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

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

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最近下载：2024-12-10

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文件列表

  • 37. Convolutional Neural Networks/10.1 Section 40 - Convolutional Neural Networks (CNN).zip 234.9 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
  • 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. -------------------- Part 7 Natural Language Processing --------------------/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!/10. 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. -------------------- Part 7 Natural Language Processing --------------------/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
  • 9. Support Vector Regression (SVR)/4. SVR in Python - Step 1.srt 89.3 MB
  • 16. Logistic Regression/4. Logistic Regression in Python - Step 2.mp4 88.8 MB
  • 34. -------------------- Part 7 Natural Language Processing --------------------/4. Classical vs Deep Learning Models.mp4 88.0 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. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 3.mp4 63.6 MB
  • 34. -------------------- Part 7 Natural Language Processing --------------------/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. -------------------- Part 7 Natural Language Processing --------------------/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. -------------------- Part 7 Natural Language Processing --------------------/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. -------------------- Part 7 Natural Language Processing --------------------/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 51.6 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 45.2 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. -------------------- Part 7 Natural Language Processing --------------------/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. -------------------- Part 7 Natural Language Processing --------------------/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. -------------------- Part 7 Natural Language Processing --------------------/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.srt 33.9 MB
  • 8. Polynomial Regression/8. Polynomial Regression in R - Step 2.mp4 33.9 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
  • 16. Logistic Regression/7. Logistic Regression in Python - Step 5.mp4 32.1 MB
  • 33. Thompson Sampling/4. Thompson Sampling in Python - Step 1.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.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!/11. 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. -------------------- Part 7 Natural Language Processing --------------------/3. Types of Natural Language Processing.mp4 23.6 MB
  • 7. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 23.0 MB
  • 34. -------------------- Part 7 Natural Language Processing --------------------/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
  • 1. Welcome to the course!/8. Updates on Udemy Reviews.mp4 21.4 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. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.mp4 18.1 MB
  • 34. -------------------- Part 7 Natural Language Processing --------------------/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. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.mp4 16.9 MB
  • 3. Data Preprocessing in Python/3. Importing the Libraries.mp4 16.8 MB
  • 16. Logistic Regression/10. Logistic Regression in R - Step 1.mp4 16.5 MB
  • 19. Kernel SVM/4. Types of Kernel Functions.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 16.1 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 14.5 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 14.4 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. -------------------- Part 7 Natural Language Processing --------------------/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
  • 4. Data Preprocessing in R/2. Getting Started.mp4 10.3 MB
  • 1. Welcome to the course!/1. Applications of Machine Learning.mp4 10.3 MB
  • 12. Evaluating Regression Models Performance/1. R-Squared Intuition.mp4 10.3 MB
  • 34. -------------------- Part 7 Natural Language Processing --------------------/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.mp4 9.0 MB
  • 34. -------------------- Part 7 Natural Language Processing --------------------/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. -------------------- Part 7 Natural Language Processing --------------------/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
  • 10. Decision Tree Regression/2.1 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
  • 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
  • 26. K-Means Clustering/4.1 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
  • 29. Apriori/2.1 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
  • 33. Thompson Sampling/3.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
  • 34. -------------------- Part 7 Natural Language Processing --------------------/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
  • 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
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  • 9. Support Vector Regression (SVR)/3.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
  • 1. Welcome to the course!/9.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
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  • 18. Support Vector Machine (SVM)/3.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
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  • 1. Welcome to the course!/7.1 Machine_Learning_A_Z_Q_A.pdf 2.4 MB
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  • 16. Logistic Regression/13. Logistic Regression in R - Step 4.srt 4.1 kB
  • 1. Welcome to the course!/15. Your Shortcut To Becoming A Better Data Scientist!.html 4.1 kB
  • 27. Hierarchical Clustering/12. Hierarchical Clustering in R - Step 4.srt 3.9 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
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  • 34. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.srt 3.3 kB
  • 1. Welcome to the course!/6. Important notes, tips & tricks for this course.html 3.3 kB
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  • 4. Data Preprocessing in R/4. Dataset Description.srt 3.3 kB
  • 1. Welcome to the course!/14. 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!/9. GET ALL THE CODES AND DATASETS HERE!.html 1.9 kB
  • 1. Welcome to the course!/8. Updates on Udemy Reviews.srt 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. -------------------- Part 7 Natural Language Processing --------------------/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 1.5 kB
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  • 1. Welcome to the course!/2. BONUS Learning Paths.html 1.4 kB
  • 34. -------------------- Part 7 Natural Language Processing --------------------/14. Homework Challenge.html 1.4 kB
  • 16. Logistic Regression/14. Warning - Update.html 1.4 kB
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  • 7. Multiple Linear Regression/14. Multiple Linear Regression in Python - BONUS.html 1.2 kB
  • 44. XGBoost/3. Model Selection and Boosting BONUS.html 1.2 kB
  • 6. Simple Linear Regression/8. Simple Linear Regression in Python - BONUS.html 1.1 kB
  • 34. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - BONUS.html 1.1 kB
  • 1. Welcome to the course!/12. BONUS Meet your instructors.html 1.1 kB
  • 36. Artificial Neural Networks/21. Deep Learning BONUS #1.html 1.0 kB
  • 23. Classification Model Selection in Python/1. Make sure you have this Model Selection folder ready.html 985 Bytes
  • 13. Regression Model Selection in Python/1. Make sure you have this Model Selection folder ready.html 973 Bytes
  • 37. Convolutional Neural Networks/17. Deep Learning BONUS #2.html 923 Bytes
  • 34. -------------------- Part 7 Natural Language Processing --------------------/26. BONUS NLP BERT.html 906 Bytes
  • 42. -------------------- Part 10 Model Selection & Boosting --------------------/1. Welcome to Part 10 - Model Selection & Boosting.html 899 Bytes
  • 5. -------------------- Part 2 Regression --------------------/1. Welcome to Part 2 - Regression.html 875 Bytes
  • 35. -------------------- Part 8 Deep Learning --------------------/1. Welcome to Part 8 - Deep Learning.html 870 Bytes
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  • 16. Logistic Regression/17. Machine Learning Regression and Classification BONUS.html 819 Bytes
  • 37. Convolutional Neural Networks/10. Make sure you have your dataset ready.html 797 Bytes
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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
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  • 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. -------------------- Part 7 Natural Language Processing --------------------/6. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
  • 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!/13. Some Additional Resources.html 556 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
  • 1. Welcome to the course!/3. BONUS #2 ML vs DL vs AI — What’s the Difference.html 521 Bytes
  • 1. Welcome to the course!/4. BONUS #3 Regression Types.html 521 Bytes
  • 27. Hierarchical Clustering/15. Conclusion of Part 4 - Clustering.html 516 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
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  • 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 125 Bytes
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  • 7. Multiple Linear Regression/21. Multiple Linear Regression.html 125 Bytes
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