Skip to content

[GigaCourse Com] Udemy - Python for Data Science & Machine Learning from A-Z

Sign up to create lists
Report [GigaCourse Com] Udemy - Python for Data Science & Machine Learning from A-Z
Unverified source. This magnet is from an unverified source. The content may be unsafe or mislabeled. Proceed with caution.
Title: Udemy - Python for Data Science & Machine Learning from AGroup: ZSource: Udemy
Info Hash
E4166DCF5CE4A8C75A656340D075DD43323182F1
Source
Unverified
Total Size
7.32 GB
Total Files
100
Seeders
0
Leechers
1
Health
Score
1
Type
Bookware

File List

FileSize
0. Websites you may like/[CourseClub.ME].url122 B
0. Websites you may like/[GigaCourse.Com].url49 B
1. Introduction/1. Who is This Course For.mp417.16 MB
1. Introduction/1. Who is This Course For.srt3.95 KB
1. Introduction/2. Data Science + Machine Learning Marketplace.mp446.94 MB
1. Introduction/2. Data Science + Machine Learning Marketplace.srt10.4 KB
1. Introduction/3. Data Science Job Opportunities.mp429.42 MB
1. Introduction/3. Data Science Job Opportunities.srt6.86 KB
1. Introduction/4. Data Science Job Roles.mp479.8 MB
1. Introduction/4. Data Science Job Roles.srt15.73 KB
1. Introduction/5. What is a Data Scientist.mp4127.47 MB
1. Introduction/5. What is a Data Scientist.srt26.86 KB
1. Introduction/6. How To Get a Data Science Job.mp4131.19 MB
1. Introduction/6. How To Get a Data Science Job.srt30.62 KB
1. Introduction/7. Data Science Projects Overview.mp479.48 MB
1. Introduction/7. Data Science Projects Overview.srt19.09 KB
10. Data Loading & Exploration/1. Exploratory Data Analysis.mp450.56 MB
10. Data Loading & Exploration/1. Exploratory Data Analysis.srt19.07 KB
11. Data Cleaning/1. Feature Scaling.mp419.38 MB
11. Data Cleaning/1. Feature Scaling.srt11.58 KB
11. Data Cleaning/2. Data Cleaning.mp430.21 MB
11. Data Cleaning/2. Data Cleaning.srt11.53 KB
12. Feature Selecting and Engineering/1. Feature Engineering.mp418.41 MB
12. Feature Selecting and Engineering/1. Feature Engineering.srt9.42 KB
13. Linear and Logistic Regression/1. Linear Regression Intro.mp430.8 MB
13. Linear and Logistic Regression/1. Linear Regression Intro.srt12.16 KB
13. Linear and Logistic Regression/2. Gradient Descent.mp415.93 MB
13. Linear and Logistic Regression/2. Gradient Descent.srt8.44 KB
13. Linear and Logistic Regression/3. Linear Regression + Correlation Methods.mp4110.38 MB
13. Linear and Logistic Regression/3. Linear Regression + Correlation Methods.srt38.61 KB
13. Linear and Logistic Regression/4. Linear Regression Implementation.mp417.86 MB
13. Linear and Logistic Regression/4. Linear Regression Implementation.srt6.88 KB
13. Linear and Logistic Regression/5. Logistic Regression.mp48.9 MB
13. Linear and Logistic Regression/5. Logistic Regression.srt4.93 KB
14. K Nearest Neighbors/1. KNN Overview.mp412.89 MB
14. K Nearest Neighbors/1. KNN Overview.srt4.27 KB
14. K Nearest Neighbors/10. Feature scaling in KNN.mp449.39 MB
14. K Nearest Neighbors/10. Feature scaling in KNN.srt8.06 KB
14. K Nearest Neighbors/11. Curse of dimensionality.mp445.99 MB
14. K Nearest Neighbors/11. Curse of dimensionality.srt9.6 KB
14. K Nearest Neighbors/12. KNN use cases.mp428.92 MB
14. K Nearest Neighbors/12. KNN use cases.srt4.85 KB
14. K Nearest Neighbors/13. KNN pros and cons.mp430.45 MB
14. K Nearest Neighbors/13. KNN pros and cons.srt7.75 KB
14. K Nearest Neighbors/2. parametric vs non-parametric models.mp415.63 MB
14. K Nearest Neighbors/2. parametric vs non-parametric models.srt4.73 KB
14. K Nearest Neighbors/3. EDA on Iris Dataset.mp4161.88 MB
14. K Nearest Neighbors/3. EDA on Iris Dataset.srt31.65 KB
14. K Nearest Neighbors/4. The KNN Intuition.mp48.09 MB
14. K Nearest Neighbors/4. The KNN Intuition.srt3.06 KB
14. K Nearest Neighbors/5. Implement the KNN algorithm from scratch.mp486.97 MB
14. K Nearest Neighbors/5. Implement the KNN algorithm from scratch.srt17.14 KB
14. K Nearest Neighbors/6. Compare the result with the sklearn library.mp424.57 MB
14. K Nearest Neighbors/6. Compare the result with the sklearn library.srt5.1 KB
14. K Nearest Neighbors/7. Hyperparameter tuning using the cross-validation.mp490.3 MB
14. K Nearest Neighbors/7. Hyperparameter tuning using the cross-validation.srt14.66 KB
14. K Nearest Neighbors/8. The decision boundary visualization.mp416.94 MB
14. K Nearest Neighbors/8. The decision boundary visualization.srt7.07 KB
14. K Nearest Neighbors/9. Manhattan vs Euclidean Distance.mp430.49 MB
14. K Nearest Neighbors/9. Manhattan vs Euclidean Distance.srt7.75 KB
15. Decision Trees/1. Decision Trees Section Overview.mp416.46 MB
15. Decision Trees/1. Decision Trees Section Overview.srt5.6 KB
15. Decision Trees/10. Visualizing the tree.mp468.17 MB
15. Decision Trees/10. Visualizing the tree.srt15 KB
15. Decision Trees/11. Plot the features importance.mp431.67 MB
15. Decision Trees/11. Plot the features importance.srt7.75 KB
15. Decision Trees/12. Decision Trees Hyper-parameters.mp481.27 MB
15. Decision Trees/12. Decision Trees Hyper-parameters.srt16.12 KB
15. Decision Trees/13. Pruning.mp4112.97 MB
15. Decision Trees/13. Pruning.srt24.35 KB
15. Decision Trees/14. [Optional] Gain Ration.mp419.18 MB
15. Decision Trees/14. [Optional] Gain Ration.srt3.7 KB
15. Decision Trees/15. Decision Trees Pros and Cons.mp447.74 MB
15. Decision Trees/15. Decision Trees Pros and Cons.srt10.6 KB
15. Decision Trees/16. [Project] Predict whether income exceeds $50Kyr - Overview.mp415.11 MB
15. Decision Trees/16. [Project] Predict whether income exceeds $50Kyr - Overview.srt3.6 KB
15. Decision Trees/2. EDA on Adult Dataset.mp4123.19 MB
15. Decision Trees/2. EDA on Adult Dataset.srt23.77 KB
15. Decision Trees/3. What is Entropy and Information Gain.mp4136.08 MB
15. Decision Trees/3. What is Entropy and Information Gain.srt29.33 KB
15. Decision Trees/4. The Decision Tree ID3 algorithm from scratch Part 1.mp485.27 MB
15. Decision Trees/4. The Decision Tree ID3 algorithm from scratch Part 1.srt14.91 KB
15. Decision Trees/5. The Decision Tree ID3 algorithm from scratch Part 2.mp463.96 MB
15. Decision Trees/5. The Decision Tree ID3 algorithm from scratch Part 2.srt10.62 KB
15. Decision Trees/6. The Decision Tree ID3 algorithm from scratch Part 3.mp433.41 MB
15. Decision Trees/6. The Decision Tree ID3 algorithm from scratch Part 3.srt5.75 KB
15. Decision Trees/7. ID3 - Putting Everything Together.mp4182.48 MB
15. Decision Trees/7. ID3 - Putting Everything Together.srt31.03 KB
15. Decision Trees/8. Evaluating our ID3 implementation.mp4121.94 MB
15. Decision Trees/8. Evaluating our ID3 implementation.srt24.54 KB
15. Decision Trees/9. Compare with Sklearn implementation.mp465.58 MB
15. Decision Trees/9. Compare with Sklearn implementation.srt12.27 KB
16. Ensemble Learning and Random Forests/1. Ensemble Learning Section Overview.mp416.07 MB
16. Ensemble Learning and Random Forests/1. Ensemble Learning Section Overview.srt5.17 KB
16. Ensemble Learning and Random Forests/10. Random Forests Pros and Cons.mp419.69 MB
16. Ensemble Learning and Random Forests/10. Random Forests Pros and Cons.srt7.85 KB
16. Ensemble Learning and Random Forests/11. What is Boosting.mp435.44 MB
16. Ensemble Learning and Random Forests/11. What is Boosting.srt6.86 KB
16. Ensemble Learning and Random Forests/12. AdaBoost Part 1.mp425.53 MB
16. Ensemble Learning and Random Forests/12. AdaBoost Part 1.srt5.5 KB

Trackers

No trackers found.