data Preprocessing/10. Feature Scaling.srt 24.0 kB
13. K-Nearest Neighbors (K-NN)/4. K-NN in R.srt 23.9 kB
21. K-Means Clustering/1. K-Means Clustering Intuition.srt 23.9 kB
16. Naive Bayes/2. Naive Bayes Intuition.srt 23.9 kB
32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.srt 23.8 kB
24. Apriori/4. Apriori in R - Step 2.srt 23.6 kB
2. Part 1 Data Preprocessing/6. Missing Data.srt 23.2 kB
24. Apriori/7. Apriori in Python - Step 2.srt 23.1 kB
4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.srt 23.0 kB
27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.srt 22.8 kB
27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.srt 22.7 kB
32. Convolutional Neural Networks/2. What are convolutional neural networks.srt 22.6 kB
38. Model Selection/4. Grid Search in Python - Step 1.srt 22.6 kB
27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.srt 22.4 kB
16. Naive Bayes/7. Naive Bayes in R.srt 22.4 kB
27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.srt 22.4 kB
36. Kernel PCA/2. Kernel PCA in Python.srt 22.0 kB
13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.srt 21.7 kB
32. Convolutional Neural Networks/5. Step 2 - Pooling.srt 21.5 kB
38. Model Selection/6. Grid Search in R.srt 21.4 kB
31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).srt 21.2 kB
27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.srt 21.0 kB
38. Model Selection/2. k-Fold Cross Validation in Python.srt 20.7 kB
31. Artificial Neural Networks/12. ANN in Python - Step 1.srt 20.5 kB
34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.srt 20.2 kB
5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.srt 20.2 kB
29. Part 7 Natural Language Processing/23. Natural Language Processing in R - Step 9.srt 20.1 kB
24. Apriori/8. Apriori in Python - Step 3.srt 20.1 kB
31. Artificial Neural Networks/16. ANN in Python - Step 5.srt 20.0 kB
21. K-Means Clustering/6. K-Means Clustering in R.srt 19.9 kB
17. Decision Tree Classification/3. Decision Tree Classification in Python.srt 19.9 kB
32. Convolutional Neural Networks/15. CNN in Python - Step 4.srt 19.7 kB
14. Support Vector Machine (SVM)/3. SVM in Python.srt 19.6 kB
31. Artificial Neural Networks/4. How do Neural Networks work.srt 19.6 kB
31. Artificial Neural Networks/5. How do Neural Networks learn.srt 19.4 kB
39. XGBoost/3. XGBoost in Python - Step 2.srt 19.3 kB
31. Artificial Neural Networks/24. ANN in R - Step 3.srt 19.3 kB
34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.srt 19.1 kB
7. Support Vector Regression (SVR)/4. SVR in R.srt 19.1 kB
2. Part 1 Data Preprocessing/4. Importing the Dataset.srt 19.1 kB
6. Polynomial Regression/13. R Regression Template.srt 19.1 kB
21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.srt 18.9 kB
14. Support Vector Machine (SVM)/4. SVM in R.srt 18.8 kB
32. Convolutional Neural Networks/12. CNN in Python - Step 1.srt 18.8 kB
29. Part 7 Natural Language Processing/4. Natural Language Processing in Python - Step 1.srt 18.8 kB
30. Part 8 Deep Learning/2. What is Deep Learning.srt 18.6 kB
34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.srt 18.1 kB
22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.srt 18.0 kB
6. Polynomial Regression/4. Polynomial Regression in Python - Step 1.srt 17.9 kB
29. Part 7 Natural Language Processing/7. Natural Language Processing in Python - Step 4.srt 17.9 kB
6. Polynomial Regression/5. Polynomial Regression in Python - Step 2.srt 17.6 kB
8. Decision Tree Regression/1. Decision Tree Regression Intuition.srt 17.5 kB
34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.srt 17.3 kB
15. Kernel SVM/3. The Kernel Trick.srt 16.9 kB
6. Polynomial Regression/8. Python Regression Template.srt 16.8 kB
19. Evaluating Classification Models Performance/4. CAP Curve.srt 16.6 kB
16. Naive Bayes/4. Naive Bayes Intuition (Extras).srt 16.3 kB
29. Part 7 Natural Language Processing/5. Natural Language Processing in Python - Step 2.srt 16.2 kB
25. Eclat/3. Eclat in R.srt 16.2 kB
14. Support Vector Machine (SVM)/1. SVM Intuition.srt 16.1 kB
4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.srt 15.8 kB
6. Polynomial Regression/12. Polynomial Regression in R - Step 4.srt 15.8 kB
5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.srt 15.8 kB
38. Model Selection/5. Grid Search in Python - Step 2.srt 15.7 kB
6. Polynomial Regression/10. Polynomial Regression in R - Step 2.srt 15.6 kB
5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.srt 15.3 kB
34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.srt 15.2 kB
22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.srt 14.9 kB
10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.srt 14.8 kB
29. Part 7 Natural Language Processing/13. Natural Language Processing in Python - Step 10.srt 14.7 kB
22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.srt 14.7 kB
5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.srt 14.5 kB
2. Part 1 Data Preprocessing/11. And here is our Data Preprocessing Template!.srt 14.5 kB
6. Polynomial Regression/9. Polynomial Regression in R - Step 1.srt 14.5 kB
31. Artificial Neural Networks/6. Gradient Descent.srt 14.4 kB
16. Naive Bayes/6. Naive Bayes in Python.srt 14.1 kB
39. XGBoost/2. XGBoost in Python - Step 1.srt 14.0 kB
10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.srt 13.6 kB
32. Convolutional Neural Networks/21. CNN in Python - Step 10.srt 13.3 kB
21. K-Means Clustering/2. K-Means Random Initialization Trap.srt 13.3 kB
10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.srt 13.2 kB
29. Part 7 Natural Language Processing/16. Natural Language Processing in R - Step 2.srt 13.2 kB
17. Decision Tree Classification/1. Decision Tree Classification Intuition.srt 13.2 kB
4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.srt 12.6 kB
1. Welcome to the course!/8. Installing Python and Anaconda (Mac, Linux & Windows).srt 12.6 kB
31. Artificial Neural Networks/7. Stochastic Gradient Descent.srt 12.4 kB
31. Artificial Neural Networks/3. The Activation Function.srt 12.3 kB
5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.srt 12.1 kB
5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.srt 12.1 kB
34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.srt 12.1 kB
7. Support Vector Regression (SVR)/2. SVR Intuition.srt 11.6 kB
19. Evaluating Classification Models Performance/1. False Positives & False Negatives.srt 11.6 kB
28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.srt 11.4 kB
31. Artificial Neural Networks/19. ANN in Python - Step 8.srt 11.3 kB
5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.srt 11.0 kB
2. Part 1 Data Preprocessing/2. Get the dataset.srt 10.9 kB
29. Part 7 Natural Language Processing/8. Natural Language Processing in Python - Step 5.srt 10.9 kB
15. Kernel SVM/2. Mapping to a higher dimension.srt 10.8 kB
31. Artificial Neural Networks/21. ANN in Python - Step 10.srt 10.6 kB
9. Random Forest Regression/1. Random Forest Regression Intuition.srt 10.5 kB
29. Part 7 Natural Language Processing/17. Natural Language Processing in R - Step 3.srt 10.4 kB
31. Artificial Neural Networks/23. ANN in R - Step 2.srt 10.4 kB
4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.srt 10.1 kB
29. Part 7 Natural Language Processing/10. Natural Language Processing in Python - Step 7.srt 10.0 kB
22. Hierarchical Clustering/6. HC in Python - Step 2.srt 9.7 kB
31. Artificial Neural Networks/20. ANN in Python - Step 9.srt 9.7 kB
16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).srt 9.7 kB
19. Evaluating Classification Models Performance/5. CAP Curve Analysis.srt 9.5 kB
1. Welcome to the course!/3. Why Machine Learning is the Future.srt 9.5 kB
32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.srt 9.4 kB
1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).srt 9.4 kB
32. Convolutional Neural Networks/18. CNN in Python - Step 7.srt 9.3 kB
12. Logistic Regression/9. Logistic Regression in R - Step 1.srt 9.1 kB
4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.srt 9.1 kB
12. Logistic Regression/3. Logistic Regression in Python - Step 1.srt 9.0 kB
6. Polynomial Regression/7. Polynomial Regression in Python - Step 4.srt 8.9 kB
29. Part 7 Natural Language Processing/20. Natural Language Processing in R - Step 6.srt 8.6 kB
4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.srt 8.5 kB
5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.srt 8.5 kB
14. Support Vector Machine (SVM)/4.1 SVM.zip 8.5 kB
29. Part 7 Natural Language Processing/12. Natural Language Processing in Python - Step 9.srt 8.4 kB
22. Hierarchical Clustering/11. HC in R - Step 2.srt 8.3 kB
25. Eclat/1. Eclat Intuition.srt 8.3 kB
2. Part 1 Data Preprocessing/3. Importing the Libraries.srt 8.3 kB
13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.srt 8.2 kB
29. Part 7 Natural Language Processing/22. Natural Language Processing in R - Step 8.srt 8.2 kB
6. Polynomial Regression/1. Polynomial Regression Intuition.srt 8.0 kB
22. Hierarchical Clustering/7. HC in Python - Step 3.srt 7.9 kB
4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.srt 7.9 kB
32. Convolutional Neural Networks/17. CNN in Python - Step 6.srt 7.8 kB
22. Hierarchical Clustering/5. HC in Python - Step 1.srt 7.8 kB
19. Evaluating Classification Models Performance/2. Confusion Matrix.srt 7.7 kB
32. Convolutional Neural Networks/16. CNN in Python - Step 5.srt 7.7 kB
12. Logistic Regression/11. Logistic Regression in R - Step 3.srt 7.6 kB
31. Artificial Neural Networks/10. Business Problem Description.srt 7.5 kB
10. Evaluating Regression Models Performance/1. R-Squared Intuition.srt 7.3 kB
12. Logistic Regression/6. Logistic Regression in Python - Step 4.srt 7.3 kB
31. Artificial Neural Networks/8. Backpropagation.srt 7.3 kB
29. Part 7 Natural Language Processing/2. Natural Language Processing Intuition.srt 7.2 kB
5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.srt 7.2 kB
18. Random Forest Classification/1. Random Forest Classification Intuition.srt 7.2 kB
22. Hierarchical Clustering/9. HC in Python - Step 5.srt 7.0 kB
12. Logistic Regression/15. R Classification Template.srt 6.9 kB
22. Hierarchical Clustering/8. HC in Python - Step 4.srt 6.6 kB
22. Hierarchical Clustering/10. HC in R - Step 1.srt 6.5 kB
12. Logistic Regression/8. Python Classification Template.srt 6.2 kB
32. Convolutional Neural Networks/8. Summary.srt 6.2 kB
28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.srt 5.9 kB
31. Artificial Neural Networks/18. ANN in Python - Step 7.srt 5.8 kB
5. Multiple Linear Regression/2. Dataset + Business Problem Description.srt 5.8 kB
29. Part 7 Natural Language Processing/21. Natural Language Processing in R - Step 7.srt 5.7 kB
4. 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
28. Thompson Sampling/7. Thompson Sampling in R - Step 2.srt 5.4 kB
32. Convolutional Neural Networks/1. Plan of attack.srt 5.4 kB
31. Artificial Neural Networks/14. ANN in Python - Step 3.srt 5.3 kB
35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.srt 5.2 kB
34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.srt 5.2 kB
27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.srt 5.1 kB
15. Kernel SVM/4. Types of Kernel Functions.srt 5.1 kB
12. Logistic Regression/4. Logistic Regression in Python - Step 2.srt 5.0 kB
12. Logistic Regression/2. How to get the dataset.srt 4.9 kB
13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.srt 4.9 kB
14. Support Vector Machine (SVM)/2. How to get the dataset.srt 4.9 kB
15. Kernel SVM/5. How to get the dataset.srt 4.9 kB
16. Naive Bayes/5. How to get the dataset.srt 4.9 kB
17. Decision Tree Classification/2. How to get the dataset.srt 4.9 kB
18. Random Forest Classification/2. How to get the dataset.srt 4.9 kB
21. K-Means Clustering/4. How to get the dataset.srt 4.9 kB
22. Hierarchical Clustering/4. How to get the dataset.srt 4.9 kB
24. Apriori/2. How to get the dataset.srt 4.9 kB
25. Eclat/2. How to get the dataset.srt 4.9 kB
27. Upper Confidence Bound (UCB)/3. How to get the dataset.srt 4.9 kB
28. Thompson Sampling/3. How to get the dataset.srt 4.9 kB
29. Part 7 Natural Language Processing/3. How to get the dataset.srt 4.9 kB
31. Artificial Neural Networks/9. How to get the dataset.srt 4.9 kB
32. Convolutional Neural Networks/10. How to get the dataset.srt 4.9 kB
34. Principal Component Analysis (PCA)/2. How to get the dataset.srt 4.9 kB
35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.srt 4.9 kB
36. Kernel PCA/1. How to get the dataset.srt 4.9 kB
38. Model Selection/1. How to get the dataset.srt 4.9 kB
39. XGBoost/1. How to get the dataset.srt 4.9 kB
4. Simple Linear Regression/1. How to get the dataset.srt 4.9 kB
5. Multiple Linear Regression/1. How to get the dataset.srt 4.9 kB
6. Polynomial Regression/2. How to get the dataset.srt 4.9 kB
7. Support Vector Regression (SVR)/1. How to get the dataset.srt 4.9 kB
8. Decision Tree Regression/2. How to get the dataset.srt 4.9 kB
9. Random Forest Regression/2. How to get the dataset.srt 4.9 kB
22. Hierarchical Clustering/12. HC in R - Step 3.srt 4.8 kB
40. Bonus Lectures/1. YOUR SPECIAL BONUS.html 4.8 kB
29. Part 7 Natural Language Processing/18. Natural Language Processing in R - Step 4.srt 4.8 kB
32. Convolutional Neural Networks/19. CNN in Python - Step 8.srt 4.7 kB
31. Artificial Neural Networks/17. ANN in Python - Step 6.srt 4.6 kB
32. Convolutional Neural Networks/13. CNN in Python - Step 2.srt 4.6 kB
27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.srt 4.5 kB
15. Kernel SVM/1. Kernel SVM Intuition.srt 4.5 kB
29. Part 7 Natural Language Processing/9. Natural Language Processing in Python - Step 6.srt 4.5 kB
12. Logistic Regression/10. Logistic Regression in R - Step 2.srt 4.5 kB
4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.srt 4.4 kB
12. Logistic Regression/5. Logistic Regression in Python - Step 3.srt 4.2 kB
4. Simple Linear Regression/2. Dataset + Business Problem Description.srt 4.2 kB
5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.srt 4.2 kB
1. Welcome to the course!/7. Updates on Udemy Reviews.srt 4.1 kB
22. Hierarchical Clustering/14. HC in R - Step 5.srt 4.1 kB
31. Artificial Neural Networks/1. Plan of attack.srt 4.1 kB
12. Logistic Regression/12. Logistic Regression in R - Step 4.srt 4.1 kB
31. Artificial Neural Networks/15. ANN in Python - Step 4.srt 4.0 kB
22. Hierarchical Clustering/13. HC in R - Step 4.srt 3.9 kB
5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.srt 3.6 kB
19. Evaluating Classification Models Performance/6. Conclusion of Part 3 - Classification.html 3.6 kB
29. Part 7 Natural Language Processing/19. Natural Language Processing in R - Step 5.srt 3.3 kB
1. Welcome to the course!/4. Important notes, tips & tricks for this course.html 3.3 kB
19. Evaluating Classification Models Performance/3. Accuracy Paradox.srt 3.3 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 2.8 kB
29. Part 7 Natural Language Processing/6. Natural Language Processing in Python - Step 3.srt 2.7 kB
32. Convolutional Neural Networks/6. Step 3 - Flattening.srt 2.6 kB
2. Part 1 Data Preprocessing/1. Welcome to Part 1 - Data Preprocessing.srt 2.6 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
39. XGBoost/5. THANK YOU bonus video.srt 2.4 kB
5. Multiple Linear Regression/15. Multiple Linear Regression in Python - Automatic Backward Elimination.html 2.2 kB
6. Polynomial Regression/3.1 polynomial_regression-updated.py 2.1 kB
2. Part 1 Data Preprocessing/5. For Python learners, summary of Object-oriented programming classes & objects.html 1.8 kB
1. Welcome to the course!/13. FAQBot!.html 1.8 kB
32. Convolutional Neural Networks/14. CNN in Python - Step 3.srt 1.8 kB
29. Part 7 Natural Language Processing/1. Welcome to Part 7 - Natural Language Processing.html 1.7 kB
2. Part 1 Data Preprocessing/7.1 categorical_data.py 1.7 kB
5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.srt 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/4. Multiple Linear Regression Intuition - Step 2.srt 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
12. Logistic Regression/13. Warning - Update.html 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
26. Part 6 Reinforcement Learning/1. Welcome to Part 6 - Reinforcement Learning.html 1.2 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 1.0 kB
2. Part 1 Data Preprocessing/6.1 missing_data.py 976 Bytes
32. Convolutional Neural Networks/11. Installing Keras.html 927 Bytes
37. Part 10 Model Selection & Boosting/1. Welcome to Part 10 - Model Selection & Boosting.html 899 Bytes
3. Part 2 Regression/1. Welcome to Part 2 - Regression.html 875 Bytes
30. Part 8 Deep Learning/1. Welcome to Part 8 - Deep Learning.html 870 Bytes
11. Part 3 Classification/1. Welcome to Part 3 - Classification.html 831 Bytes
20. Part 4 Clustering/1. Welcome to Part 4 - Clustering.html 734 Bytes
5. Multiple Linear Regression/21. Multiple Linear Regression in R - Automatic Backward Elimination.html 726 Bytes
5. Multiple Linear Regression/7. Prerequisites What is the P-Value.html 676 Bytes
6. Polynomial Regression/3. Polynomial Regression update for Python.html 609 Bytes
1. Welcome to the course!/12. Some Additional Resources.html 551 Bytes
22. Hierarchical Clustering/16. Conclusion of Part 4 - Clustering.html 516 Bytes
23. Part 5 Association Rule Learning/1. Welcome to Part 5 - Association Rule Learning.html 425 Bytes
12. Logistic Regression/16. Logistic Regression.html 125 Bytes
13. K-Nearest Neighbors (K-NN)/5. K-Nearest Neighbor.html 125 Bytes
2. Part 1 Data Preprocessing/12. Data Preprocessing.html 125 Bytes
21. K-Means Clustering/7. K-Means Clustering.html 125 Bytes
22. Hierarchical Clustering/15. Hierarchical Clustering.html 125 Bytes
4. Simple Linear Regression/13. Simple Linear Regression.html 125 Bytes
5. Multiple Linear Regression/22. Multiple Linear Regression.html 125 Bytes
[GigaCourse.com].url 49 Bytes

==查看完整文档列表==

上一个：fhd.whitney.westgate.25.02.14_950.mp4536.9 MB
下一个：111718_377-paco-mp4 2.6 GB

种子标签

种子评价

优质的种子 (0) 假种子 (0) 有密码 (0) 低质量 (0) 有病毒 (0) 无法下载 (0)
欢迎对种子质量进行评价。

人气女优

北川ゆい Akira COCOLO Saiko あいだももあさのくるみあまいれもんいしかわ愛里いとうしいなうさみ恭香うちだまひろかぐやひめかとりこのみかないかほくすのき琴美クミコグレースくらもとまい（葉月ありさ）さとみ中村あみしいな純菜しのざきさとみ（三沢亜也）牧本千幸（つかもと友希）眞木ありさデヴィテラパトリックドミニカともさかまいともさか愛なごみもえひなこ

分享BT种子/磁力链接

亲，你知道吗？下载的人越多速度越快，赶快把本页面分享给好友一起下载吧^_^

2048BT

BT种子名称

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

分享给好友

BT种子基本信息

下载磁力链接

下载BT种子

文档列表

猜你喜欢

种子标签

种子评价

最近搜索

人气女优

最新番号

分享BT种子/磁力链接

友情链接