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  • 2024-01-15 19:21:31
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ReScene version pyReScene Auto 0.7 SOFTiMAGE File size CRC
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Archived files
2. Intro to Data Science\1. What is Data Science.mp4 [648c5ab573eddf0a] 12,870,084 1F1B404B
2. Intro to Data Science\10. Intro to Data Science.html 148 0FD60DBA
2. Intro to Data Science\2. The Data Science Skillset.mp4 [f2dffe21ecf9a3a5] 7,634,012 D89653F5
2. Intro to Data Science\3. What is Machine Learning.mp4 [cb8a71fc58efc5aa] 14,628,083 E39EDA4F
2. Intro to Data Science\4. Common Machine Learning Algorithms.mp4 [a432b85c2170693b] 10,838,338 DC54DEA7
2. Intro to Data Science\5. Data Science Workflow.mp4 [20ed7b6366e1efd5] 5,808,840 62E21BAB
2. Intro to Data Science\6. Data Prep & EDA Steps.mp4 [721b9b53f26ca53c] 20,966,456 74018D24
2. Intro to Data Science\7. Modeling Steps.mp4 [89bdfaa30715f914] 17,988,809 6A28F1C7
2. Intro to Data Science\8. Classification Modeling.mp4 [d4281b941a38cb33] 3,739,686 A2F36202
2. Intro to Data Science\9. Key Takeaways.mp4 [5a73a451687a21a1] 7,951,074 43D6F01C
3. Classification 101\1. Classification 101.mp4 [ac8abcf50de6f9ac] 30,664,819 6F62DA8B
3. Classification 101\2. Goals of Classification.mp4 [b36336ad391fbff4] 10,956,654 1D1A8CFC
3. Classification 101\3. Types of Classification.mp4 [5ff16400fc8d76e2] 11,043,572 DE9B8C9B
3. Classification 101\4. Classification Modeling Workflow.mp4 [e0babeea094cded6] 14,597,631 D4F1A647
3. Classification 101\5. Key Takeaways.mp4 [9e8529a2af10b583] 8,256,521 F61D2CCA
3. Classification 101\6. Classification 101.html 148 2F138002
4. Data Prep & EDA\1. EDA For Classification.mp4 [d5c6ec631e2dede8] 19,028,220 8A7CE50F
4. Data Prep & EDA\10. PRO TIP Correlation Matrix.mp4 [21a2c87928b9bdba] 13,182,616 7B05B966
4. Data Prep & EDA\11. DEMO Correlation Matrix.mp4 [513ef7f5ac3da50c] 32,863,969 9FC56FE0
4. Data Prep & EDA\12. Feature-Target Relationships.mp4 [606dcba55f8b4c6b] 38,895,127 7585A327
4. Data Prep & EDA\13. Feature-Feature Relationships.mp4 [10db19a06aff320] 14,585,686 6227591D
4. Data Prep & EDA\14. PRO TIP Pair Plots.mp4 [68181c9e30cd0a02] 45,856,168 E9E35F68
4. Data Prep & EDA\15. ASSIGNMENT Exploring Relationships.mp4 [226d2c81a57402b3] 12,227,564 46AD9D11
4. Data Prep & EDA\16. SOLUTION Exploring Relationships.mp4 [9c93e3dea2f86ee7] 53,709,465 B28F8C77
4. Data Prep & EDA\17. Feature Engineering Overview.mp4 [b3dd4914ffe1cca] 25,228,937 AF354D8E
4. Data Prep & EDA\18. Numeric Feature Engineering.mp4 [dcbb64071e4ab84a] 23,978,715 D61DD3DF
4. Data Prep & EDA\19. Dummy Variables.mp4 [3fb3bb9ea8f48622] 25,730,226 D2D9F682
4. Data Prep & EDA\2. Defining a Target.mp4 [5eee10d64f018d40] 22,959,256 60C55E42
4. Data Prep & EDA\20. Binning Categories.mp4 [28b7164045709c1b] 20,223,591 7D709FEB
4. Data Prep & EDA\21. DEMO Feature Engineering.mp4 [1df908de70f00c24] 46,794,806 6C28179B
4. Data Prep & EDA\22. Data Splitting.mp4 [929081f91b9ef675] 33,222,477 76AE3D54
4. Data Prep & EDA\23. Preparing Data for Modeling.mp4 [389a4ce0c81690d5] 9,927,430 EF6F3682
4. Data Prep & EDA\24. ASSIGNMENT Preparing the Data for Modeling.mp4 [3bed9fc9ee110d12] 7,920,129 3B81DCAB
4. Data Prep & EDA\25. SOLUTION Prepare the Data for Modeling.mp4 [f3cc6e5e91d7aa49] 60,088,473 DFAED338
4. Data Prep & EDA\26. Key Takeaways.mp4 [e5a40f55beb8c944] 7,638,381 9F307EDE
4. Data Prep & EDA\27. Data Prep & EDA.html 148 6986CE0F
4. Data Prep & EDA\3. DEMO Defining a Target.mp4 [4a58010bf723a3b2] 38,001,454 B96F63C4
4. Data Prep & EDA\4. Exploring the Target.mp4 [b896df8ea09f2281] 20,604,877 A1DE018D
4. Data Prep & EDA\5. Exploring the Features.mp4 [651d6e13d6d5e226] 10,893,463 5ADBF5B9
4. Data Prep & EDA\6. DEMO Exploring the Features.mp4 [859db6529d149ee6] 29,726,361 A5D2ECF7
4. Data Prep & EDA\7. ASSIGNMENT Exploring the Target & Features.mp4 [633acb25a9407c2a] 14,850,058 954E00C7
4. Data Prep & EDA\8. SOLUTION Exploring the Target & Features.mp4 [ea8d2e3fcf315633] 41,196,331 534307EB
4. Data Prep & EDA\9. Correlation.mp4 [e6c993c0b413bdde] 22,758,700 E5D9D0E1
5. K-Nearest Neighbors\1. K-Nearest Neighbors.mp4 [decbfca1b3890ee8] 32,116,017 14DA0CD6
5. K-Nearest Neighbors\10. Overfitting & Validation.mp4 [a55a5aeeb337d66a] 39,168,711 DFD57D13
5. K-Nearest Neighbors\11. DEMO Hyperparameter Tuning.mp4 [55328f3c3a75a6a6] 31,108,787 661EBE0F
5. K-Nearest Neighbors\12. Hard vs. Soft Classification.mp4 [a0c08f9e133a3081] 24,499,352 4029A5E3
5. K-Nearest Neighbors\13. DEMO Probability vs. Event Rate.mp4 [499705c457df1e2a] 61,902,709 8245B341
5. K-Nearest Neighbors\14. ASSIGNMENT Tuning a KNN Model.mp4 [92edb30e4e683653] 7,681,539 7EAAE992
5. K-Nearest Neighbors\15. SOLUTION Tuning a KNN Model.mp4 [e65a18e93fd6d8d] 25,964,348 E37F8064
5. K-Nearest Neighbors\16. Pros & Cons of KNN.mp4 [e7a91cd459512077] 27,219,324 52F08D14
5. K-Nearest Neighbors\17. Key Takeaways.mp4 [64472717074c41f5] 6,374,528 282F7AF1
5. K-Nearest Neighbors\18. K-Nearest Neighbors.html 148 17F3F328
5. K-Nearest Neighbors\2. The KNN Workflow.mp4 [ac5add7cdd4ae8e2] 28,611,414 0591535A
5. K-Nearest Neighbors\3. KNN in Python.mp4 [12f23fdaa313fd3b] 12,447,288 28D661E5
5. K-Nearest Neighbors\4. Model Accuracy.mp4 [b0140980a0fbf83a] 21,793,503 0FF9B793
5. K-Nearest Neighbors\5. Confusion Matrix.mp4 [80f0a3ffaea337e9] 21,068,220 56460D1A
5. K-Nearest Neighbors\6. DEMO Confusion Matrix.mp4 [4fe69822180bfb4e] 24,412,809 5BB6FB89
5. K-Nearest Neighbors\7. ASSIGNMENT Fitting a Simple KNN Model.mp4 [4b4a167e08bdc346] 11,366,492 2D234CE7
5. K-Nearest Neighbors\8. SOLUTION Fitting a Simple KNN Model.mp4 [652824f43297a13e] 29,492,117 5E45E0B2
5. K-Nearest Neighbors\9. Hyperparameter Tuning.mp4 [e6c75e5624794554] 17,158,336 1697B10A
6. Logistic Regression\1. Logistic Regression.mp4 [2b63a3f85b69eafe] 15,996,514 50E06990
6. Logistic Regression\10. SOLUTION Logistic Regression.mp4 [a481f172c1815583] 25,640,310 067127A6
6. Logistic Regression\11. Feature Engineering & Selection.mp4 [610985146712e803] 22,748,388 B8AE4470
6. Logistic Regression\12. Regularization.mp4 [df6ca142b27188dc] 25,597,482 DA4F9A7C
6. Logistic Regression\13. Tuning a Regularized Model.mp4 [43b3ff1b7b324ecb] 22,470,710 C3E10224
6. Logistic Regression\14. DEMO Regularized Logistic Regression.mp4 [ed472351790ee730] 32,795,876 311D668A
6. Logistic Regression\15. ASSIGNMENT Regularized Logistic Regression.mp4 [bc1d06887410b922] 7,204,797 B3726CDF
6. Logistic Regression\16. SOLUTION Regularized Logistic Regression.mp4 [d83f60cfb03c66a9] 45,680,175 8B4BC5F6
6. Logistic Regression\17. Multi-class Logistic Regression.mp4 [d358771e7cad8d63] 36,221,348 54A7B718
6. Logistic Regression\18. ASSIGNMENT Multi-class Logistic Regression.mp4 [502a8ac5920035c4] 8,587,237 D1F0A8B8
6. Logistic Regression\19. SOLUTION Multi-class Logistic Regression.mp4 [307535c0befe9ad8] 20,850,874 FF2A464A
6. Logistic Regression\2. Logistic vs. Linear Regression.mp4 [5cd69f1ac361f1aa] 12,575,986 7B044860
6. Logistic Regression\20. Pros & Cons of Logistic Regression.mp4 [7c89e5c0738c3fe4] 17,068,111 2CD66BA0
6. Logistic Regression\21. Key Takeaways.mp4 [ef23852945bde124] 8,339,754 8BE49F33
6. Logistic Regression\22. Logistic Regression.html 148 CF6F672C
6. Logistic Regression\3. The Logistic Function.mp4 [a2feae838caf4439] 14,919,646 3D441AB4
6. Logistic Regression\4. Likelihood.mp4 [654b1db748fe88b8] 24,496,747 7D581570
6. Logistic Regression\5. Multiple Logistic Regression.mp4 [73806b185d9bfb65] 14,827,517 C9F7BA11
6. Logistic Regression\6. The Logistic Regression Workflow.mp4 [ffb82a814e48b1a3] 4,624,625 7EF5C0A5
6. Logistic Regression\7. Logistic Regression in Python.mp4 [7cdb70ebcf7800fe] 29,253,215 FA6EE6E2
6. Logistic Regression\8. Interpreting Coefficients.mp4 [5e62ca10b9c30f4c] 22,101,759 5CB1DE73
6. Logistic Regression\9. ASSIGNMENT Logistic Regression.mp4 [563f5c45fc4058d5] 9,959,252 6CDD3DD4
7. Classification Metrics\1. Classification Metrics.mp4 [bae77fd2fb2e3aec] 14,204,763 F0FD539A
7. Classification Metrics\10. DEMO Plotting Precision-Recall & F1 Curves.mp4 [6981ea883e46f21e] 24,620,165 DA9E9EAB
7. Classification Metrics\11. The ROC Curve & AUC.mp4 [4fccb48dc0414972] 19,945,770 834CDABD
7. Classification Metrics\12. DEMO The ROC Curve & AUC.mp4 [e9e3da4337a62a12] 20,793,311 FD5F97DD
7. Classification Metrics\13. Classification Metrics Recap.mp4 [9fa10fb41dec4bb3] 15,537,031 E1D81F31
7. Classification Metrics\14. ASSIGNMENT Threshold Shifting.mp4 [a6b6e35de29bc5dc] 7,345,466 65D765AB
7. Classification Metrics\15. SOLUTION Threshold Shifting.mp4 [61d7eb964efe8ca5] 38,514,233 B3DFC9D5
7. Classification Metrics\16. Multi-class Metrics.mp4 [99df8d5ab118c461] 31,217,980 90D0DCA0
7. Classification Metrics\17. Multi-class Metrics in Python.mp4 [27d21db3d47328c] 8,889,787 143266C8
7. Classification Metrics\18. ASSIGNMENT Multi-class Metrics.mp4 [607ba24d6c14604f] 6,546,239 B42AAF0F
7. Classification Metrics\19. SOLUTION Multi-class Metrics.mp4 [52ee49a8c9cfb38c] 17,427,542 5CCFD93E
7. Classification Metrics\2. Accuracy, Precision & Recall.mp4 [b89a485551af439b] 31,021,456 21D868AE
7. Classification Metrics\20. Key Takeaways.mp4 [98346c59fdd39bab] 10,372,004 051569A7
7. Classification Metrics\21. Classification Metrics.html 148 45043A06
7. Classification Metrics\3. DEMO Accuracy, Precision & Recall.mp4 [dafe19609f1ad176] 35,518,823 B48835AB
7. Classification Metrics\4. PRO TIP F1 Score.mp4 [b0452ccab4a352de] 19,590,477 722AE268
7. Classification Metrics\5. ASSIGNMENT Model Metrics.mp4 [89d88e940e7a0432] 5,550,160 F2D2AB76
7. Classification Metrics\6. SOLUTION Model Metrics.mp4 [7c25c34256776d88] 25,504,994 F3B0AC94
7. Classification Metrics\7. Soft Classification.mp4 [27d9d019b110bc1d] 32,614,904 AE9815A2
7. Classification Metrics\8. DEMO Leveraging Soft Classification.mp4 [b02e4090491b06c4] 20,706,553 18E220E3
7. Classification Metrics\9. PRO TIP Precision-Recall & F1 Curves.mp4 [6cd1a23a58b3b7e5] 21,934,628 3C5AB096
8. Imbalanced Data\1. Imbalanced Data.mp4 [d4c399e8bb5eb691] 24,608,986 4561303F
8. Imbalanced Data\10. Undersampling.mp4 [3a1c8104d330a53a] 10,314,912 0AE9D2A3
8. Imbalanced Data\11. Undersampling in Python.mp4 [7b562c2596737433] 35,033,963 E0CDC893
8. Imbalanced Data\12. ASSIGNMENT Sampling Methods.mp4 [e276ff2314e3795e] 17,812,228 5EDC7EBB
8. Imbalanced Data\13. SOLUTION Sampling Methods.mp4 [5615d6c8c81742d7] 47,532,329 465CC8D4
8. Imbalanced Data\14. Changing Class Weights.mp4 [a17b6e2d2a82c20a] 17,360,813 A06AE2A5
8. Imbalanced Data\15. DEMO Changing Class Weights.mp4 [fccdb3a1db737aab] 22,381,926 1DFF4618
8. Imbalanced Data\16. ASSIGNMENT Changing Class Weights.mp4 [10327a337775ce4e] 4,475,364 01E81520
8. Imbalanced Data\17. SOLUTION Changing Class Weights.mp4 [23e0e3759609786d] 22,971,922 021DFA53
8. Imbalanced Data\18. Imbalanced Data Recap.mp4 [f0e61eb2fdef494a] 8,681,396 EE4B8528
8. Imbalanced Data\19. Key Takeaways.mp4 [6552aa0e4491ac21] 5,818,109 572F3EDB
8. Imbalanced Data\2. Managing Imbalanced Data.mp4 [ff1d6793969fa774] 22,292,329 9229E700
8. Imbalanced Data\20. Imbalanced Data.html 148 8D494AD3
8. Imbalanced Data\3. Threshold Shifting.mp4 [f27d5f5b7043503f] 11,811,085 A2E529E9
8. Imbalanced Data\4. Sampling Strategies.mp4 [48c52df33495dbe9] 11,158,973 003635C3
8. Imbalanced Data\5. Oversampling.mp4 [31579d00a65c5e5c] 7,604,424 DA7F070C
8. Imbalanced Data\6. Oversampling in Python.mp4 [1791786ceb0cd3f2] 16,152,345 84E34677
8. Imbalanced Data\7. DEMO Oversampling.mp4 [8cd2d11e4f80bd17] 29,493,071 32DE20C6
8. Imbalanced Data\8. SMOTE.mp4 [51553790274b7d63] 5,943,342 988722E1
8. Imbalanced Data\9. SMOTE in Python.mp4 [abec5930d07e821c] 15,589,115 DA5ABA31
9. Mid-Course Project\1. Project Brief.mp4 [c3dddb13dea20046] 25,388,087 D47C6022
9. Mid-Course Project\2. Solution Walkthrough.mp4 [6d530c44f30ece08] 79,545,845 FA855946
10. Decision Trees\1. Decision Trees.mp4 [a56d939621f138b0] 18,890,189 E2DB130F
10. Decision Trees\10. DEMO Hyperparameter Tuning.mp4 [be1e7bb65d7802af] 23,968,372 E0AA94DE
10. Decision Trees\11. ASSIGNMENT Tuned Decision Tree.mp4 [fff868818bea34e7] 3,720,839 376012C0
10. Decision Trees\12. SOLUTION Tuned Decision Tree.mp4 [a061a73423773078] 27,871,696 B8F441A7
10. Decision Trees\13. Pros & Cons of Decision Trees.mp4 [67173d9896467995] 17,466,961 2438967E
10. Decision Trees\14. Key Takeaways.mp4 [35614e5a0b595a33] 6,760,762 0C8448BD
10. Decision Trees\15. Decision Trees.html 148 55D5DED7
10. Decision Trees\2. Entropy.mp4 [6aca8a4987304cb0] 25,166,744 920AE3BC
10. Decision Trees\3. Decision Tree Predictions.mp4 [5917d30728e6bbed] 21,841,113 F0E1D038
10. Decision Trees\4. Decision Trees in Python.mp4 [3506747c4facb349] 16,608,797 35F65949
10. Decision Trees\5. DEMO Decision Trees.mp4 [828419ad449ef282] 33,895,868 07BEAC64
10. Decision Trees\6. Feature Importance.mp4 [c822c3925a6c66a3] 29,664,709 DF24D1F1
10. Decision Trees\7. ASSIGNMENT Decision Trees.mp4 [e921e9a78fafa7d7] 5,935,634 662C22D8
10. Decision Trees\8. SOLUTION Decision Trees.mp4 [5b783ef1a2307f01] 36,845,426 4C6D8EB9
10. Decision Trees\9. Hyperparameter Tuning for Decision Trees.mp4 [7c07c8e545b9f49c] 27,485,978 91938A24
11. Ensemble Models\1. Ensemble Models.mp4 [b127064c13b8fa7a] 21,909,487 BD5846A0
11. Ensemble Models\10. Pros & Cons of Random Forests.mp4 [22e1188656f51baa] 11,345,375 B6EBA1CB
11. Ensemble Models\11. ASSIGNMENT Random Forests.mp4 [a5f5abba522a1183] 5,420,464 D9C36959
11. Ensemble Models\12. SOLUTION Random Forests.mp4 [4286997a2a934588] 45,750,566 5773DB24
11. Ensemble Models\13. Gradient Boosting.mp4 [c1f15bad4bd77816] 10,077,729 3CC885F0
11. Ensemble Models\14. Gradient Boosting in Python.mp4 [896d00d0e7f08c5a] 13,003,560 428191DB
11. Ensemble Models\15. Hyperparameter Tuning for Gradient Boosting.mp4 [640e32757fd75626] 29,918,126 8D2294B8
11. Ensemble Models\16. DEMO Hyperparameter Tuning for Gradient Boosting.mp4 [9d53bdf2b31bf624] 24,036,865 9981CBD4
11. Ensemble Models\17. Pros & Cons of Gradient Boosting.mp4 [7efabee9313f8bd3] 11,264,292 8E9F8269
11. Ensemble Models\18. ASSIGNMENT Gradient Boosting.mp4 [e70040ffb42452c9] 5,183,788 F2F6A631
11. Ensemble Models\19. SOLUTION Gradient Boosting.mp4 [2e0249c189292307] 31,890,262 F6BEE985
11. Ensemble Models\2. Simple Ensemble Models.mp4 [ba44ecbf6c33374a] 12,944,376 4D9C05D2
11. Ensemble Models\20. PRO TIP SHAP Values.mp4 [a50f2830af367f5e] 33,679,138 99099A7D
11. Ensemble Models\21. DEMO SHAP Values.mp4 [6b30b6b59442f9ba] 30,119,418 F001E256
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