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[GigaCourse.Com] Udemy - Python for Data Science & Machine Learning from A-Z
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[GigaCourse.Com] Udemy - Python for Data Science & Machine Learning from A-Z
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2022-01-09
最近下载:
2024-11-09
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文件列表
19. PCA/7. PCA - Image Compression.mp4
262.1 MB
16. Ensemble Learning and Random Forests/6. Implementing Random Forests from scratch Part 1.mp4
212.4 MB
15. Decision Trees/7. ID3 - Putting Everything Together.mp4
191.3 MB
14. K Nearest Neighbors/3. EDA on Iris Dataset.mp4
169.7 MB
15. Decision Trees/3. What is Entropy and Information Gain.mp4
142.7 MB
19. PCA/9. PCA - Biplot and the Screen Plot.mp4
142.2 MB
1. Introduction/6. How To Get a Data Science Job.mp4
137.6 MB
1. Introduction/5. What is a Data Scientist.mp4
133.7 MB
17. Support Vector Machines/6. SVM - Kernel Types.mp4
132.5 MB
15. Decision Trees/2. EDA on Adult Dataset.mp4
129.2 MB
15. Decision Trees/8. Evaluating our ID3 implementation.mp4
127.9 MB
19. PCA/8. PCA Data Preprocessing.mp4
126.3 MB
15. Decision Trees/13. Pruning.mp4
118.5 MB
17. Support Vector Machines/8. SVM with Non-linear Dataset.mp4
117.0 MB
13. Linear and Logistic Regression/3. Linear Regression + Correlation Methods.mp4
115.7 MB
18. K-means/3. Representing Clusters.mp4
114.9 MB
3. Python For Data Science/15. Python Dictionaries.mp4
109.2 MB
17. Support Vector Machines/7. SVM with Linear Dataset (Iris).mp4
106.5 MB
18. K-means/1. Unsupervised Machine Learning Intro.mp4
105.8 MB
9. Machine Learning/1. Introduction To Machine Learning.mp4
103.5 MB
16. Ensemble Learning and Random Forests/2. What is Ensemble Learning.mp4
96.4 MB
14. K Nearest Neighbors/7. Hyperparameter tuning using the cross-validation.mp4
94.7 MB
2. Data Science & Machine Learning Concepts/2. What is Data Science.mp4
92.3 MB
14. K Nearest Neighbors/5. Implement the KNN algorithm from scratch.mp4
91.2 MB
3. Python For Data Science/7. Python Operators.mp4
91.0 MB
16. Ensemble Learning and Random Forests/13. AdaBoost Part 2.mp4
90.1 MB
15. Decision Trees/4. The Decision Tree ID3 algorithm from scratch Part 1.mp4
89.4 MB
2. Data Science & Machine Learning Concepts/3. What is Machine Learning.mp4
87.5 MB
18. K-means/2. Unsupervised Machine Learning Continued.mp4
87.2 MB
15. Decision Trees/12. Decision Trees Hyper-parameters.mp4
85.2 MB
1. Introduction/4. Data Science Job Roles.mp4
83.7 MB
1. Introduction/7. Data Science Projects Overview.mp4
83.3 MB
2. Data Science & Machine Learning Concepts/4. Machine Learning Concepts & Algorithms.mp4
81.8 MB
2. Data Science & Machine Learning Concepts/5. What is Deep Learning.mp4
81.6 MB
17. Support Vector Machines/5. Kernel Trick.mp4
80.8 MB
2. Data Science & Machine Learning Concepts/6. Machine Learning vs Deep Learning.mp4
79.6 MB
8. Python Data Visualization/1. Data Visualization Overview.mp4
76.6 MB
7. Pandas Data Analysis/2. Introduction to Pandas Continued.mp4
74.5 MB
3. Python For Data Science/19. Object Oriented Programming in Python.mp4
73.7 MB
19. PCA/10. PCA - Feature Scaling and Screen Plot.mp4
71.5 MB
15. Decision Trees/10. Visualizing the tree.mp4
71.5 MB
19. PCA/12. PCA - Visualization.mp4
71.3 MB
17. Support Vector Machines/3. Hard vs Soft Margins.mp4
68.8 MB
15. Decision Trees/9. Compare with Sklearn implementation.mp4
68.8 MB
15. Decision Trees/5. The Decision Tree ID3 algorithm from scratch Part 2.mp4
67.1 MB
3. Python For Data Science/18. Python Functions.mp4
65.5 MB
5. Probability & Hypothesis Testing/4. Hypothesis Testing Overview.mp4
63.5 MB
3. Python For Data Science/13. More about Lists.mp4
63.4 MB
19. PCA/4. PCA Algorithm Steps (Mathematics).mp4
60.5 MB
3. Python For Data Science/9. Python Strings.mp4
59.0 MB
16. Ensemble Learning and Random Forests/3. What is Bootstrap Sampling.mp4
58.6 MB
3. Python For Data Science/10. Python Conditional Statements.mp4
57.3 MB
3. Python For Data Science/14. Python Tuples.mp4
57.2 MB
10. Data Loading & Exploration/1. Exploratory Data Analysis.mp4
53.0 MB
16. Ensemble Learning and Random Forests/7. Implementing Random Forests from scratch Part 2.mp4
53.0 MB
14. K Nearest Neighbors/10. Feature scaling in KNN.mp4
51.8 MB
17. Support Vector Machines/2. SVM intuition.mp4
51.2 MB
15. Decision Trees/15. Decision Trees Pros and Cons.mp4
50.1 MB
19. PCA/2. What is PCA.mp4
49.6 MB
3. Python For Data Science/17. Compound Data Types & When to use each one.mp4
49.4 MB
1. Introduction/2. Data Science + Machine Learning Marketplace.mp4
49.2 MB
7. Pandas Data Analysis/1. Introduction to Pandas.mp4
49.1 MB
14. K Nearest Neighbors/11. Curse of dimensionality.mp4
48.2 MB
4. Statistics for Data Science/6. Inferential Statistics.mp4
47.2 MB
16. Ensemble Learning and Random Forests/5. Out-of-Bag Error (OOB Error).mp4
44.1 MB
6. NumPy Data Analysis/3. NumPy Arrays Basics.mp4
41.9 MB
16. Ensemble Learning and Random Forests/9. Random Forests Hyper-Parameters.mp4
41.6 MB
17. Support Vector Machines/10. SMV - Project Overview.mp4
41.5 MB
19. PCA/5. Covariance Matrix vs SVD.mp4
40.6 MB
3. Python For Data Science/5. Python Variables, Booleans and None.mp4
40.1 MB
4. Statistics for Data Science/3. Measure of Variability.mp4
40.1 MB
20. Data Science Career/1. Creating A Data Science Resume.mp4
38.9 MB
19. PCA/11. PCA - Supervised vs Unsupervised.mp4
37.5 MB
16. Ensemble Learning and Random Forests/11. What is Boosting.mp4
37.2 MB
17. Support Vector Machines/1. SVM Outline.mp4
37.0 MB
3. Python For Data Science/6. Getting Started with Google Colab.mp4
36.8 MB
6. NumPy Data Analysis/4. NumPy Array Indexing.mp4
36.4 MB
6. NumPy Data Analysis/1. Intro NumPy Array Data Types.mp4
36.4 MB
4. Statistics for Data Science/4. Measure of Variability Continued.mp4
36.3 MB
15. Decision Trees/6. The Decision Tree ID3 algorithm from scratch Part 3.mp4
35.0 MB
5. Probability & Hypothesis Testing/3. Relative Frequency.mp4
34.3 MB
6. NumPy Data Analysis/2. NumPy Arrays.mp4
33.9 MB
19. PCA/1. PCA Section Overview.mp4
33.3 MB
15. Decision Trees/11. Plot the features importance.mp4
33.2 MB
13. Linear and Logistic Regression/1. Linear Regression Intro.mp4
32.3 MB
20. Data Science Career/6. Personal Branding.mp4
32.0 MB
14. K Nearest Neighbors/9. Manhattan vs Euclidean Distance.mp4
32.0 MB
14. K Nearest Neighbors/13. KNN pros and cons.mp4
31.9 MB
20. Data Science Career/4. Getting Started with Freelancing.mp4
31.7 MB
11. Data Cleaning/2. Data Cleaning.mp4
31.7 MB
20. Data Science Career/5. Top Freelance Websites.mp4
31.0 MB
16. Ensemble Learning and Random Forests/4. What is Bagging.mp4
30.9 MB
3. Python For Data Science/16. Python Sets.mp4
30.9 MB
1. Introduction/3. Data Science Job Opportunities.mp4
30.9 MB
14. K Nearest Neighbors/12. KNN use cases.mp4
30.3 MB
16. Ensemble Learning and Random Forests/8. Compare with sklearn implementation.mp4
29.0 MB
8. Python Data Visualization/3. Python Data Visualization Implementation.mp4
28.8 MB
5. Probability & Hypothesis Testing/1. What is Exactly is Probability.mp4
28.5 MB
4. Statistics for Data Science/8. Sampling Distribution.mp4
27.7 MB
3. Python For Data Science/8. Python Numbers & Booleans.mp4
26.9 MB
3. Python For Data Science/11. Python For Loops and While Loops.mp4
26.8 MB
16. Ensemble Learning and Random Forests/12. AdaBoost Part 1.mp4
26.8 MB
17. Support Vector Machines/9. SVM with Regression.mp4
26.2 MB
20. Data Science Career/3. How to Contact Recruiters.mp4
25.8 MB
14. K Nearest Neighbors/6. Compare the result with the sklearn library.mp4
25.8 MB
20. Data Science Career/7. Networking Do's and Don'ts.mp4
24.8 MB
4. Statistics for Data Science/5. Measures of Variable Relationship.mp4
24.7 MB
20. Data Science Career/2. Data Science Cover Letter.mp4
24.1 MB
4. Statistics for Data Science/2. Descriptive Statistics.mp4
22.5 MB
3. Python For Data Science/12. Python Lists.mp4
22.5 MB
4. Statistics for Data Science/1. Intro To Statistics.mp4
22.3 MB
17. Support Vector Machines/4. C hyper-parameter.mp4
22.1 MB
16. Ensemble Learning and Random Forests/10. Random Forests Pros and Cons.mp4
20.6 MB
19. PCA/3. PCA Drawbacks.mp4
20.4 MB
11. Data Cleaning/1. Feature Scaling.mp4
20.3 MB
15. Decision Trees/14. [Optional] Gain Ration.mp4
20.1 MB
12. Feature Selecting and Engineering/1. Feature Engineering.mp4
19.3 MB
3. Python For Data Science/1. What is Programming.mp4
19.2 MB
6. NumPy Data Analysis/6. Broadcasting.mp4
18.7 MB
13. Linear and Logistic Regression/4. Linear Regression Implementation.mp4
18.7 MB
1. Introduction/1. Who is This Course For.mp4
18.0 MB
6. NumPy Data Analysis/5. NumPy Array Computations.mp4
17.8 MB
14. K Nearest Neighbors/8. The decision boundary visualization.mp4
17.8 MB
15. Decision Trees/1. Decision Trees Section Overview.mp4
17.3 MB
3. Python For Data Science/2. Why Python for Data Science.mp4
17.1 MB
16. Ensemble Learning and Random Forests/1. Ensemble Learning Section Overview.mp4
16.9 MB
8. Python Data Visualization/2. Different Data Visualization Libraries in Python.mp4
16.7 MB
13. Linear and Logistic Regression/2. Gradient Descent.mp4
16.7 MB
14. K Nearest Neighbors/2. parametric vs non-parametric models.mp4
16.4 MB
20. Data Science Career/8. Importance of a Website.mp4
16.1 MB
15. Decision Trees/16. [Project] Predict whether income exceeds $50Kyr - Overview.mp4
15.8 MB
5. Probability & Hypothesis Testing/2. Expected Values.mp4
15.4 MB
3. Python For Data Science/3. What is Jupyter.mp4
15.3 MB
2. Data Science & Machine Learning Concepts/1. Why We Use Python.mp4
14.2 MB
14. K Nearest Neighbors/1. KNN Overview.mp4
13.5 MB
19. PCA/6. PCA - Main Applications.mp4
10.5 MB
13. Linear and Logistic Regression/5. Logistic Regression.mp4
9.3 MB
3. Python For Data Science/4. What is Google Colab.mp4
8.7 MB
14. K Nearest Neighbors/4. The KNN Intuition.mp4
8.5 MB
4. Statistics for Data Science/7. Measure of Asymmetry.mp4
7.1 MB
9. Machine Learning/1.1 Supervised Learning.pdf
856.8 kB
18. K-means/1.1 Unsupervised Learning.pdf
651.8 kB
3. Python For Data Science/3.1 Jupyter Notebook.pdf
314.5 kB
3. Python For Data Science/2.2 Python Basics.pdf
130.8 kB
7. Pandas Data Analysis/1.1 Pandas.pdf
112.8 kB
6. NumPy Data Analysis/1.1 NumPy Basics.pdf
79.0 kB
7. Pandas Data Analysis/1.2 Pandas Basics.pdf
78.9 kB
3. Python For Data Science/2.1 Importing Python Data.pdf
63.0 kB
19. PCA/7. PCA - Image Compression.srt
40.3 kB
13. Linear and Logistic Regression/3. Linear Regression + Correlation Methods.srt
39.5 kB
9. Machine Learning/1. Introduction To Machine Learning.srt
37.9 kB
8. Python Data Visualization/1. Data Visualization Overview.srt
37.7 kB
14. K Nearest Neighbors/3. EDA on Iris Dataset.srt
32.4 kB
3. Python For Data Science/7. Python Operators.srt
32.2 kB
15. Decision Trees/7. ID3 - Putting Everything Together.srt
31.8 kB
1. Introduction/6. How To Get a Data Science Job.srt
31.4 kB
16. Ensemble Learning and Random Forests/6. Implementing Random Forests from scratch Part 1.srt
30.8 kB
18. K-means/1. Unsupervised Machine Learning Intro.srt
30.1 kB
15. Decision Trees/3. What is Entropy and Information Gain.srt
30.0 kB
18. K-means/2. Unsupervised Machine Learning Continued.srt
29.9 kB
18. K-means/3. Representing Clusters.srt
29.0 kB
3. Python For Data Science/15. Python Dictionaries.srt
28.4 kB
1. Introduction/5. What is a Data Scientist.srt
27.5 kB
7. Pandas Data Analysis/2. Introduction to Pandas Continued.srt
27.5 kB
17. Support Vector Machines/6. SVM - Kernel Types.srt
27.4 kB
19. PCA/9. PCA - Biplot and the Screen Plot.srt
27.0 kB
3. Python For Data Science/19. Object Oriented Programming in Python.srt
26.3 kB
15. Decision Trees/8. Evaluating our ID3 implementation.srt
25.1 kB
15. Decision Trees/13. Pruning.srt
24.9 kB
15. Decision Trees/2. EDA on Adult Dataset.srt
24.3 kB
2. Data Science & Machine Learning Concepts/4. Machine Learning Concepts & Algorithms.srt
24.1 kB
2. Data Science & Machine Learning Concepts/3. What is Machine Learning.srt
23.5 kB
7. Pandas Data Analysis/1. Introduction to Pandas.srt
22.8 kB
4. Statistics for Data Science/6. Inferential Statistics.srt
22.6 kB
2. Data Science & Machine Learning Concepts/2. What is Data Science.srt
21.7 kB
19. PCA/8. PCA Data Preprocessing.srt
21.6 kB
16. Ensemble Learning and Random Forests/13. AdaBoost Part 2.srt
21.4 kB
3. Python For Data Science/18. Python Functions.srt
21.3 kB
17. Support Vector Machines/7. SVM with Linear Dataset (Iris).srt
20.3 kB
3. Python For Data Science/13. More about Lists.srt
19.9 kB
1. Introduction/7. Data Science Projects Overview.srt
19.5 kB
10. Data Loading & Exploration/1. Exploratory Data Analysis.srt
19.5 kB
17. Support Vector Machines/3. Hard vs Soft Margins.srt
19.4 kB
19. PCA/4. PCA Algorithm Steps (Mathematics).srt
19.0 kB
17. Support Vector Machines/8. SVM with Non-linear Dataset.srt
18.8 kB
6. NumPy Data Analysis/1. Intro NumPy Array Data Types.srt
18.7 kB
4. Statistics for Data Science/3. Measure of Variability.srt
18.6 kB
17. Support Vector Machines/5. Kernel Trick.srt
18.6 kB
3. Python For Data Science/10. Python Conditional Statements.srt
18.6 kB
3. Python For Data Science/17. Compound Data Types & When to use each one.srt
18.4 kB
2. Data Science & Machine Learning Concepts/6. Machine Learning vs Deep Learning.srt
18.3 kB
16. Ensemble Learning and Random Forests/2. What is Ensemble Learning.srt
17.8 kB
14. K Nearest Neighbors/5. Implement the KNN algorithm from scratch.srt
17.6 kB
6. NumPy Data Analysis/3. NumPy Arrays Basics.srt
17.2 kB
3. Python For Data Science/9. Python Strings.srt
16.5 kB
15. Decision Trees/12. Decision Trees Hyper-parameters.srt
16.5 kB
17. Support Vector Machines/2. SVM intuition.srt
16.4 kB
2. Data Science & Machine Learning Concepts/5. What is Deep Learning.srt
16.2 kB
1. Introduction/4. Data Science Job Roles.srt
16.1 kB
3. Python For Data Science/5. Python Variables, Booleans and None.srt
15.6 kB
15. Decision Trees/10. Visualizing the tree.srt
15.4 kB
3. Python For Data Science/14. Python Tuples.srt
15.3 kB
15. Decision Trees/4. The Decision Tree ID3 algorithm from scratch Part 1.srt
15.3 kB
14. K Nearest Neighbors/7. Hyperparameter tuning using the cross-validation.srt
15.0 kB
19. PCA/2. What is PCA.srt
14.9 kB
5. Probability & Hypothesis Testing/4. Hypothesis Testing Overview.srt
14.9 kB
19. PCA/10. PCA - Feature Scaling and Screen Plot.srt
14.7 kB
6. NumPy Data Analysis/4. NumPy Array Indexing.srt
14.4 kB
3. Python For Data Science/16. Python Sets.srt
13.8 kB
4. Statistics for Data Science/4. Measure of Variability Continued.srt
13.6 kB
8. Python Data Visualization/3. Python Data Visualization Implementation.srt
12.7 kB
3. Python For Data Science/6. Getting Started with Google Colab.srt
12.7 kB
15. Decision Trees/9. Compare with Sklearn implementation.srt
12.6 kB
13. Linear and Logistic Regression/1. Linear Regression Intro.srt
12.5 kB
11. Data Cleaning/1. Feature Scaling.srt
11.9 kB
11. Data Cleaning/2. Data Cleaning.srt
11.8 kB
4. Statistics for Data Science/1. Intro To Statistics.srt
11.4 kB
16. Ensemble Learning and Random Forests/3. What is Bootstrap Sampling.srt
11.4 kB
6. NumPy Data Analysis/2. NumPy Arrays.srt
11.4 kB
19. PCA/12. PCA - Visualization.srt
11.2 kB
3. Python For Data Science/11. Python For Loops and While Loops.srt
11.0 kB
4. Statistics for Data Science/5. Measures of Variable Relationship.srt
11.0 kB
15. Decision Trees/5. The Decision Tree ID3 algorithm from scratch Part 2.srt
10.9 kB
15. Decision Trees/15. Decision Trees Pros and Cons.srt
10.9 kB
20. Data Science Career/1. Creating A Data Science Resume.srt
10.8 kB
1. Introduction/2. Data Science + Machine Learning Marketplace.srt
10.7 kB
4. Statistics for Data Science/8. Sampling Distribution.srt
10.5 kB
4. Statistics for Data Science/2. Descriptive Statistics.srt
10.3 kB
16. Ensemble Learning and Random Forests/5. Out-of-Bag Error (OOB Error).srt
10.2 kB
3. Python For Data Science/8. Python Numbers & Booleans.srt
9.8 kB
14. K Nearest Neighbors/11. Curse of dimensionality.srt
9.8 kB
12. Feature Selecting and Engineering/1. Feature Engineering.srt
9.7 kB
3. Python For Data Science/1. What is Programming.srt
9.2 kB
8. Python Data Visualization/2. Different Data Visualization Libraries in Python.srt
9.0 kB
5. Probability & Hypothesis Testing/3. Relative Frequency.srt
8.9 kB
6. NumPy Data Analysis/5. NumPy Array Computations.srt
8.8 kB
13. Linear and Logistic Regression/2. Gradient Descent.srt
8.6 kB
20. Data Science Career/5. Top Freelance Websites.srt
8.6 kB
16. Ensemble Learning and Random Forests/7. Implementing Random Forests from scratch Part 2.srt
8.5 kB
14. K Nearest Neighbors/10. Feature scaling in KNN.srt
8.3 kB
17. Support Vector Machines/9. SVM with Regression.srt
8.2 kB
16. Ensemble Learning and Random Forests/10. Random Forests Pros and Cons.srt
8.0 kB
14. K Nearest Neighbors/13. KNN pros and cons.srt
7.9 kB
14. K Nearest Neighbors/9. Manhattan vs Euclidean Distance.srt
7.9 kB
15. Decision Trees/11. Plot the features importance.srt
7.9 kB
16. Ensemble Learning and Random Forests/4. What is Bagging.srt
7.9 kB
17. Support Vector Machines/1. SVM Outline.srt
7.6 kB
20. Data Science Career/3. How to Contact Recruiters.srt
7.5 kB
19. PCA/11. PCA - Supervised vs Unsupervised.srt
7.3 kB
3. Python For Data Science/12. Python Lists.srt
7.3 kB
20. Data Science Career/4. Getting Started with Freelancing.srt
7.2 kB
14. K Nearest Neighbors/8. The decision boundary visualization.srt
7.2 kB
19. PCA/1. PCA Section Overview.srt
7.2 kB
13. Linear and Logistic Regression/4. Linear Regression Implementation.srt
7.0 kB
1. Introduction/3. Data Science Job Opportunities.srt
7.0 kB
16. Ensemble Learning and Random Forests/11. What is Boosting.srt
7.0 kB
3. Python For Data Science/2. Why Python for Data Science.srt
6.9 kB
5. Probability & Hypothesis Testing/1. What is Exactly is Probability.srt
6.9 kB
19. PCA/5. Covariance Matrix vs SVD.srt
6.7 kB
20. Data Science Career/6. Personal Branding.srt
6.6 kB
6. NumPy Data Analysis/6. Broadcasting.srt
6.5 kB
20. Data Science Career/7. Networking Do's and Don'ts.srt
6.4 kB
17. Support Vector Machines/10. SMV - Project Overview.srt
6.4 kB
20. Data Science Career/2. Data Science Cover Letter.srt
6.1 kB
16. Ensemble Learning and Random Forests/9. Random Forests Hyper-Parameters.srt
6.1 kB
3. Python For Data Science/3. What is Jupyter.srt
6.1 kB
15. Decision Trees/6. The Decision Tree ID3 algorithm from scratch Part 3.srt
5.9 kB
17. Support Vector Machines/4. C hyper-parameter.srt
5.8 kB
15. Decision Trees/1. Decision Trees Section Overview.srt
5.7 kB
16. Ensemble Learning and Random Forests/12. AdaBoost Part 1.srt
5.6 kB
16. Ensemble Learning and Random Forests/1. Ensemble Learning Section Overview.srt
5.3 kB
14. K Nearest Neighbors/6. Compare the result with the sklearn library.srt
5.2 kB
16. Ensemble Learning and Random Forests/8. Compare with sklearn implementation.srt
5.1 kB
13. Linear and Logistic Regression/5. Logistic Regression.srt
5.0 kB
2. Data Science & Machine Learning Concepts/1. Why We Use Python.srt
5.0 kB
19. PCA/3. PCA Drawbacks.srt
5.0 kB
3. Python For Data Science/4. What is Google Colab.srt
5.0 kB
14. K Nearest Neighbors/12. KNN use cases.srt
5.0 kB
20. Data Science Career/8. Importance of a Website.srt
4.9 kB
14. K Nearest Neighbors/2. parametric vs non-parametric models.srt
4.8 kB
14. K Nearest Neighbors/1. KNN Overview.srt
4.4 kB
5. Probability & Hypothesis Testing/2. Expected Values.srt
4.2 kB
1. Introduction/1. Who is This Course For.srt
4.0 kB
19. PCA/6. PCA - Main Applications.srt
4.0 kB
15. Decision Trees/14. [Optional] Gain Ration.srt
3.8 kB
15. Decision Trees/16. [Project] Predict whether income exceeds $50Kyr - Overview.srt
3.7 kB
14. K Nearest Neighbors/4. The KNN Intuition.srt
3.1 kB
4. Statistics for Data Science/7. Measure of Asymmetry.srt
2.8 kB
0. Websites you may like/[CourseClub.ME].url
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16. Ensemble Learning and Random Forests/[CourseClub.Me].url
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3. Python For Data Science/[CourseClub.Me].url
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9. Machine Learning/[CourseClub.Me].url
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[CourseClub.Me].url
122 Bytes
0. Websites you may like/[GigaCourse.Com].url
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16. Ensemble Learning and Random Forests/[GigaCourse.Com].url
49 Bytes
3. Python For Data Science/[GigaCourse.Com].url
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9. Machine Learning/[GigaCourse.Com].url
49 Bytes
[GigaCourse.Com].url
49 Bytes
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