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

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  • 36. Kernel PCA/3. Kernel PCA in R.mp4 56.57 MB
    1. Welcome to the course!/5. Updates on Udemy Reviews.mp4 52.92 MB
    12. Logistic Regression/13. Logistic Regression in R - Step 5.mp4 51.68 MB
    35. Linear Discriminant Analysis (LDA)/4. LDA in R.mp4 51.29 MB
    17. Decision Tree Classification/4. Decision Tree Classification in R.mp4 51.18 MB
    18. Random Forest Classification/4. Random Forest Classification in R.mp4 49.39 MB
    31. Artificial Neural Networks/13. ANN in Python - Step 2.mp4 48.1 MB
    39. XGBoost/4. XGBoost in R.mp4 47.27 MB
    27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.mp4 47.21 MB
    18. Random Forest Classification/3. Random Forest Classification in Python.mp4 47.16 MB
    32. Convolutional Neural Networks/20. CNN in Python - Step 9.mp4 46.86 MB
    7. Support Vector Regression (SVR)/2. SVR Intuition.mp4 46.59 MB
    7. Support Vector Regression (SVR)/3. SVR in Python.mp4 46.19 MB
    35. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4 45.42 MB
    8. Decision Tree Regression/4. Decision Tree Regression in R.mp4 44.38 MB
    16. Naive Bayes/1. Bayes Theorem.mp4 43.9 MB
    24. Apriori/5. Apriori in R - Step 3.mp4 43.84 MB
    38. Model Selection/3. k-Fold Cross Validation in R.mp4 43.63 MB
    6. Polynomial Regression/10. Polynomial Regression in R - Step 3.mp4 43.32 MB
    28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp4 43.13 MB
    6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp4 42.98 MB
    24. Apriori/3. Apriori in R - Step 1.mp4 42.88 MB
    32. Convolutional Neural Networks/7. Step 4 - Full Connection.mp4 42.74 MB
    12. Logistic Regression/7. Logistic Regression in Python - Step 5.mp4 42.56 MB
    15. Kernel SVM/6. Kernel SVM in Python.mp4 41.63 MB
    13. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp4 41.38 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.mp4 41.19 MB
    27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp4 41.12 MB
    28. Thompson Sampling/6. Thompson Sampling in R - Step 1.mp4 40.94 MB
    2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.mp4 40.8 MB
    15. Kernel SVM/7. Kernel SVM in R.mp4 40.45 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.mp4 40.37 MB
    9. Random Forest Regression/4. Random Forest Regression in R.mp4 40.35 MB
    32. Convolutional Neural Networks/5. Step 2 - Pooling.mp4 40.24 MB
    21. K-Means Clustering/5. K-Means Clustering in Python.mp4 39.77 MB
    5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 39.73 MB
    5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp4 39.57 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 8.mp4 39.49 MB
    9. Random Forest Regression/3. Random Forest Regression in Python.mp4 39.48 MB
    2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.mp4 39.04 MB
    31. Artificial Neural Networks/22. ANN in R - Step 1.mp4 38.55 MB
    38. Model Selection/4. Grid Search in Python - Step 1.mp4 38.22 MB
    24. Apriori/6. Apriori in Python - Step 1.mp4 37.98 MB
    4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp4 37.37 MB
    16. Naive Bayes/7. Naive Bayes in R.mp4 37.32 MB
    28. Thompson Sampling/1. Thompson Sampling Intuition.mp4 37.28 MB
    34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.mp4 36.74 MB
    38. Model Selection/6. Grid Search in R.mp4 35.55 MB
    27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp4 35.45 MB
    13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp4 35.22 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/4. Natural Language Processing in Python - Step 1.mp4 35.21 MB
    24. Apriori/1. Apriori Intuition.mp4 35.03 MB
    2. -------------------- Part 1 Data Preprocessing --------------------/10. Feature Scaling.mp4 34.62 MB
    8. Decision Tree Regression/3. Decision Tree Regression in Python.mp4 33.55 MB
    31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).mp4 33.44 MB
    36. Kernel PCA/2. Kernel PCA in Python.mp4 33.39 MB
    32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp4 33.23 MB
    38. Model Selection/2. k-Fold Cross Validation in Python.mp4 32.83 MB
    5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp4 32.59 MB
    14. Support Vector Machine (SVM)/4. SVM in R.mp4 32.27 MB
    2. -------------------- Part 1 Data Preprocessing --------------------/6. Missing Data.mp4 32.16 MB
    34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp4 32.12 MB
    39. XGBoost/3. XGBoost in Python - Step 2.mp4 31.97 MB
    34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp4 31.96 MB
    27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp4 31.54 MB
    30. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.mp4 31.32 MB
    14. Support Vector Machine (SVM)/3. SVM in Python.mp4 31.17 MB
    32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp4 31.03 MB
    4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.mp4 30.82 MB
    34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.mp4 30.66 MB
    24. Apriori/4. Apriori in R - Step 2.mp4 30.5 MB
    27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp4 30.19 MB
    31. Artificial Neural Networks/2. The Neuron.mp4 29.86 MB
    17. Decision Tree Classification/3. Decision Tree Classification in Python.mp4 29.8 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/2. Natural Language Processing Intuition.mp4 29.69 MB
    31. Artificial Neural Networks/16. ANN in Python - Step 5.mp4 29.58 MB
    24. Apriori/7. Apriori in Python - Step 2.mp4 29.53 MB
    38. Model Selection/5. Grid Search in Python - Step 2.mp4 29.51 MB
    32. Convolutional Neural Networks/2. What are convolutional neural networks.mp4 29.51 MB
    27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp4 29.33 MB
    31. Artificial Neural Networks/12. ANN in Python - Step 1.mp4 29.31 MB
    15. Kernel SVM/3. The Kernel Trick.mp4 29.28 MB
    12. Logistic Regression/1. Logistic Regression Intuition.mp4 29.18 MB
    34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.mp4 29.02 MB
    27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.mp4 29.01 MB
    21. K-Means Clustering/6. K-Means Clustering in R.mp4 29 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.mp4 28.99 MB
    31. Artificial Neural Networks/24. ANN in R - Step 3.mp4 28.94 MB
    5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.mp4 28.83 MB
    27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.mp4 28.05 MB
    16. Naive Bayes/2. Naive Bayes Intuition.mp4 27.79 MB
    6. Polynomial Regression/7. Python Regression Template.mp4 27.43 MB
    32. Convolutional Neural Networks/15. CNN in Python - Step 4.mp4 27.18 MB
    5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 27.18 MB
    6. Polynomial Regression/4. Polynomial Regression in Python - Step 2.mp4 27.11 MB
    35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp4 26.99 MB
    24. Apriori/8. Apriori in Python - Step 3.mp4 26.96 MB
    21. K-Means Clustering/1. K-Means Clustering Intuition.mp4 26.87 MB
    31. Artificial Neural Networks/5. How do Neural Networks learn.mp4 26.56 MB
    5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.mp4 25.93 MB
    7. Support Vector Regression (SVR)/4. SVR in R.mp4 25.87 MB
    34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.mp4 25.51 MB
    6. Polynomial Regression/12. R Regression Template.mp4 25.42 MB
    32. Convolutional Neural Networks/12. CNN in Python - Step 1.mp4 24.93 MB
    6. Polynomial Regression/3. Polynomial Regression in Python - Step 1.mp4 24.9 MB
    10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.mp4 24.21 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - Step 10.mp4 24.13 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 4.mp4 24 MB
    6. Polynomial Regression/9. Polynomial Regression in R - Step 2.mp4 23.88 MB
    5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 23.83 MB
    31. Artificial Neural Networks/4. How do Neural Networks work.mp4 23.54 MB
    16. Naive Bayes/6. Naive Bayes in Python.mp4 23.39 MB
    2. -------------------- Part 1 Data Preprocessing --------------------/4. Importing the Dataset.mp4 23.31 MB
    21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp4 23.13 MB
    22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.mp4 22.82 MB
    8. Decision Tree Regression/1. Decision Tree Regression Intuition.mp4 22.7 MB
    6. Polynomial Regression/11. Polynomial Regression in R - Step 4.mp4 22.34 MB
    34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp4 22.08 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/5. Natural Language Processing in Python - Step 2.mp4 21.96 MB
    10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.mp4 21.89 MB
    4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.mp4 21.73 MB
    39. XGBoost/2. XGBoost in Python - Step 1.mp4 21.39 MB
    2. -------------------- Part 1 Data Preprocessing --------------------/2. Get the dataset.mp4 21.15 MB
    25. Eclat/3. Eclat in R.mp4 20.68 MB
    32. Convolutional Neural Networks/21. CNN in Python - Step 10.mp4 20.6 MB
    2. -------------------- Part 1 Data Preprocessing --------------------/11. And here is our Data Preprocessing Template!.mp4 19.67 MB
    1. Welcome to the course!/6. Installing Python and Anaconda (Mac, Linux & Windows).mp4 19.52 MB
    18. Random Forest Classification/1. Random Forest Classification Intuition.mp4 19.44 MB
    10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp4 19.29 MB
    16. Naive Bayes/4. Naive Bayes Intuition (Extras).mp4 18.94 MB
    17. Decision Tree Classification/1. Decision Tree Classification Intuition.mp4 18.8 MB
    4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.mp4 18.75 MB
    19. Evaluating Classification Models Performance/4. CAP Curve.mp4 18.68 MB
    31. Artificial Neural Networks/6. Gradient Descent.mp4 18.54 MB
    31. Artificial Neural Networks/19. ANN in Python - Step 8.mp4 18.16 MB
    14. Support Vector Machine (SVM)/1. SVM Intuition.mp4 18.01 MB
    5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.mp4 17.94 MB
    6. Polynomial Regression/8. Polynomial Regression in R - Step 1.mp4 17.78 MB
    1. Welcome to the course!/8. Installing R and R Studio (Mac, Linux & Windows).mp4 17.55 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.mp4 17.47 MB
    22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.mp4 17.46 MB
    5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 17.25 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 7.mp4 17.11 MB
    31. Artificial Neural Networks/21. ANN in Python - Step 10.mp4 17.09 MB
    31. Artificial Neural Networks/20. ANN in Python - Step 9.mp4 16.89 MB
    31. Artificial Neural Networks/7. Stochastic Gradient Descent.mp4 16.83 MB
    22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.mp4 16.53 MB
    31. Artificial Neural Networks/10. Business Problem Description.mp4 16.37 MB
    4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.mp4 15.61 MB
    21. K-Means Clustering/2. K-Means Random Initialization Trap.mp4 15.37 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 5.mp4 14.9 MB
    31. Artificial Neural Networks/3. The Activation Function.mp4 14.76 MB
    12. Logistic Regression/11. Logistic Regression in R - Step 3.mp4 14.59 MB
    4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp4 14.36 MB
    5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp4 14.3 MB
    5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.mp4 14.28 MB
    31. Artificial Neural Networks/23. ANN in R - Step 2.mp4 14.18 MB
    32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp4 14.09 MB
    28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp4 14.09 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 9.mp4 14.01 MB
    9. Random Forest Regression/1. Random Forest Regression Intuition.mp4 13.82 MB
    15. Kernel SVM/2. Mapping to a higher dimension.mp4 13.74 MB
    19. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp4 13.65 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.mp4 13.52 MB
    6. Polynomial Regression/6. Polynomial Regression in Python - Step 4.mp4 13.5 MB
    5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.vtt 13.46 MB
    16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp4 13.28 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.mp4 13.27 MB
    32. Convolutional Neural Networks/18. CNN in Python - Step 7.mp4 12.93 MB
    12. Logistic Regression/3. Logistic Regression in Python - Step 1.mp4 12.93 MB
    1. Welcome to the course!/2. Why Machine Learning is the Future.mp4 12.82 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.mp4 12.73 MB
    22. Hierarchical Clustering/6. HC in Python - Step 2.mp4 12.65 MB
    12. Logistic Regression/9. Logistic Regression in R - Step 1.mp4 12.58 MB
    12. Logistic Regression/14. R Classification Template.mp4 12.47 MB
    22. Hierarchical Clustering/7. HC in Python - Step 3.mp4 12.31 MB
    15. Kernel SVM/4. Types of Kernel Functions.mp4 12.3 MB
    12. Logistic Regression/8. Python Classification Template.mp4 12.07 MB
    22. Hierarchical Clustering/8. HC in Python - Step 4.mp4 12.02 MB
    14. Support Vector Machine (SVM)/2. How to get the dataset.mp4 11.72 MB
    18. Random Forest Classification/2. How to get the dataset.mp4 11.72 MB
    27. Upper Confidence Bound (UCB)/3. How to get the dataset.mp4 11.72 MB
    28. Thompson Sampling/3. How to get the dataset.mp4 11.72 MB
    32. Convolutional Neural Networks/10. How to get the dataset.mp4 11.72 MB
    34. Principal Component Analysis (PCA)/2. How to get the dataset.mp4 11.72 MB
    36. Kernel PCA/1. How to get the dataset.mp4 11.72 MB
    39. XGBoost/1. How to get the dataset.mp4 11.72 MB
    4. Simple Linear Regression/1. How to get the dataset.mp4 11.72 MB
    6. Polynomial Regression/2. How to get the dataset.mp4 11.72 MB
    7. Support Vector Regression (SVR)/1. How to get the dataset.mp4 11.72 MB
    8. Decision Tree Regression/2. How to get the dataset.mp4 11.72 MB
    9. Random Forest Regression/2. How to get the dataset.mp4 11.72 MB
    12. Logistic Regression/2. How to get the dataset.mp4 11.71 MB
    13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.mp4 11.71 MB
    15. Kernel SVM/5. How to get the dataset.mp4 11.71 MB
    16. Naive Bayes/5. How to get the dataset.mp4 11.71 MB
    17. Decision Tree Classification/2. How to get the dataset.mp4 11.71 MB
    21. K-Means Clustering/4. How to get the dataset.mp4 11.71 MB
    22. Hierarchical Clustering/4. How to get the dataset.mp4 11.71 MB
    24. Apriori/2. How to get the dataset.mp4 11.71 MB
    25. Eclat/2. How to get the dataset.mp4 11.71 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/3. How to get the dataset.mp4 11.71 MB
    31. Artificial Neural Networks/9. How to get the dataset.mp4 11.71 MB
    35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.mp4 11.71 MB
    38. Model Selection/1. How to get the dataset.mp4 11.71 MB
    5. Multiple Linear Regression/1. How to get the dataset.mp4 11.71 MB
    19. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp4 11.51 MB
    22. Hierarchical Clustering/11. HC in R - Step 2.mp4 11.15 MB
    2. -------------------- Part 1 Data Preprocessing --------------------/3. Importing the Libraries.mp4 11.07 MB
    31. Artificial Neural Networks/8. Backpropagation.mp4 10.93 MB
    22. Hierarchical Clustering/5. HC in Python - Step 1.mp4 10.73 MB
    25. Eclat/1. Eclat Intuition.mp4 10.65 MB
    5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.mp4 10.42 MB
    12. Logistic Regression/6. Logistic Regression in Python - Step 4.mp4 10.37 MB
    5. Multiple Linear Regression/2. Dataset + Business Problem Description.mp4 9.98 MB
    32. Convolutional Neural Networks/16. CNN in Python - Step 5.mp4 9.91 MB
    32. Convolutional Neural Networks/17. CNN in Python - Step 6.mp4 9.71 MB
    4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp4 9.53 MB
    4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.mp4 9.47 MB
    6. Polynomial Regression/1. Polynomial Regression Intuition.mp4 9.44 MB
    13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp4 9.27 MB
    27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp4 9.13 MB
    31. Artificial Neural Networks/18. ANN in Python - Step 7.mp4 8.99 MB
    10. Evaluating Regression Models Performance/1. R-Squared Intuition.mp4 8.86 MB
    4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp4 8.64 MB
    28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp4 8.41 MB
    22. Hierarchical Clustering/9. HC in Python - Step 5.mp4 8.4 MB
    31. Artificial Neural Networks/14. ANN in Python - Step 3.mp4 8.37 MB
    12. Logistic Regression/4. Logistic Regression in Python - Step 2.mp4 8.23 MB
    19. Evaluating Classification Models Performance/2. Confusion Matrix.mp4 8.21 MB
    1. Welcome to the course!/1. Applications of Machine Learning.mp4 7.99 MB
    32. Convolutional Neural Networks/8. Summary.mp4 7.91 MB
    12. Logistic Regression/10. Logistic Regression in R - Step 2.mp4 7.84 MB
    22. Hierarchical Clustering/12. HC in R - Step 3.mp4 7.81 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.mp4 7.51 MB
    28. Thompson Sampling/7. Thompson Sampling in R - Step 2.mp4 7.47 MB
    22. Hierarchical Clustering/13. HC in R - Step 4.mp4 7.44 MB
    27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.mp4 7.41 MB
    22. Hierarchical Clustering/10. HC in R - Step 1.mp4 7.38 MB
    5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp4 7.22 MB
    31. Artificial Neural Networks/17. ANN in Python - Step 6.mp4 7.06 MB
    12. Logistic Regression/12. Logistic Regression in R - Step 4.mp4 6.9 MB
    22. Hierarchical Clustering/14. HC in R - Step 5.mp4 6.88 MB
    32. Convolutional Neural Networks/19. CNN in Python - Step 8.mp4 6.79 MB
    4. Simple Linear Regression/2. Dataset + Business Problem Description.mp4 6.63 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.mp4 6.51 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 6.mp4 6.49 MB
    12. Logistic Regression/5. Logistic Regression in Python - Step 3.mp4 5.97 MB
    32. Convolutional Neural Networks/1. Plan of attack.mp4 5.9 MB
    31. Artificial Neural Networks/15. ANN in Python - Step 4.mp4 5.88 MB
    32. Convolutional Neural Networks/13. CNN in Python - Step 2.mp4 5.85 MB
    15. Kernel SVM/1. Kernel SVM Intuition.mp4 5.79 MB
    4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.mp4 5.37 MB
    31. Artificial Neural Networks/1. Plan of attack.mp4 4.74 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.mp4 4.57 MB
    5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.mp4 4.52 MB
    19. Evaluating Classification Models Performance/3. Accuracy Paradox.mp4 3.8 MB
    29. -------------------- Part 7 Natural Language Processing --------------------/6. Natural Language Processing in Python - Step 3.mp4 3.39 MB
    32. Convolutional Neural Networks/6. Step 3 - Flattening.mp4 3.28 MB
    2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.mp4 2.98 MB
    1. Welcome to the course!/4.1 Machine_Learning_A_Z_Q_A.pdf.pdf 2.26 MB
    32. Convolutional Neural Networks/14. CNN in Python - Step 3.mp4 2.22 MB
    5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.mp4 1.82 MB
    5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.mp4 1.78 MB
    25. Eclat/3.1 Eclat.zip.zip 48.54 KB
    16. Naive Bayes/1. Bayes Theorem.vtt 30.66 KB
    18. Random Forest Classification/4. Random Forest Classification in R.vtt 28.85 KB
    8. Decision Tree Regression/4. Decision Tree Regression in R.vtt 28.54 KB
    6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.vtt 27.83 KB
    24. Apriori/5. Apriori in R - Step 3.vtt 27.72 KB
    24. Apriori/3. Apriori in R - Step 1.vtt 27.66 KB
    7. Support Vector Regression (SVR)/3. SVR in Python.vtt 27.45 KB
    6. Polynomial Regression/10. Polynomial Regression in R - Step 3.vtt 27.44 KB
    18. Random Forest Classification/3. Random Forest Classification in Python.vtt 27.43 KB
    36. Kernel PCA/3. Kernel PCA in R.vtt 26.63 KB
    12. Logistic Regression/7. Logistic Regression in Python - Step 5.vtt 26.48 KB
    12. Logistic Regression/13. Logistic Regression in R - Step 5.vtt 26 KB
    17. Decision Tree Classification/4. Decision Tree Classification in R.vtt 25.86 KB
    35. Linear Discriminant Analysis (LDA)/4. LDA in R.vtt 25.61 KB
    32. Convolutional Neural Networks/20. CNN in Python - Step 9.vtt 25.49 KB
    21. K-Means Clustering/5. K-Means Clustering in Python.vtt 25.22 KB
    28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.vtt 25.19 KB
    9. Random Forest Regression/4. Random Forest Regression in R.vtt 25.1 KB
    32. Convolutional Neural Networks/7. Step 4 - Full Connection.vtt 25.08 KB
    15. Kernel SVM/6. Kernel SVM in Python.vtt 24.98 KB
    24. Apriori/6. Apriori in Python - Step 1.vtt 24.86 KB
    31. Artificial Neural Networks/13. ANN in Python - Step 2.vtt 24.77 KB
    5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.vtt 24.57 KB
    9. Random Forest Regression/3. Random Forest Regression in Python.vtt 24.45 KB
    28. Thompson Sampling/6. Thompson Sampling in R - Step 1.vtt 24.31 KB
    38. Model Selection/3. k-Fold Cross Validation in R.vtt 24.24 KB
    28. Thompson Sampling/1. Thompson Sampling Intuition.vtt 24.09 KB
    2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.vtt 23.91 KB
    2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.vtt 23.86 KB
    27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.vtt 23.38 KB
    35. Linear Discriminant Analysis (LDA)/3. LDA in Python.vtt 23.05 KB
    31. Artificial Neural Networks/22. ANN in R - Step 1.vtt 23.03 KB
    29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.vtt 22.89 KB
    15. Kernel SVM/7. Kernel SVM in R.vtt 22.66 KB
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    1. Welcome to the course!/9. BONUS Meet your instructors.html 1.04 KB
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    37. -------------------- Part 10 Model Selection & Boosting --------------------/1. Welcome to Part 10 - Model Selection & Boosting.html 899 B
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    23. -------------------- Part 5 Association Rule Learning --------------------/1. Welcome to Part 5 - Association Rule Learning.html 425 B
    [FCS Forum].url 133 B
    [FreeCourseSite.com].url 127 B
    [CourseClub.NET].url 123 B
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    5. Multiple Linear Regression/22. Multiple Linear Regression.html 121 B

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