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Название 2021 Python for Data Science & Machine Learning from A-Z
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[TGx]Downloaded from torrentgalaxy.to .txt 585б
0 1.83Кб
1 433б
1.1 NumPy Basics.pdf 77.10Кб
1.1 Pandas.pdf 110.18Кб
1.1 Supervised Learning.pdf 836.74Кб
1.1 Unsupervised Learning.pdf 636.55Кб
1.2 Pandas Basics.pdf 77.06Кб
1. Creating A Data Science Resume.mp4 37.08Мб
1. Creating A Data Science Resume.srt 10.52Кб
1. Data Visualization Overview.mp4 73.08Мб
1. Data Visualization Overview.srt 36.78Кб
1. Decision Trees Section Overview.mp4 16.46Мб
1. Decision Trees Section Overview.srt 5.60Кб
1. Ensemble Learning Section Overview.mp4 16.07Мб
1. Ensemble Learning Section Overview.srt 5.17Кб
1. Exploratory Data Analysis.mp4 50.56Мб
1. Exploratory Data Analysis.srt 19.07Кб
1. Feature Engineering.mp4 18.41Мб
1. Feature Engineering.srt 9.42Кб
1. Feature Scaling.mp4 19.38Мб
1. Feature Scaling.srt 11.58Кб
1. Introduction To Machine Learning.mp4 98.71Мб
1. Introduction To Machine Learning.srt 36.98Кб
1. Introduction to Pandas.mp4 46.83Мб
1. Introduction to Pandas.srt 22.30Кб
1. Intro NumPy Array Data Types.mp4 34.67Мб
1. Intro NumPy Array Data Types.srt 18.29Кб
1. Intro To Statistics.mp4 21.24Мб
1. Intro To Statistics.srt 11.17Кб
1. KNN Overview.mp4 12.89Мб
1. KNN Overview.srt 4.27Кб
1. Linear Regression Intro.mp4 30.80Мб
1. Linear Regression Intro.srt 12.16Кб
1. PCA Section Overview.mp4 31.77Мб
1. PCA Section Overview.srt 7.06Кб
1. SVM Outline.mp4 35.31Мб
1. SVM Outline.srt 7.40Кб
1. Unsupervised Machine Learning Intro.mp4 100.92Мб
1. Unsupervised Machine Learning Intro.srt 29.36Кб
1. What is Exactly is Probability.mp4 27.17Мб
1. What is Exactly is Probability.srt 6.73Кб
1. What is Programming.mp4 18.35Мб
1. What is Programming.srt 9.03Кб
1. Who is This Course For.mp4 17.16Мб
1. Who is This Course For.srt 3.95Кб
1. Why We Use Python.mp4 13.51Мб
1. Why We Use Python.srt 4.91Кб
10 60.42Кб
10. Feature scaling in KNN.mp4 49.39Мб
10. Feature scaling in KNN.srt 8.06Кб
10. PCA - Feature Scaling and Screen Plot.mp4 68.20Мб
10. PCA - Feature Scaling and Screen Plot.srt 14.40Кб
10. Python Conditional Statements.mp4 54.61Мб
10. Python Conditional Statements.srt 18.15Кб
10. Random Forests Pros and Cons.mp4 19.69Мб
10. Random Forests Pros and Cons.srt 7.85Кб
10. SMV - Project Overview.mp4 39.61Мб
10. SMV - Project Overview.srt 6.23Кб
10. Visualizing the tree.mp4 68.17Мб
10. Visualizing the tree.srt 15.00Кб
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11. Curse of dimensionality.mp4 45.99Мб
11. Curse of dimensionality.srt 9.60Кб
11. PCA - Supervised vs Unsupervised.mp4 35.79Мб
11. PCA - Supervised vs Unsupervised.srt 7.12Кб
11. Plot the features importance.mp4 31.67Мб
11. Plot the features importance.srt 7.75Кб
11. Python For Loops and While Loops.mp4 25.61Мб
11. Python For Loops and While Loops.srt 10.74Кб
11. What is Boosting.mp4 35.44Мб
11. What is Boosting.srt 6.86Кб
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12. AdaBoost Part 1.mp4 25.53Мб
12. AdaBoost Part 1.srt 5.50Кб
12. Decision Trees Hyper-parameters.mp4 81.27Мб
12. Decision Trees Hyper-parameters.srt 16.12Кб
12. KNN use cases.mp4 28.92Мб
12. KNN use cases.srt 4.85Кб
12. PCA - Visualization.mp4 68.02Мб
12. PCA - Visualization.srt 10.96Кб
12. Python Lists.mp4 21.44Мб
12. Python Lists.srt 7.12Кб
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13. AdaBoost Part 2.mp4 85.94Мб
13. AdaBoost Part 2.srt 20.87Кб
13. KNN pros and cons.mp4 30.45Мб
13. KNN pros and cons.srt 7.75Кб
13. More about Lists.mp4 60.42Мб
13. More about Lists.srt 19.44Кб
13. Pruning.mp4 112.97Мб
13. Pruning.srt 24.35Кб
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14. [Optional] Gain Ration.mp4 19.18Мб
14. [Optional] Gain Ration.srt 3.70Кб
14. Python Tuples.mp4 54.53Мб
14. Python Tuples.srt 14.96Кб
15 391.78Кб
15. Decision Trees Pros and Cons.mp4 47.74Мб
15. Decision Trees Pros and Cons.srt 10.60Кб
15. Python Dictionaries.mp4 104.18Мб
15. Python Dictionaries.srt 27.72Кб
16 841.00Кб
16. [Project] Predict whether income exceeds $50Kyr - Overview.mp4 15.11Мб
16. [Project] Predict whether income exceeds $50Kyr - Overview.srt 3.60Кб
16. Python Sets.mp4 29.43Мб
16. Python Sets.srt 13.48Кб
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17. Compound Data Types & When to use each one.mp4 47.07Мб
17. Compound Data Types & When to use each one.srt 17.98Кб
18 77.60Кб
18. Python Functions.mp4 62.51Мб
18. Python Functions.srt 20.84Кб
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19. Object Oriented Programming in Python.mp4 70.25Мб
19. Object Oriented Programming in Python.srt 25.73Кб
2 45б
2.1 Importing Python Data.pdf 61.55Кб
2.2 Python Basics.pdf 127.71Кб
2. Data Cleaning.mp4 30.21Мб
2. Data Cleaning.srt 11.53Кб
2. Data Science + Machine Learning Marketplace.mp4 46.94Мб
2. Data Science + Machine Learning Marketplace.srt 10.40Кб
2. Data Science Cover Letter.mp4 22.96Мб
2. Data Science Cover Letter.srt 5.99Кб
2. Descriptive Statistics.mp4 21.48Мб
2. Descriptive Statistics.srt 10.04Кб
2. Different Data Visualization Libraries in Python.mp4 15.95Мб
2. Different Data Visualization Libraries in Python.srt 8.76Кб
2. EDA on Adult Dataset.mp4 123.19Мб
2. EDA on Adult Dataset.srt 23.77Кб
2. Expected Values.mp4 14.72Мб
2. Expected Values.srt 4.11Кб
2. Gradient Descent.mp4 15.93Мб
2. Gradient Descent.srt 8.44Кб
2. Introduction to Pandas Continued.mp4 71.10Мб
2. Introduction to Pandas Continued.srt 26.81Кб
2. NumPy Arrays.mp4 32.33Мб
2. NumPy Arrays.srt 11.09Кб
2. parametric vs non-parametric models.mp4 15.63Мб
2. parametric vs non-parametric models.srt 4.73Кб
2. SVM intuition.mp4 48.86Мб
2. SVM intuition.srt 16.01Кб
2. Unsupervised Machine Learning Continued.mp4 83.13Мб
2. Unsupervised Machine Learning Continued.srt 29.19Кб
2. What is Data Science.mp4 87.99Мб
2. What is Data Science.srt 21.23Кб
2. What is Ensemble Learning.mp4 91.97Мб
2. What is Ensemble Learning.srt 17.42Кб
2. What is PCA.mp4 47.26Мб
2. What is PCA.srt 14.59Кб
2. Why Python for Data Science.mp4 16.32Мб
2. Why Python for Data Science.srt 6.77Кб
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3.1 Jupyter Notebook.pdf 307.15Кб
3. Data Science Job Opportunities.mp4 29.42Мб
3. Data Science Job Opportunities.srt 6.86Кб
3. EDA on Iris Dataset.mp4 161.88Мб
3. EDA on Iris Dataset.srt 31.65Кб
3. Hard vs Soft Margins.mp4 65.64Мб
3. Hard vs Soft Margins.srt 18.92Кб
3. How to Contact Recruiters.mp4 24.65Мб
3. How to Contact Recruiters.srt 7.34Кб
3. Linear Regression + Correlation Methods.mp4 110.38Мб
3. Linear Regression + Correlation Methods.srt 38.61Кб
3. Measure of Variability.mp4 38.20Мб
3. Measure of Variability.srt 18.19Кб
3. NumPy Arrays Basics.mp4 39.98Мб
3. NumPy Arrays Basics.srt 16.84Кб
3. PCA Drawbacks.mp4 19.44Мб
3. PCA Drawbacks.srt 4.90Кб
3. Python Data Visualization Implementation.mp4 27.43Мб
3. Python Data Visualization Implementation.srt 12.44Кб
3. Relative Frequency.mp4 32.69Мб
3. Relative Frequency.srt 8.67Кб
3. Representing Clusters.mp4 109.62Мб
3. Representing Clusters.srt 28.32Кб
3. What is Bootstrap Sampling.mp4 55.88Мб
3. What is Bootstrap Sampling.srt 11.13Кб
3. What is Entropy and Information Gain.mp4 136.08Мб
3. What is Entropy and Information Gain.srt 29.33Кб
3. What is Jupyter.mp4 14.57Мб
3. What is Jupyter.srt 5.92Кб
3. What is Machine Learning.mp4 83.41Мб
3. What is Machine Learning.srt 22.93Кб
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4. C hyper-parameter.mp4 21.06Мб
4. C hyper-parameter.srt 5.64Кб
4. Data Science Job Roles.mp4 79.80Мб
4. Data Science Job Roles.srt 15.73Кб
4. Getting Started with Freelancing.mp4 30.24Мб
4. Getting Started with Freelancing.srt 7.08Кб
4. Hypothesis Testing Overview.mp4 60.59Мб
4. Hypothesis Testing Overview.srt 14.58Кб
4. Linear Regression Implementation.mp4 17.86Мб
4. Linear Regression Implementation.srt 6.88Кб
4. Machine Learning Concepts & Algorithms.mp4 77.98Мб
4. Machine Learning Concepts & Algorithms.srt 23.56Кб
4. Measure of Variability Continued.mp4 34.61Мб
4. Measure of Variability Continued.srt 13.32Кб
4. NumPy Array Indexing.mp4 34.74Мб
4. NumPy Array Indexing.srt 14.02Кб
4. PCA Algorithm Steps (Mathematics).mp4 57.73Мб
4. PCA Algorithm Steps (Mathematics).srt 18.59Кб
4. The Decision Tree ID3 algorithm from scratch Part 1.mp4 85.27Мб
4. The Decision Tree ID3 algorithm from scratch Part 1.srt 14.91Кб
4. The KNN Intuition.mp4 8.09Мб
4. The KNN Intuition.srt 3.06Кб
4. What is Bagging.mp4 29.48Мб
4. What is Bagging.srt 7.75Кб
4. What is Google Colab.mp4 8.26Мб
4. What is Google Colab.srt 4.89Кб
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5. Covariance Matrix vs SVD.mp4 38.74Мб
5. Covariance Matrix vs SVD.srt 6.54Кб
5. Implement the KNN algorithm from scratch.mp4 86.97Мб
5. Implement the KNN algorithm from scratch.srt 17.14Кб
5. Kernel Trick.mp4 77.05Мб
5. Kernel Trick.srt 18.19Кб
5. Logistic Regression.mp4 8.90Мб
5. Logistic Regression.srt 4.93Кб
5. Measures of Variable Relationship.mp4 23.57Мб
5. Measures of Variable Relationship.srt 10.72Кб
5. NumPy Array Computations.mp4 16.96Мб
5. NumPy Array Computations.srt 8.60Кб
5. Out-of-Bag Error (OOB Error).mp4 42.03Мб
5. Out-of-Bag Error (OOB Error).srt 9.97Кб
5. Python Variables, Booleans and None.mp4 38.26Мб
5. Python Variables, Booleans and None.srt 15.22Кб
5. The Decision Tree ID3 algorithm from scratch Part 2.mp4 63.96Мб
5. The Decision Tree ID3 algorithm from scratch Part 2.srt 10.62Кб
5. Top Freelance Websites.mp4 29.55Мб
5. Top Freelance Websites.srt 8.37Кб
5. What is a Data Scientist.mp4 127.47Мб
5. What is a Data Scientist.srt 26.86Кб
5. What is Deep Learning.mp4 77.81Мб
5. What is Deep Learning.srt 15.83Кб
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6. Broadcasting.mp4 17.86Мб
6. Broadcasting.srt 6.36Кб
6. Compare the result with the sklearn library.mp4 24.57Мб
6. Compare the result with the sklearn library.srt 5.10Кб
6. Getting Started with Google Colab.mp4 35.09Мб
6. Getting Started with Google Colab.srt 12.39Кб
6. How To Get a Data Science Job.mp4 131.19Мб
6. How To Get a Data Science Job.srt 30.62Кб
6. Implementing Random Forests from scratch Part 1.mp4 202.55Мб
6. Implementing Random Forests from scratch Part 1.srt 30.12Кб
6. Inferential Statistics.mp4 45.01Мб
6. Inferential Statistics.srt 22.10Кб
6. Machine Learning vs Deep Learning.mp4 75.92Мб
6. Machine Learning vs Deep Learning.srt 17.90Кб
6. PCA - Main Applications.mp4 10.05Мб
6. PCA - Main Applications.srt 3.90Кб
6. Personal Branding.mp4 30.49Мб
6. Personal Branding.srt 6.42Кб
6. SVM - Kernel Types.mp4 126.38Мб
6. SVM - Kernel Types.srt 26.75Кб
6. The Decision Tree ID3 algorithm from scratch Part 3.mp4 33.41Мб
6. The Decision Tree ID3 algorithm from scratch Part 3.srt 5.75Кб
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7. Data Science Projects Overview.mp4 79.48Мб
7. Data Science Projects Overview.srt 19.09Кб
7. Hyperparameter tuning using the cross-validation.mp4 90.30Мб
7. Hyperparameter tuning using the cross-validation.srt 14.66Кб
7. ID3 - Putting Everything Together.mp4 182.48Мб
7. ID3 - Putting Everything Together.srt 31.03Кб
7. Implementing Random Forests from scratch Part 2.mp4 50.50Мб
7. Implementing Random Forests from scratch Part 2.srt 8.27Кб
7. Measure of Asymmetry.mp4 6.76Мб
7. Measure of Asymmetry.srt 2.75Кб
7. Networking Do's and Don'ts.mp4 23.70Мб
7. Networking Do's and Don'ts.srt 6.28Кб
7. PCA - Image Compression.mp4 249.92Мб
7. PCA - Image Compression.srt 39.37Кб
7. Python Operators.mp4 86.76Мб
7. Python Operators.srt 31.44Кб
7. SVM with Linear Dataset (Iris).mp4 101.56Мб
7. SVM with Linear Dataset (Iris).srt 19.85Кб
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8. Compare with sklearn implementation.mp4 27.65Мб
8. Compare with sklearn implementation.srt 4.94Кб
8. Evaluating our ID3 implementation.mp4 121.94Мб
8. Evaluating our ID3 implementation.srt 24.54Кб
8. Importance of a Website.mp4 15.37Мб
8. Importance of a Website.srt 4.79Кб
8. PCA Data Preprocessing.mp4 120.46Мб
8. PCA Data Preprocessing.srt 21.05Кб
8. Python Numbers & Booleans.mp4 25.61Мб
8. Python Numbers & Booleans.srt 9.60Кб
8. Sampling Distribution.mp4 26.46Мб
8. Sampling Distribution.srt 10.25Кб
8. SVM with Non-linear Dataset.mp4 111.55Мб
8. SVM with Non-linear Dataset.srt 18.38Кб
8. The decision boundary visualization.mp4 16.94Мб
8. The decision boundary visualization.srt 7.07Кб
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9. Compare with Sklearn implementation.mp4 65.58Мб
9. Compare with Sklearn implementation.srt 12.27Кб
9. Manhattan vs Euclidean Distance.mp4 30.49Мб
9. Manhattan vs Euclidean Distance.srt 7.75Кб
9. PCA - Biplot and the Screen Plot.mp4 135.60Мб
9. PCA - Biplot and the Screen Plot.srt 26.37Кб
9. Python Strings.mp4 56.27Мб
9. Python Strings.srt 16.14Кб
9. Random Forests Hyper-Parameters.mp4 39.67Мб
9. Random Forests Hyper-Parameters.srt 5.97Кб
9. SVM with Regression.mp4 25.00Мб
9. SVM with Regression.srt 8.00Кб
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TutsNode.com.txt 63б
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