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| [TGx]Downloaded from torrentgalaxy.to .txt |
585B |
| 0 |
1.83KB |
| 1 |
433B |
| 1.1 NumPy Basics.pdf |
77.10KB |
| 1.1 Pandas.pdf |
110.18KB |
| 1.1 Supervised Learning.pdf |
836.74KB |
| 1.1 Unsupervised Learning.pdf |
636.55KB |
| 1.2 Pandas Basics.pdf |
77.06KB |
| 1. Creating A Data Science Resume.mp4 |
37.08MB |
| 1. Creating A Data Science Resume.srt |
10.52KB |
| 1. Data Visualization Overview.mp4 |
73.08MB |
| 1. Data Visualization Overview.srt |
36.78KB |
| 1. Decision Trees Section Overview.mp4 |
16.46MB |
| 1. Decision Trees Section Overview.srt |
5.60KB |
| 1. Ensemble Learning Section Overview.mp4 |
16.07MB |
| 1. Ensemble Learning Section Overview.srt |
5.17KB |
| 1. Exploratory Data Analysis.mp4 |
50.56MB |
| 1. Exploratory Data Analysis.srt |
19.07KB |
| 1. Feature Engineering.mp4 |
18.41MB |
| 1. Feature Engineering.srt |
9.42KB |
| 1. Feature Scaling.mp4 |
19.38MB |
| 1. Feature Scaling.srt |
11.58KB |
| 1. Introduction To Machine Learning.mp4 |
98.71MB |
| 1. Introduction To Machine Learning.srt |
36.98KB |
| 1. Introduction to Pandas.mp4 |
46.83MB |
| 1. Introduction to Pandas.srt |
22.30KB |
| 1. Intro NumPy Array Data Types.mp4 |
34.67MB |
| 1. Intro NumPy Array Data Types.srt |
18.29KB |
| 1. Intro To Statistics.mp4 |
21.24MB |
| 1. Intro To Statistics.srt |
11.17KB |
| 1. KNN Overview.mp4 |
12.89MB |
| 1. KNN Overview.srt |
4.27KB |
| 1. Linear Regression Intro.mp4 |
30.80MB |
| 1. Linear Regression Intro.srt |
12.16KB |
| 1. PCA Section Overview.mp4 |
31.77MB |
| 1. PCA Section Overview.srt |
7.06KB |
| 1. SVM Outline.mp4 |
35.31MB |
| 1. SVM Outline.srt |
7.40KB |
| 1. Unsupervised Machine Learning Intro.mp4 |
100.92MB |
| 1. Unsupervised Machine Learning Intro.srt |
29.36KB |
| 1. What is Exactly is Probability.mp4 |
27.17MB |
| 1. What is Exactly is Probability.srt |
6.73KB |
| 1. What is Programming.mp4 |
18.35MB |
| 1. What is Programming.srt |
9.03KB |
| 1. Who is This Course For.mp4 |
17.16MB |
| 1. Who is This Course For.srt |
3.95KB |
| 1. Why We Use Python.mp4 |
13.51MB |
| 1. Why We Use Python.srt |
4.91KB |
| 10 |
60.42KB |
| 10. Feature scaling in KNN.mp4 |
49.39MB |
| 10. Feature scaling in KNN.srt |
8.06KB |
| 10. PCA - Feature Scaling and Screen Plot.mp4 |
68.20MB |
| 10. PCA - Feature Scaling and Screen Plot.srt |
14.40KB |
| 10. Python Conditional Statements.mp4 |
54.61MB |
| 10. Python Conditional Statements.srt |
18.15KB |
| 10. Random Forests Pros and Cons.mp4 |
19.69MB |
| 10. Random Forests Pros and Cons.srt |
7.85KB |
| 10. SMV - Project Overview.mp4 |
39.61MB |
| 10. SMV - Project Overview.srt |
6.23KB |
| 10. Visualizing the tree.mp4 |
68.17MB |
| 10. Visualizing the tree.srt |
15.00KB |
| 100 |
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| 101 |
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| 102 |
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| 106 |
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| 107 |
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| 108 |
528.47KB |
| 109 |
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| 11 |
554.95KB |
| 11. Curse of dimensionality.mp4 |
45.99MB |
| 11. Curse of dimensionality.srt |
9.60KB |
| 11. PCA - Supervised vs Unsupervised.mp4 |
35.79MB |
| 11. PCA - Supervised vs Unsupervised.srt |
7.12KB |
| 11. Plot the features importance.mp4 |
31.67MB |
| 11. Plot the features importance.srt |
7.75KB |
| 11. Python For Loops and While Loops.mp4 |
25.61MB |
| 11. Python For Loops and While Loops.srt |
10.74KB |
| 11. What is Boosting.mp4 |
35.44MB |
| 11. What is Boosting.srt |
6.86KB |
| 110 |
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| 112 |
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| 116 |
603.58KB |
| 117 |
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| 118 |
142.78KB |
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143.06KB |
| 12 |
33.74KB |
| 12. AdaBoost Part 1.mp4 |
25.53MB |
| 12. AdaBoost Part 1.srt |
5.50KB |
| 12. Decision Trees Hyper-parameters.mp4 |
81.27MB |
| 12. Decision Trees Hyper-parameters.srt |
16.12KB |
| 12. KNN use cases.mp4 |
28.92MB |
| 12. KNN use cases.srt |
4.85KB |
| 12. PCA - Visualization.mp4 |
68.02MB |
| 12. PCA - Visualization.srt |
10.96KB |
| 12. Python Lists.mp4 |
21.44MB |
| 12. Python Lists.srt |
7.12KB |
| 120 |
862.71KB |
| 121 |
45.85KB |
| 122 |
65.97KB |
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| 126 |
50.47KB |
| 127 |
73.94KB |
| 128 |
374.41KB |
| 129 |
648.74KB |
| 13 |
461.98KB |
| 13. AdaBoost Part 2.mp4 |
85.94MB |
| 13. AdaBoost Part 2.srt |
20.87KB |
| 13. KNN pros and cons.mp4 |
30.45MB |
| 13. KNN pros and cons.srt |
7.75KB |
| 13. More about Lists.mp4 |
60.42MB |
| 13. More about Lists.srt |
19.44KB |
| 13. Pruning.mp4 |
112.97MB |
| 13. Pruning.srt |
24.35KB |
| 130 |
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| 131 |
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| 132 |
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| 134 |
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| 135 |
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| 136 |
101.79KB |
| 137 |
761.13KB |
| 138 |
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| 14 |
636.46KB |
| 14. [Optional] Gain Ration.mp4 |
19.18MB |
| 14. [Optional] Gain Ration.srt |
3.70KB |
| 14. Python Tuples.mp4 |
54.53MB |
| 14. Python Tuples.srt |
14.96KB |
| 15 |
391.78KB |
| 15. Decision Trees Pros and Cons.mp4 |
47.74MB |
| 15. Decision Trees Pros and Cons.srt |
10.60KB |
| 15. Python Dictionaries.mp4 |
104.18MB |
| 15. Python Dictionaries.srt |
27.72KB |
| 16 |
841.00KB |
| 16. [Project] Predict whether income exceeds $50Kyr - Overview.mp4 |
15.11MB |
| 16. [Project] Predict whether income exceeds $50Kyr - Overview.srt |
3.60KB |
| 16. Python Sets.mp4 |
29.43MB |
| 16. Python Sets.srt |
13.48KB |
| 17 |
449.09KB |
| 17. Compound Data Types & When to use each one.mp4 |
47.07MB |
| 17. Compound Data Types & When to use each one.srt |
17.98KB |
| 18 |
77.60KB |
| 18. Python Functions.mp4 |
62.51MB |
| 18. Python Functions.srt |
20.84KB |
| 19 |
294.34KB |
| 19. Object Oriented Programming in Python.mp4 |
70.25MB |
| 19. Object Oriented Programming in Python.srt |
25.73KB |
| 2 |
45B |
| 2.1 Importing Python Data.pdf |
61.55KB |
| 2.2 Python Basics.pdf |
127.71KB |
| 2. Data Cleaning.mp4 |
30.21MB |
| 2. Data Cleaning.srt |
11.53KB |
| 2. Data Science + Machine Learning Marketplace.mp4 |
46.94MB |
| 2. Data Science + Machine Learning Marketplace.srt |
10.40KB |
| 2. Data Science Cover Letter.mp4 |
22.96MB |
| 2. Data Science Cover Letter.srt |
5.99KB |
| 2. Descriptive Statistics.mp4 |
21.48MB |
| 2. Descriptive Statistics.srt |
10.04KB |
| 2. Different Data Visualization Libraries in Python.mp4 |
15.95MB |
| 2. Different Data Visualization Libraries in Python.srt |
8.76KB |
| 2. EDA on Adult Dataset.mp4 |
123.19MB |
| 2. EDA on Adult Dataset.srt |
23.77KB |
| 2. Expected Values.mp4 |
14.72MB |
| 2. Expected Values.srt |
4.11KB |
| 2. Gradient Descent.mp4 |
15.93MB |
| 2. Gradient Descent.srt |
8.44KB |
| 2. Introduction to Pandas Continued.mp4 |
71.10MB |
| 2. Introduction to Pandas Continued.srt |
26.81KB |
| 2. NumPy Arrays.mp4 |
32.33MB |
| 2. NumPy Arrays.srt |
11.09KB |
| 2. parametric vs non-parametric models.mp4 |
15.63MB |
| 2. parametric vs non-parametric models.srt |
4.73KB |
| 2. SVM intuition.mp4 |
48.86MB |
| 2. SVM intuition.srt |
16.01KB |
| 2. Unsupervised Machine Learning Continued.mp4 |
83.13MB |
| 2. Unsupervised Machine Learning Continued.srt |
29.19KB |
| 2. What is Data Science.mp4 |
87.99MB |
| 2. What is Data Science.srt |
21.23KB |
| 2. What is Ensemble Learning.mp4 |
91.97MB |
| 2. What is Ensemble Learning.srt |
17.42KB |
| 2. What is PCA.mp4 |
47.26MB |
| 2. What is PCA.srt |
14.59KB |
| 2. Why Python for Data Science.mp4 |
16.32MB |
| 2. Why Python for Data Science.srt |
6.77KB |
| 20 |
28.67KB |
| 21 |
713.81KB |
| 22 |
5.19KB |
| 23 |
26.11KB |
| 24 |
244.21KB |
| 25 |
60.39KB |
| 26 |
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| 27 |
604.93KB |
| 28 |
894.66KB |
| 29 |
746.62KB |
| 3 |
256B |
| 3.1 Jupyter Notebook.pdf |
307.15KB |
| 3. Data Science Job Opportunities.mp4 |
29.42MB |
| 3. Data Science Job Opportunities.srt |
6.86KB |
| 3. EDA on Iris Dataset.mp4 |
161.88MB |
| 3. EDA on Iris Dataset.srt |
31.65KB |
| 3. Hard vs Soft Margins.mp4 |
65.64MB |
| 3. Hard vs Soft Margins.srt |
18.92KB |
| 3. How to Contact Recruiters.mp4 |
24.65MB |
| 3. How to Contact Recruiters.srt |
7.34KB |
| 3. Linear Regression + Correlation Methods.mp4 |
110.38MB |
| 3. Linear Regression + Correlation Methods.srt |
38.61KB |
| 3. Measure of Variability.mp4 |
38.20MB |
| 3. Measure of Variability.srt |
18.19KB |
| 3. NumPy Arrays Basics.mp4 |
39.98MB |
| 3. NumPy Arrays Basics.srt |
16.84KB |
| 3. PCA Drawbacks.mp4 |
19.44MB |
| 3. PCA Drawbacks.srt |
4.90KB |
| 3. Python Data Visualization Implementation.mp4 |
27.43MB |
| 3. Python Data Visualization Implementation.srt |
12.44KB |
| 3. Relative Frequency.mp4 |
32.69MB |
| 3. Relative Frequency.srt |
8.67KB |
| 3. Representing Clusters.mp4 |
109.62MB |
| 3. Representing Clusters.srt |
28.32KB |
| 3. What is Bootstrap Sampling.mp4 |
55.88MB |
| 3. What is Bootstrap Sampling.srt |
11.13KB |
| 3. What is Entropy and Information Gain.mp4 |
136.08MB |
| 3. What is Entropy and Information Gain.srt |
29.33KB |
| 3. What is Jupyter.mp4 |
14.57MB |
| 3. What is Jupyter.srt |
5.92KB |
| 3. What is Machine Learning.mp4 |
83.41MB |
| 3. What is Machine Learning.srt |
22.93KB |
| 30 |
201.37KB |
| 31 |
537.54KB |
| 32 |
21.45KB |
| 33 |
195.71KB |
| 34 |
977.41KB |
| 35 |
81.60KB |
| 36 |
942.37KB |
| 37 |
925.90KB |
| 38 |
763.91KB |
| 39 |
820.11KB |
| 4 |
5B |
| 4. C hyper-parameter.mp4 |
21.06MB |
| 4. C hyper-parameter.srt |
5.64KB |
| 4. Data Science Job Roles.mp4 |
79.80MB |
| 4. Data Science Job Roles.srt |
15.73KB |
| 4. Getting Started with Freelancing.mp4 |
30.24MB |
| 4. Getting Started with Freelancing.srt |
7.08KB |
| 4. Hypothesis Testing Overview.mp4 |
60.59MB |
| 4. Hypothesis Testing Overview.srt |
14.58KB |
| 4. Linear Regression Implementation.mp4 |
17.86MB |
| 4. Linear Regression Implementation.srt |
6.88KB |
| 4. Machine Learning Concepts & Algorithms.mp4 |
77.98MB |
| 4. Machine Learning Concepts & Algorithms.srt |
23.56KB |
| 4. Measure of Variability Continued.mp4 |
34.61MB |
| 4. Measure of Variability Continued.srt |
13.32KB |
| 4. NumPy Array Indexing.mp4 |
34.74MB |
| 4. NumPy Array Indexing.srt |
14.02KB |
| 4. PCA Algorithm Steps (Mathematics).mp4 |
57.73MB |
| 4. PCA Algorithm Steps (Mathematics).srt |
18.59KB |
| 4. The Decision Tree ID3 algorithm from scratch Part 1.mp4 |
85.27MB |
| 4. The Decision Tree ID3 algorithm from scratch Part 1.srt |
14.91KB |
| 4. The KNN Intuition.mp4 |
8.09MB |
| 4. The KNN Intuition.srt |
3.06KB |
| 4. What is Bagging.mp4 |
29.48MB |
| 4. What is Bagging.srt |
7.75KB |
| 4. What is Google Colab.mp4 |
8.26MB |
| 4. What is Google Colab.srt |
4.89KB |
| 40 |
849.42KB |
| 41 |
1004.12KB |
| 42 |
369.93KB |
| 43 |
429.60KB |
| 44 |
39.64KB |
| 45 |
500.60KB |
| 46 |
420.86KB |
| 47 |
590.20KB |
| 48 |
276.84KB |
| 49 |
750.84KB |
| 5 |
2.08KB |
| 5. Covariance Matrix vs SVD.mp4 |
38.74MB |
| 5. Covariance Matrix vs SVD.srt |
6.54KB |
| 5. Implement the KNN algorithm from scratch.mp4 |
86.97MB |
| 5. Implement the KNN algorithm from scratch.srt |
17.14KB |
| 5. Kernel Trick.mp4 |
77.05MB |
| 5. Kernel Trick.srt |
18.19KB |
| 5. Logistic Regression.mp4 |
8.90MB |
| 5. Logistic Regression.srt |
4.93KB |
| 5. Measures of Variable Relationship.mp4 |
23.57MB |
| 5. Measures of Variable Relationship.srt |
10.72KB |
| 5. NumPy Array Computations.mp4 |
16.96MB |
| 5. NumPy Array Computations.srt |
8.60KB |
| 5. Out-of-Bag Error (OOB Error).mp4 |
42.03MB |
| 5. Out-of-Bag Error (OOB Error).srt |
9.97KB |
| 5. Python Variables, Booleans and None.mp4 |
38.26MB |
| 5. Python Variables, Booleans and None.srt |
15.22KB |
| 5. The Decision Tree ID3 algorithm from scratch Part 2.mp4 |
63.96MB |
| 5. The Decision Tree ID3 algorithm from scratch Part 2.srt |
10.62KB |
| 5. Top Freelance Websites.mp4 |
29.55MB |
| 5. Top Freelance Websites.srt |
8.37KB |
| 5. What is a Data Scientist.mp4 |
127.47MB |
| 5. What is a Data Scientist.srt |
26.86KB |
| 5. What is Deep Learning.mp4 |
77.81MB |
| 5. What is Deep Learning.srt |
15.83KB |
| 50 |
125.12KB |
| 51 |
398.01KB |
| 52 |
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| 53 |
452.16KB |
| 54 |
510.82KB |
| 55 |
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| 56 |
147.12KB |
| 57 |
265.59KB |
| 58 |
760.38KB |
| 59 |
954.32KB |
| 6 |
35B |
| 6. Broadcasting.mp4 |
17.86MB |
| 6. Broadcasting.srt |
6.36KB |
| 6. Compare the result with the sklearn library.mp4 |
24.57MB |
| 6. Compare the result with the sklearn library.srt |
5.10KB |
| 6. Getting Started with Google Colab.mp4 |
35.09MB |
| 6. Getting Started with Google Colab.srt |
12.39KB |
| 6. How To Get a Data Science Job.mp4 |
131.19MB |
| 6. How To Get a Data Science Job.srt |
30.62KB |
| 6. Implementing Random Forests from scratch Part 1.mp4 |
202.55MB |
| 6. Implementing Random Forests from scratch Part 1.srt |
30.12KB |
| 6. Inferential Statistics.mp4 |
45.01MB |
| 6. Inferential Statistics.srt |
22.10KB |
| 6. Machine Learning vs Deep Learning.mp4 |
75.92MB |
| 6. Machine Learning vs Deep Learning.srt |
17.90KB |
| 6. PCA - Main Applications.mp4 |
10.05MB |
| 6. PCA - Main Applications.srt |
3.90KB |
| 6. Personal Branding.mp4 |
30.49MB |
| 6. Personal Branding.srt |
6.42KB |
| 6. SVM - Kernel Types.mp4 |
126.38MB |
| 6. SVM - Kernel Types.srt |
26.75KB |
| 6. The Decision Tree ID3 algorithm from scratch Part 3.mp4 |
33.41MB |
| 6. The Decision Tree ID3 algorithm from scratch Part 3.srt |
5.75KB |
| 60 |
63.74KB |
| 61 |
173.71KB |
| 62 |
11.56KB |
| 63 |
1015.51KB |
| 64 |
996.71KB |
| 65 |
22.17KB |
| 66 |
342.12KB |
| 67 |
398.70KB |
| 68 |
264.54KB |
| 69 |
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| 7 |
67B |
| 7. Data Science Projects Overview.mp4 |
79.48MB |
| 7. Data Science Projects Overview.srt |
19.09KB |
| 7. Hyperparameter tuning using the cross-validation.mp4 |
90.30MB |
| 7. Hyperparameter tuning using the cross-validation.srt |
14.66KB |
| 7. ID3 - Putting Everything Together.mp4 |
182.48MB |
| 7. ID3 - Putting Everything Together.srt |
31.03KB |
| 7. Implementing Random Forests from scratch Part 2.mp4 |
50.50MB |
| 7. Implementing Random Forests from scratch Part 2.srt |
8.27KB |
| 7. Measure of Asymmetry.mp4 |
6.76MB |
| 7. Measure of Asymmetry.srt |
2.75KB |
| 7. Networking Do's and Don'ts.mp4 |
23.70MB |
| 7. Networking Do's and Don'ts.srt |
6.28KB |
| 7. PCA - Image Compression.mp4 |
249.92MB |
| 7. PCA - Image Compression.srt |
39.37KB |
| 7. Python Operators.mp4 |
86.76MB |
| 7. Python Operators.srt |
31.44KB |
| 7. SVM with Linear Dataset (Iris).mp4 |
101.56MB |
| 7. SVM with Linear Dataset (Iris).srt |
19.85KB |
| 70 |
821.74KB |
| 71 |
940.89KB |
| 72 |
216.68KB |
| 73 |
573.58KB |
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| 75 |
933.60KB |
| 76 |
263.89KB |
| 77 |
333.23KB |
| 78 |
396.20KB |
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| 8 |
364.83KB |
| 8. Compare with sklearn implementation.mp4 |
27.65MB |
| 8. Compare with sklearn implementation.srt |
4.94KB |
| 8. Evaluating our ID3 implementation.mp4 |
121.94MB |
| 8. Evaluating our ID3 implementation.srt |
24.54KB |
| 8. Importance of a Website.mp4 |
15.37MB |
| 8. Importance of a Website.srt |
4.79KB |
| 8. PCA Data Preprocessing.mp4 |
120.46MB |
| 8. PCA Data Preprocessing.srt |
21.05KB |
| 8. Python Numbers & Booleans.mp4 |
25.61MB |
| 8. Python Numbers & Booleans.srt |
9.60KB |
| 8. Sampling Distribution.mp4 |
26.46MB |
| 8. Sampling Distribution.srt |
10.25KB |
| 8. SVM with Non-linear Dataset.mp4 |
111.55MB |
| 8. SVM with Non-linear Dataset.srt |
18.38KB |
| 8. The decision boundary visualization.mp4 |
16.94MB |
| 8. The decision boundary visualization.srt |
7.07KB |
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| 81 |
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| 9 |
826.00KB |
| 9. Compare with Sklearn implementation.mp4 |
65.58MB |
| 9. Compare with Sklearn implementation.srt |
12.27KB |
| 9. Manhattan vs Euclidean Distance.mp4 |
30.49MB |
| 9. Manhattan vs Euclidean Distance.srt |
7.75KB |
| 9. PCA - Biplot and the Screen Plot.mp4 |
135.60MB |
| 9. PCA - Biplot and the Screen Plot.srt |
26.37KB |
| 9. Python Strings.mp4 |
56.27MB |
| 9. Python Strings.srt |
16.14KB |
| 9. Random Forests Hyper-Parameters.mp4 |
39.67MB |
| 9. Random Forests Hyper-Parameters.srt |
5.97KB |
| 9. SVM with Regression.mp4 |
25.00MB |
| 9. SVM with Regression.srt |
8.00KB |
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| TutsNode.com.txt |
63B |