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| [TGx]Downloaded from torrentgalaxy.to .txt |
585B |
| 0 |
16B |
| 1 |
18B |
| 1.1 11-Logistic-Regression-Models.zip |
2.02MB |
| 1.1 12-K-Nearest-Neighbors.zip |
1.35MB |
| 1.1 13-Support-Vector-Machines.zip |
1.51MB |
| 1.1 14-Decision-Trees.zip |
1.79MB |
| 1.1 data_banknote_authentication.csv |
45.38KB |
| 1.2 15-Random-Forests.zip |
3.94MB |
| 1. A note from Jose on Feature Engineering and Data Preparation.html |
990B |
| 1. Capstone Project Overview.mp4 |
93.20MB |
| 1. Capstone Project Overview.srt |
20.60KB |
| 1. EARLY BIRD INFO.html |
550B |
| 1. Early Bird Note on Downloading .zip for Logistic Regression Notes.html |
523B |
| 1. Introduction to KNN Section.mp4 |
11.41MB |
| 1. Introduction to KNN Section.srt |
3.63KB |
| 1. Introduction to Linear Regression Section.mp4 |
8.87MB |
| 1. Introduction to Linear Regression Section.srt |
2.68KB |
| 1. Introduction to Machine Learning Overview Section.mp4 |
29.73MB |
| 1. Introduction to Machine Learning Overview Section.srt |
8.58KB |
| 1. Introduction to Matplotlib.mp4 |
21.57MB |
| 1. Introduction to Matplotlib.srt |
6.72KB |
| 1. Introduction to NumPy.mp4 |
11.28MB |
| 1. Introduction to NumPy.srt |
3.01KB |
| 1. Introduction to Pandas.mp4 |
21.01MB |
| 1. Introduction to Pandas.srt |
7.24KB |
| 1. Introduction to Random Forests Section.mp4 |
9.49MB |
| 1. Introduction to Random Forests Section.srt |
2.81KB |
| 1. Introduction to Seaborn.mp4 |
20.00MB |
| 1. Introduction to Seaborn.srt |
6.51KB |
| 1. Introduction to Support Vector Machines.mp4 |
9.39MB |
| 1. Introduction to Support Vector Machines.srt |
2.30KB |
| 1. Introduction to Tree Based Methods.mp4 |
7.43MB |
| 1. Introduction to Tree Based Methods.srt |
2.21KB |
| 1. Machine Learning Pathway.mp4 |
40.54MB |
| 1. Machine Learning Pathway.srt |
15.79KB |
| 1. OPTIONAL Python Crash Course.html |
472B |
| 1. Section Overview and Introduction.mp4 |
20.53MB |
| 1. Section Overview and Introduction.srt |
5.05KB |
| 10 |
1.22MB |
| 10. Classification Metrics - Precison, Recall, F1-Score.mp4 |
33.06MB |
| 10. Classification Metrics - Precison, Recall, F1-Score.srt |
8.34KB |
| 10. Coding Regression with Random Forest Regressor - Part Three - Polynomials.mp4 |
60.02MB |
| 10. Coding Regression with Random Forest Regressor - Part Three - Polynomials.srt |
15.34KB |
| 10. Linear Regression - Residual Plots.mp4 |
59.52MB |
| 10. Linear Regression - Residual Plots.srt |
20.22KB |
| 10. Matplotlib Exercise Questions Overview.mp4 |
50.78MB |
| 10. Matplotlib Exercise Questions Overview.srt |
9.33KB |
| 10. Pandas - Useful Methods - Apply on Single Column.mp4 |
73.05MB |
| 10. Pandas - Useful Methods - Apply on Single Column.srt |
20.23KB |
| 10. Seaborn - Comparison Plots - Coding with Seaborn.mp4 |
70.16MB |
| 10. Seaborn - Comparison Plots - Coding with Seaborn.srt |
15.70KB |
| 10. Support Vector Machine Project Solutions.mp4 |
108.85MB |
| 10. Support Vector Machine Project Solutions.srt |
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| 11 |
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| 11. Classification Metrics - ROC Curves.mp4 |
34.29MB |
| 11. Classification Metrics - ROC Curves.srt |
11.06KB |
| 11. Coding Regression with Random Forest Regressor - Part Four - Advanced Models.mp4 |
59.02MB |
| 11. Coding Regression with Random Forest Regressor - Part Four - Advanced Models.srt |
15.45KB |
| 11. Linear Regression - Model Deployment and Coefficient Interpretation.mp4 |
88.19MB |
| 11. Linear Regression - Model Deployment and Coefficient Interpretation.srt |
25.62KB |
| 11. Matplotlib Exercise Questions - Solutions.mp4 |
123.11MB |
| 11. Matplotlib Exercise Questions - Solutions.srt |
24.53KB |
| 11. Pandas - Useful Methods - Apply on Multiple Columns.mp4 |
98.55MB |
| 11. Pandas - Useful Methods - Apply on Multiple Columns.srt |
25.93KB |
| 11. Seaborn Grid Plots.mp4 |
91.62MB |
| 11. Seaborn Grid Plots.srt |
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| 110 |
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| 12. Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.mp4 |
74.21MB |
| 12. Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.srt |
23.43KB |
| 12. Pandas - Useful Methods - Statistical Information and Sorting.mp4 |
85.65MB |
| 12. Pandas - Useful Methods - Statistical Information and Sorting.srt |
23.40KB |
| 12. Polynomial Regression - Theory and Motivation.mp4 |
44.24MB |
| 12. Polynomial Regression - Theory and Motivation.srt |
11.21KB |
| 12. Seaborn - Matrix Plots.mp4 |
71.25MB |
| 12. Seaborn - Matrix Plots.srt |
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| 13 |
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| 13. Missing Data - Overview.mp4 |
53.18MB |
| 13. Missing Data - Overview.srt |
18.36KB |
| 13. Multi-Class Classification with Logistic Regression - Part One - Data and EDA.mp4 |
44.03MB |
| 13. Multi-Class Classification with Logistic Regression - Part One - Data and EDA.srt |
12.01KB |
| 13. Polynomial Regression - Creating Polynomial Features.mp4 |
52.62MB |
| 13. Polynomial Regression - Creating Polynomial Features.srt |
16.39KB |
| 13. Seaborn Plot Exercises Overview.mp4 |
49.91MB |
| 13. Seaborn Plot Exercises Overview.srt |
11.26KB |
| 130 |
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| 14 |
1.63MB |
| 14. Missing Data - Pandas Operations.mp4 |
97.86MB |
| 14. Missing Data - Pandas Operations.srt |
27.41KB |
| 14. Multi-Class Classification with Logistic Regression - Part Two - Model.mp4 |
110.96MB |
| 14. Multi-Class Classification with Logistic Regression - Part Two - Model.srt |
23.82KB |
| 14. Polynomial Regression - Training and Evaluation.mp4 |
48.87MB |
| 14. Polynomial Regression - Training and Evaluation.srt |
14.17KB |
| 14. Seaborn Plot Exercises Solutions.mp4 |
110.60MB |
| 14. Seaborn Plot Exercises Solutions.srt |
22.40KB |
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1.63MB |
| 149 |
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| 15 |
774.24KB |
| 15. Bias Variance Trade-Off.mp4 |
43.04MB |
| 15. Bias Variance Trade-Off.srt |
15.94KB |
| 15. GroupBy Operations - Part One.mp4 |
93.11MB |
| 15. GroupBy Operations - Part One.srt |
21.41KB |
| 15. Logistic Regression Exercise Project Overview.mp4 |
35.80MB |
| 15. Logistic Regression Exercise Project Overview.srt |
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| 16 |
968.53KB |
| 16. GroupBy Operations - Part Two - MultiIndex.mp4 |
105.86MB |
| 16. GroupBy Operations - Part Two - MultiIndex.srt |
20.86KB |
| 16. Logistic Regression Project Exercise - Solutions.mp4 |
168.39MB |
| 16. Logistic Regression Project Exercise - Solutions.srt |
35.59KB |
| 16. Polynomial Regression - Choosing Degree of Polynomial.mp4 |
72.93MB |
| 16. Polynomial Regression - Choosing Degree of Polynomial.srt |
19.88KB |
| 160 |
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| 17 |
1.04MB |
| 17. Combining DataFrames - Concatenation.mp4 |
50.51MB |
| 17. Combining DataFrames - Concatenation.srt |
15.02KB |
| 17. Polynomial Regression - Model Deployment.mp4 |
28.94MB |
| 17. Polynomial Regression - Model Deployment.srt |
8.38KB |
| 18 |
1.40MB |
| 18. Combining DataFrames - Inner Merge.mp4 |
53.61MB |
| 18. Combining DataFrames - Inner Merge.srt |
18.52KB |
| 18. Regularization Overview.mp4 |
33.34MB |
| 18. Regularization Overview.srt |
10.33KB |
| 19 |
273.01KB |
| 19. Combining DataFrames - Left and Right Merge.mp4 |
27.90MB |
| 19. Combining DataFrames - Left and Right Merge.srt |
9.10KB |
| 19. Feature Scaling.mp4 |
53.97MB |
| 19. Feature Scaling.srt |
14.83KB |
| 2 |
20B |
| 2.1 UNZIP_ME_FOR_NOTEBOOKS_V4.zip |
35.69MB |
| 2. Capstone Project Solutions - Part One.mp4 |
116.95MB |
| 2. Capstone Project Solutions - Part One.srt |
26.84KB |
| 2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.mp4 |
24.55MB |
| 2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.srt |
7.17KB |
| 2. Cross Validation - Test Train Split.mp4 |
60.46MB |
| 2. Cross Validation - Test Train Split.srt |
17.43KB |
| 2. Decision Tree - History.mp4 |
51.89MB |
| 2. Decision Tree - History.srt |
13.15KB |
| 2. History of Support Vector Machines.mp4 |
31.42MB |
| 2. History of Support Vector Machines.srt |
6.52KB |
| 2. Introduction to Feature Engineering and Data Preparation.mp4 |
78.11MB |
| 2. Introduction to Feature Engineering and Data Preparation.srt |
24.10KB |
| 2. Introduction to Logistic Regression Section.mp4 |
31.68MB |
| 2. Introduction to Logistic Regression Section.srt |
8.39KB |
| 2. KNN Classification - Theory and Intuition.mp4 |
50.19MB |
| 2. KNN Classification - Theory and Intuition.srt |
16.92KB |
| 2. Linear Regression - Algorithm History.mp4 |
54.71MB |
| 2. Linear Regression - Algorithm History.srt |
13.09KB |
| 2. Matplotlib Basics.mp4 |
53.61MB |
| 2. Matplotlib Basics.srt |
19.64KB |
| 2. NumPy Arrays.mp4 |
109.63MB |
| 2. NumPy Arrays.srt |
31.91KB |
| 2. Python Crash Course - Part One.mp4 |
29.52MB |
| 2. Python Crash Course - Part One.srt |
24.63KB |
| 2. Random Forests - History and Motivation.mp4 |
44.91MB |
| 2. Random Forests - History and Motivation.srt |
17.22KB |
| 2. Scatterplots with Seaborn.mp4 |
128.61MB |
| 2. Scatterplots with Seaborn.srt |
29.72KB |
| 2. Series - Part One.mp4 |
38.47MB |
| 2. Series - Part One.srt |
13.39KB |
| 2. Why Machine Learning.mp4 |
44.77MB |
| 2. Why Machine Learning.srt |
14.66KB |
| 20 |
376.13KB |
| 20. Combining DataFrames - Outer Merge.mp4 |
39.89MB |
| 20. Combining DataFrames - Outer Merge.srt |
14.57KB |
| 20. Introduction to Cross Validation.mp4 |
62.58MB |
| 20. Introduction to Cross Validation.srt |
19.81KB |
| 21 |
1.15MB |
| 21. Pandas - Text Methods for String Data.mp4 |
75.69MB |
| 21. Pandas - Text Methods for String Data.srt |
23.95KB |
| 21. Regularization Data Setup.mp4 |
34.44MB |
| 21. Regularization Data Setup.srt |
12.42KB |
| 22 |
1.35MB |
| 22. L2 Regularization - Ridge Regression Theory.mp4 |
61.09MB |
| 22. L2 Regularization - Ridge Regression Theory.srt |
20.72KB |
| 22. Pandas - Time Methods for Date and Time Data.mp4 |
101.92MB |
| 22. Pandas - Time Methods for Date and Time Data.srt |
31.72KB |
| 23 |
139.29KB |
| 23. L2 Regularization - Ridge Regression - Python Implementation.mp4 |
96.42MB |
| 23. L2 Regularization - Ridge Regression - Python Implementation.srt |
26.45KB |
| 23. Pandas Input and Output - CSV Files.mp4 |
49.87MB |
| 23. Pandas Input and Output - CSV Files.srt |
16.59KB |
| 24 |
834.26KB |
| 24. L1 Regularization - Lasso Regression - Background and Implementation.mp4 |
100.00MB |
| 24. L1 Regularization - Lasso Regression - Background and Implementation.srt |
22.44KB |
| 24. Pandas Input and Output - HTML Tables.mp4 |
106.65MB |
| 24. Pandas Input and Output - HTML Tables.srt |
22.36KB |
| 25 |
81.63KB |
| 25. L1 and L2 Regularization - Elastic Net.mp4 |
93.41MB |
| 25. L1 and L2 Regularization - Elastic Net.srt |
25.72KB |
| 25. Pandas Input and Output - Excel Files.mp4 |
34.58MB |
| 25. Pandas Input and Output - Excel Files.srt |
10.88KB |
| 26 |
2.00MB |
| 26. Linear Regression Project - Data Overview.mp4 |
39.07MB |
| 26. Linear Regression Project - Data Overview.srt |
7.67KB |
| 26. Pandas Input and Output - SQL Databases.mp4 |
103.19MB |
| 26. Pandas Input and Output - SQL Databases.srt |
29.43KB |
| 27 |
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| 27. Pandas Pivot Tables.mp4 |
128.74MB |
| 27. Pandas Pivot Tables.srt |
32.18KB |
| 28 |
1.25MB |
| 28. Pandas Project Exercise Overview.mp4 |
41.07MB |
| 28. Pandas Project Exercise Overview.srt |
9.59KB |
| 29 |
1.45MB |
| 29. Pandas Project Exercise Solutions.mp4 |
181.60MB |
| 29. Pandas Project Exercise Solutions.srt |
38.76KB |
| 3 |
240B |
| 3.1 UNZIP_ME_FOR_NOTEBOOKS_V4.zip |
35.69MB |
| 3. Anaconda Python and Jupyter Install and Setup.mp4 |
98.75MB |
| 3. Anaconda Python and Jupyter Install and Setup.srt |
21.55KB |
| 3. Capstone Project Solutions - Part Two.mp4 |
111.05MB |
| 3. Capstone Project Solutions - Part Two.srt |
23.48KB |
| 3. Check-in Labeled Index in Pandas Series.html |
163B |
| 3. Coding Exercise Check-in Creating NumPy Arrays.html |
163B |
| 3. Cross Validation - Test Validation Train Split.mp4 |
77.29MB |
| 3. Cross Validation - Test Validation Train Split.srt |
21.65KB |
| 3. Dealing with Outliers.mp4 |
141.01MB |
| 3. Dealing with Outliers.srt |
41.20KB |
| 3. Decision Tree - Terminology.mp4 |
15.06MB |
| 3. Decision Tree - Terminology.srt |
6.42KB |
| 3. Distribution Plots - Part One - Understanding Plot Types.mp4 |
32.05MB |
| 3. Distribution Plots - Part One - Understanding Plot Types.srt |
15.00KB |
| 3. KNN Coding with Python - Part One.mp4 |
83.24MB |
| 3. KNN Coding with Python - Part One.srt |
22.24KB |
| 3. Linear Regression - Understanding Ordinary Least Squares.mp4 |
86.26MB |
| 3. Linear Regression - Understanding Ordinary Least Squares.srt |
22.52KB |
| 3. Logistic Regression - Theory and Intuition - Part One The Logistic Function.mp4 |
34.17MB |
| 3. Logistic Regression - Theory and Intuition - Part One The Logistic Function.srt |
8.09KB |
| 3. Matplotlib - Understanding the Figure Object.mp4 |
25.81MB |
| 3. Matplotlib - Understanding the Figure Object.srt |
11.55KB |
| 3. Python Crash Course - Part Two.mp4 |
22.25MB |
| 3. Python Crash Course - Part Two.srt |
18.03KB |
| 3. Random Forests - Key Hyperparameters.mp4 |
19.13MB |
| 3. Random Forests - Key Hyperparameters.srt |
4.44KB |
| 3. SVM - Theory and Intuition - Hyperplanes and Margins.mp4 |
66.78MB |
| 3. SVM - Theory and Intuition - Hyperplanes and Margins.srt |
18.58KB |
| 3. Types of Machine Learning Algorithms.mp4 |
38.68MB |
| 3. Types of Machine Learning Algorithms.srt |
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| 4 |
408B |
| 4. Capstone Project Solutions - Part Three.mp4 |
143.96MB |
| 4. Capstone Project Solutions - Part Three.srt |
30.88KB |
| 4. Cross Validation - cross_val_score.mp4 |
57.73MB |
| 4. Cross Validation - cross_val_score.srt |
17.42KB |
| 4. Dealing with Missing Data Part One - Evaluation of Missing Data.mp4 |
56.66MB |
| 4. Dealing with Missing Data Part One - Evaluation of Missing Data.srt |
16.97KB |
| 4. Decision Tree - Understanding Gini Impurity.mp4 |
35.66MB |
| 4. Decision Tree - Understanding Gini Impurity.srt |
11.10KB |
| 4. Distribution Plots - Part Two - Coding with Seaborn.mp4 |
77.74MB |
| 4. Distribution Plots - Part Two - Coding with Seaborn.srt |
24.79KB |
| 4. KNN Coding with Python - Part Two - Choosing K.mp4 |
112.37MB |
| 4. KNN Coding with Python - Part Two - Choosing K.srt |
35.26KB |
| 4. Linear Regression - Cost Functions.mp4 |
36.02MB |
| 4. Linear Regression - Cost Functions.srt |
11.46KB |
| 4. Logistic Regression - Theory and Intuition - Part Two Linear to Logistic.mp4 |
24.37MB |
| 4. Logistic Regression - Theory and Intuition - Part Two Linear to Logistic.srt |
7.27KB |
| 4. Matplotlib - Implementing Figures and Axes.mp4 |
59.09MB |
| 4. Matplotlib - Implementing Figures and Axes.srt |
20.97KB |
| 4. Note on Environment Setup - Please read me!.html |
857B |
| 4. NumPy Indexing and Selection.mp4 |
46.35MB |
| 4. NumPy Indexing and Selection.srt |
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| 4. Python Crash Course - Part Three.mp4 |
23.17MB |
| 4. Python Crash Course - Part Three.srt |
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| 4. Random Forests - Number of Estimators and Features in Subsets.mp4 |
60.90MB |
| 4. Random Forests - Number of Estimators and Features in Subsets.srt |
16.16KB |
| 4. Series - Part Two.mp4 |
45.30MB |
| 4. Series - Part Two.srt |
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| 4. Supervised Machine Learning Process.mp4 |
71.42MB |
| 4. Supervised Machine Learning Process.srt |
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| 4. SVM - Theory and Intuition - Kernel Intuition.mp4 |
26.26MB |
| 4. SVM - Theory and Intuition - Kernel Intuition.srt |
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269B |
| 5.1 Backup Google Link for requirements.txt file.html |
143B |
| 5.2 requirements.txt |
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| 5. Categorical Plots - Statistics within Categories - Understanding Plot Types.mp4 |
21.86MB |
| 5. Categorical Plots - Statistics within Categories - Understanding Plot Types.srt |
8.80KB |
| 5. Coding Exercise Check-in Selecting Data from Numpy Array.html |
163B |
| 5. Companion Book - Introduction to Statistical Learning.mp4 |
19.29MB |
| 5. Companion Book - Introduction to Statistical Learning.srt |
4.66KB |
| 5. Constructing Decision Trees with Gini Impurity - Part One.mp4 |
38.32MB |
| 5. Constructing Decision Trees with Gini Impurity - Part One.srt |
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| 5. Cross Validation - cross_validate.mp4 |
47.61MB |
| 5. Cross Validation - cross_validate.srt |
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| 5. DataFrames - Part One - Creating a DataFrame.mp4 |
114.08MB |
| 5. DataFrames - Part One - Creating a DataFrame.srt |
29.00KB |
| 5. Dealing with Missing Data Part Two - Filling or Dropping data based on Rows.mp4 |
125.24MB |
| 5. Dealing with Missing Data Part Two - Filling or Dropping data based on Rows.srt |
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| 5. Environment Setup.mp4 |
49.32MB |
| 5. Environment Setup.srt |
14.49KB |
| 5. KNN Classification Project Exercise Overview.mp4 |
31.18MB |
| 5. KNN Classification Project Exercise Overview.srt |
5.23KB |
| 5. Linear Regression - Gradient Descent.mp4 |
65.04MB |
| 5. Linear Regression - Gradient Descent.srt |
16.73KB |
| 5. Logistic Regression - Theory and Intuition - Linear to Logistic Math.mp4 |
75.82MB |
| 5. Logistic Regression - Theory and Intuition - Linear to Logistic Math.srt |
24.81KB |
| 5. Matplotlib - Figure Parameters.mp4 |
23.75MB |
| 5. Matplotlib - Figure Parameters.srt |
7.65KB |
| 5. Python Crash Course - Exercise Questions.mp4 |
5.01MB |
| 5. Python Crash Course - Exercise Questions.srt |
2.53KB |
| 5. Random Forests - Bootstrapping and Out-of-Bag Error.mp4 |
63.32MB |
| 5. Random Forests - Bootstrapping and Out-of-Bag Error.srt |
17.97KB |
| 5. SVM - Theory and Intuition - Kernel Trick and Mathematics.mp4 |
93.86MB |
| 5. SVM - Theory and Intuition - Kernel Trick and Mathematics.srt |
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1.17MB |
| 6. Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4 |
54.99MB |
| 6. Categorical Plots - Statistics within Categories - Coding with Seaborn.srt |
14.61KB |
| 6. Coding Classification with Random Forest Classifier - Part One.mp4 |
68.49MB |
| 6. Coding Classification with Random Forest Classifier - Part One.srt |
18.08KB |
| 6. Constructing Decision Trees with Gini Impurity - Part Two.mp4 |
52.15MB |
| 6. Constructing Decision Trees with Gini Impurity - Part Two.srt |
16.42KB |
| 6. DataFrames - Part Two - Basic Properties.mp4 |
53.90MB |
| 6. DataFrames - Part Two - Basic Properties.srt |
13.28KB |
| 6. Dealing with Missing Data Part 3 - Fixing data based on Columns.mp4 |
122.78MB |
| 6. Dealing with Missing Data Part 3 - Fixing data based on Columns.srt |
36.75KB |
| 6. Grid Search.mp4 |
78.11MB |
| 6. Grid Search.srt |
19.26KB |
| 6. KNN Classification Project Exercise Solutions.mp4 |
109.73MB |
| 6. KNN Classification Project Exercise Solutions.srt |
21.40KB |
| 6. Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.mp4 |
76.83MB |
| 6. Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.srt |
22.96KB |
| 6. Matplotlib - Subplots Functionality.mp4 |
96.18MB |
| 6. Matplotlib - Subplots Functionality.srt |
28.63KB |
| 6. NumPy Operations.mp4 |
48.59MB |
| 6. NumPy Operations.srt |
12.05KB |
| 6. Python coding Simple Linear Regression.mp4 |
91.92MB |
| 6. Python coding Simple Linear Regression.srt |
28.14KB |
| 6. Python Crash Course - Exercise Solutions.mp4 |
25.10MB |
| 6. Python Crash Course - Exercise Solutions.srt |
13.43KB |
| 6. SVM with Scikit-Learn and Python - Classification Part One.mp4 |
62.71MB |
| 6. SVM with Scikit-Learn and Python - Classification Part One.srt |
16.38KB |
| 60 |
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1.84MB |
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1.51MB |
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1.22MB |
| 69 |
985.60KB |
| 7 |
1.39MB |
| 7. Categorical Plots - Distributions within Categories - Understanding Plot Types.mp4 |
61.09MB |
| 7. Categorical Plots - Distributions within Categories - Understanding Plot Types.srt |
20.10KB |
| 7. Check-In Operations on NumPy Array.html |
163B |
| 7. Coding Classification with Random Forest Classifier - Part Two.mp4 |
139.04MB |
| 7. Coding Classification with Random Forest Classifier - Part Two.srt |
32.15KB |
| 7. Coding Decision Trees - Part One - The Data.mp4 |
115.13MB |
| 7. Coding Decision Trees - Part One - The Data.srt |
29.27KB |
| 7. DataFrames - Part Three - Working with Columns.mp4 |
89.30MB |
| 7. DataFrames - Part Three - Working with Columns.srt |
20.61KB |
| 7. Dealing with Categorical Data - Encoding Options.mp4 |
78.74MB |
| 7. Dealing with Categorical Data - Encoding Options.srt |
20.10KB |
| 7. Linear Regression Project Overview.mp4 |
27.48MB |
| 7. Linear Regression Project Overview.srt |
5.82KB |
| 7. Logistic Regression with Scikit-Learn - Part One - EDA.mp4 |
73.22MB |
| 7. Logistic Regression with Scikit-Learn - Part One - EDA.srt |
21.90KB |
| 7. Matplotlib Styling - Legends.mp4 |
34.10MB |
| 7. Matplotlib Styling - Legends.srt |
10.35KB |
| 7. Overview of Scikit-Learn and Python.mp4 |
45.61MB |
| 7. Overview of Scikit-Learn and Python.srt |
12.34KB |
| 7. SVM with Scikit-Learn and Python - Classification Part Two.mp4 |
96.60MB |
| 7. SVM with Scikit-Learn and Python - Classification Part Two.srt |
23.94KB |
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1.29MB |
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1.42MB |
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1.54MB |
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1.98MB |
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492.64KB |
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| 8 |
781.77KB |
| 8. Categorical Plots - Distributions within Categories - Coding with Seaborn.mp4 |
111.24MB |
| 8. Categorical Plots - Distributions within Categories - Coding with Seaborn.srt |
28.26KB |
| 8. Coding Decision Trees - Part Two -Creating the Model.mp4 |
136.35MB |
| 8. Coding Decision Trees - Part Two -Creating the Model.srt |
32.69KB |
| 8. Coding Regression with Random Forest Regressor - Part One - Data.mp4 |
27.61MB |
| 8. Coding Regression with Random Forest Regressor - Part One - Data.srt |
6.86KB |
| 8. DataFrames - Part Four - Working with Rows.mp4 |
96.72MB |
| 8. DataFrames - Part Four - Working with Rows.srt |
21.08KB |
| 8. Linear Regression Project - Solutions.mp4 |
95.84MB |
| 8. Linear Regression Project - Solutions.srt |
18.29KB |
| 8. Linear Regression - Scikit-Learn Train Test Split.mp4 |
82.93MB |
| 8. Linear Regression - Scikit-Learn Train Test Split.srt |
23.78KB |
| 8. Logistic Regression with Scikit-Learn - Part Two - Model Training.mp4 |
35.26MB |
| 8. Logistic Regression with Scikit-Learn - Part Two - Model Training.srt |
9.57KB |
| 8. Matplotlib Styling - Colors and Styles.mp4 |
81.19MB |
| 8. Matplotlib Styling - Colors and Styles.srt |
21.04KB |
| 8. NumPy Exercises.mp4 |
11.52MB |
| 8. NumPy Exercises.srt |
2.07KB |
| 8. SVM with Scikit-Learn and Python - Regression Tasks.mp4 |
99.27MB |
| 8. SVM with Scikit-Learn and Python - Regression Tasks.srt |
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1.34MB |
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100.95KB |
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910.45KB |
| 9. Advanced Matplotlib Commands (Optional).mp4 |
40.44MB |
| 9. Advanced Matplotlib Commands (Optional).srt |
6.49KB |
| 9. Classification Metrics - Confusion Matrix and Accuracy.mp4 |
46.99MB |
| 9. Classification Metrics - Confusion Matrix and Accuracy.srt |
13.92KB |
| 9. Coding Regression with Random Forest Regressor - Part Two - Basic Models.mp4 |
89.73MB |
| 9. Coding Regression with Random Forest Regressor - Part Two - Basic Models.srt |
20.42KB |
| 9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp4 |
73.16MB |
| 9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.srt |
23.00KB |
| 9. Numpy Exercises - Solutions.mp4 |
48.57MB |
| 9. Numpy Exercises - Solutions.srt |
10.87KB |
| 9. Pandas - Conditional Filtering.mp4 |
90.05MB |
| 9. Pandas - Conditional Filtering.srt |
27.14KB |
| 9. Seaborn - Comparison Plots - Understanding the Plot Types.mp4 |
23.35MB |
| 9. Seaborn - Comparison Plots - Understanding the Plot Types.srt |
8.73KB |
| 9. Support Vector Machine Project Overview.mp4 |
40.46MB |
| 9. Support Vector Machine Project Overview.srt |
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| TutsNode.com.txt |
63B |