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[FTUForum com] [UDEMY] Complete Data Science Training with Python for Data Analysis [FTU]

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Name:[FTUForum com] [UDEMY] Complete Data Science Training with Python for Data Analysis [FTU]

Infohash: BDCF2933D59F4D0ED95A0EC7F904F79A4643F4B0

Total Size: 2.25 GB

Seeds: 0

Leechers: 1

Stream: Watch Full Movie @ Movie4u

Last Updated: 2024-05-19 09:35:16 (Update Now)

Torrent added: 2019-07-18 20:00:07






Torrent Files List


0. Websites you may like (Size: 2.25 GB) (Files: 253)

 0. Websites you may like

  1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url

0.32 KB

  2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url

0.28 KB

  3. (NulledPremium.com) Download Cracked Website Themes, Plugins, Scripts And Stock Images.url

0.16 KB

  4. (FTUApps.com) Download Cracked Developers Applications For Free.url

0.23 KB

  5. (Discuss.FTUForum.com) FTU Discussion Forum.url

0.29 KB

  How you can help Team-FTU.txt

0.23 KB

 1. Introduction to the Data Science in Python Bootcamp

  1. What is Data Science.mp4

17.39 MB

  1. What is Data Science.vtt

3.97 KB

  2. Introduction to the Course Instructor.m4v

55.61 MB

  2. Introduction to the Course Instructor.vtt

13.50 KB

  3. Data For the Course.html

0.10 KB

  3.1 scriptsLecture.zip.zip

308.04 MB

  4. Introduction to the Python Data Science Tool.mp4

25.02 MB

  4. Introduction to the Python Data Science Tool.vtt

10.12 KB

  5. For Mac Users.mp4

10.22 MB

  5. For Mac Users.vtt

3.88 KB

  6. Introduction to the Python Data Science Environment.mp4

40.32 MB

  6. Introduction to the Python Data Science Environment.vtt

17.22 KB

  7. Some Miscellaneous IPython Usage Facts.mp4

12.01 MB

  7. Some Miscellaneous IPython Usage Facts.vtt

4.54 KB

  8. Online iPython Interpreter.mp4

7.73 MB

  8. Online iPython Interpreter.vtt

3.43 KB

  9. Conclusion to Section 1.mp4

6.48 MB

  9. Conclusion to Section 1.vtt

3.06 KB

 10. Unsupervised Learning in Python

  1. Unsupervised Classification- Some Basic Ideas.mp4

6.17 MB

  1. Unsupervised Classification- Some Basic Ideas.vtt

1.81 KB

  10. Principal Component Analysis (PCA)-Practical Implementation.mp4

9.06 MB

  10. Principal Component Analysis (PCA)-Practical Implementation.vtt

4.15 KB

  11. Conclusions to Section 10.mp4

5.49 MB

  11. Conclusions to Section 10.vtt

2.48 KB

  2. KMeans-theory.mp4

5.15 MB

  2. KMeans-theory.vtt

2.50 KB

  3. KMeans-implementation on the iris data.mp4

19.54 MB

  3. KMeans-implementation on the iris data.vtt

7.61 KB

  4. Quantifying KMeans Clustering Performance.mp4

9.57 MB

  4. Quantifying KMeans Clustering Performance.vtt

4.41 KB

  5. KMeans Clustering with Real Data.mp4

12.08 MB

  5. KMeans Clustering with Real Data.vtt

4.49 KB

  6. How Do We Select the Number of Clusters.mp4

19.04 MB

  6. How Do We Select the Number of Clusters.vtt

4.21 KB

  7. Hierarchical Clustering-theory.mp4

10.23 MB

  7. Hierarchical Clustering-theory.vtt

5.00 KB

  8. Hierarchical Clustering-practical.mp4

29.39 MB

  8. Hierarchical Clustering-practical.vtt

9.53 KB

  9. Principal Component Analysis (PCA)-Theory.mp4

5.91 MB

  9. Principal Component Analysis (PCA)-Theory.vtt

2.96 KB

 11. Supervised Learning

  1. What is This Section About.mp4

24.88 MB

  1. What is This Section About.vtt

11.50 KB

  10. knn-Classification.mp4

18.20 MB

  10. knn-Classification.vtt

8.00 KB

  11. knn-Regression.mp4

8.38 MB

  11. knn-Regression.vtt

3.95 KB

  12. Gradient Boosting-classification.mp4

15.04 MB

  12. Gradient Boosting-classification.vtt

6.04 KB

  13. Gradient Boosting-regression.mp4

10.90 MB

  13. Gradient Boosting-regression.vtt

3.67 KB

  14. Voting Classifier.mp4

9.53 MB

  14. Voting Classifier.vtt

3.76 KB

  15. Conclusions to Section 11.mp4

7.23 MB

  15. Conclusions to Section 11.vtt

2.94 KB

  16. Section 11 Quiz.html

0.16 KB

  2. Data Preparation for Supervised Learning.mp4

28.28 MB

  2. Data Preparation for Supervised Learning.vtt

10.08 KB

  3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.mp4

24.00 MB

  3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.vtt

10.46 KB

  4. Using Logistic Regression as a Classification Model.mp4

20.64 MB

  4. Using Logistic Regression as a Classification Model.vtt

8.73 KB

  5. RF-Classification.mp4

28.48 MB

  5. RF-Classification.vtt

12.19 KB

  6. RF-Regression.mp4

23.63 MB

  6. RF-Regression.vtt

9.73 KB

  7. SVM- Linear Classification.mp4

7.39 MB

  7. SVM- Linear Classification.vtt

3.22 KB

  8. SVM- Non Linear Classification.mp4

5.12 MB

  8. SVM- Non Linear Classification.vtt

2.31 KB

  9. Support Vector Regression.mp4

10.19 MB

  9. Support Vector Regression.vtt

4.33 KB

 12. Artificial Neural Networks (ANN) and Deep Learning (DL)

  1. Theory Behind ANN and DNN.mp4

22.56 MB

  1. Theory Behind ANN and DNN.vtt

9.91 KB

  10. Specify the Activation Function.mp4

6.21 MB

  10. Specify the Activation Function.vtt

2.18 KB

  11. H2O Deep Learning For Predictions.mp4

12.00 MB

  11. H2O Deep Learning For Predictions.vtt

5.19 KB

  12. Conclusions to Section 12.mp4

5.16 MB

  12. Conclusions to Section 12.vtt

2.12 KB

  13. Section 12 Quiz.html

0.16 KB

  2. Perceptrons for Binary Classification.mp4

10.05 MB

  2. Perceptrons for Binary Classification.vtt

4.68 KB

  3. Getting Started with ANN-binary classification.mp4

8.46 MB

  3. Getting Started with ANN-binary classification.vtt

3.48 KB

  4. Multi-label classification with MLP.mp4

13.49 MB

  4. Multi-label classification with MLP.vtt

4.77 KB

  5. Regression with MLP.mp4

9.02 MB

  5. Regression with MLP.vtt

3.49 KB

  6. MLP with PCA on a Large Dataset.mp4

19.25 MB

  6. MLP with PCA on a Large Dataset.vtt

7.65 KB

  7. Start With Deep Neural Network (DNN).html

0.22 KB

  8. Start with H20.mp4

12.12 MB

  8. Start with H20.vtt

4.31 KB

  9. Default H2O Deep Learning Algorithm.mp4

8.23 MB

  9. Default H2O Deep Learning Algorithm.vtt

3.36 KB

 13. Miscellaneous Lectures Information

  1. Data For This Section.html

0.13 KB

  2. Read in Data from Online CSV.mp4

6.66 MB

  2. Read in Data from Online CSV.vtt

3.87 KB

  3. Read Data from a Database.mp4

12.26 MB

  3. Read Data from a Database.vtt

7.79 KB

  4. Naive Bayes Classification.m4v

28.16 MB

  4. Naive Bayes Classification.vtt

6.84 KB

  5. Data Imputation.m4v

44.84 MB

  5. Data Imputation.vtt

8.99 KB

 2. Introduction to Python Pre-Requisites for Data Science

  1. Rationale Behind This Section.html

0.42 KB

  2. Different Types of Data Used in Statistical ML Analysis.mp4

9.36 MB

  2. Different Types of Data Used in Statistical ML Analysis.vtt

3.66 KB

  3. Different Types of Data Used Programatically.mp4

7.74 MB

  3. Different Types of Data Used Programatically.vtt

3.01 KB

  4. Python Data Science Packages To Be Used.mp4

7.93 MB

  4. Python Data Science Packages To Be Used.vtt

3.80 KB

  5. Conclusions to Section 2.mp4

4.88 MB

  5. Conclusions to Section 2.vtt

2.43 KB

 3. Introduction to Numpy

  1. Numpy Introduction.mp4

8.70 MB

  1. Numpy Introduction.vtt

3.84 KB

  10. Conclusion to Section 3.mp4

6.17 MB

  10. Conclusion to Section 3.vtt

2.55 KB

  11. Section 3 Quiz.html

0.16 KB

  2. Create Numpy Arrays.mp4

20.91 MB

  2. Create Numpy Arrays.vtt

5.92 KB

  3. Numpy Operations.mp4

36.71 MB

  3. Numpy Operations.vtt

14.96 KB

  4. Matrix Arithmetic and Linear Systems.mp4

15.83 MB

  4. Matrix Arithmetic and Linear Systems.vtt

6.47 KB

  5. Numpy for Basic Vector Arithmetric.mp4

11.75 MB

  5. Numpy for Basic Vector Arithmetric.vtt

3.79 KB

  6. Numpy for Basic Matrix Arithmetic.mp4

13.89 MB

  6. Numpy for Basic Matrix Arithmetic.vtt

5.16 KB

  7. Broadcasting with Numpy.mp4

8.95 MB

  7. Broadcasting with Numpy.vtt

3.79 KB

  8. Solve Equations with Numpy.mp4

11.44 MB

  8. Solve Equations with Numpy.vtt

4.19 KB

  9. Numpy for Statistical Operation.mp4

14.95 MB

  9. Numpy for Statistical Operation.vtt

6.75 KB

 4. Introduction to Pandas

  1. Data Structures in Python.mp4

25.07 MB

  1. Data Structures in Python.vtt

10.04 KB

  2. Read in Data.html

0.24 KB

  3. Read in CSV Data Using Pandas.mp4

15.32 MB

  3. Read in CSV Data Using Pandas.vtt

5.79 KB

  4. Read in Excel Data Using Pandas.mp4

11.38 MB

  4. Read in Excel Data Using Pandas.vtt

3.78 KB

  5. Reading in JSON Data.mp4

18.72 MB

  5. Reading in JSON Data.vtt

3.06 KB

  6. Read in HTML Data.mp4

51.31 MB

  6. Read in HTML Data.vtt

11.14 KB

  7. Conclusion to Section 4.mp4

5.40 MB

  7. Conclusion to Section 4.vtt

2.24 KB

 5. Data Pre-ProcessingWrangling

  1. Rationale behind this section.mp4

8.11 MB

  1. Rationale behind this section.vtt

4.59 KB

  10. Rank and Sort Data.mp4

24.32 MB

  10. Rank and Sort Data.vtt

7.30 KB

  11. Concatenate.mp4

23.74 MB

  11. Concatenate.vtt

8.00 KB

  12. Merging and Joining Data Frames.mp4

28.80 MB

  12. Merging and Joining Data Frames.vtt

10.66 KB

  13. Conclusion to Section 5.mp4

5.39 MB

  13. Conclusion to Section 5.vtt

2.21 KB

  2. Removing NAsNo Values From Our Data.mp4

19.29 MB

  2. Removing NAsNo Values From Our Data.vtt

6.38 KB

  3. Basic Data Handling Starting with Conditional Data Selection.mp4

14.85 MB

  3. Basic Data Handling Starting with Conditional Data Selection.vtt

4.08 KB

  4. Drop ColumnRow.mp4

15.70 MB

  4. Drop ColumnRow.vtt

4.39 KB

  5. Subset and Index Data.mp4

28.00 MB

  5. Subset and Index Data.vtt

7.77 KB

  6. Basic Data Grouping Based on Qualitative Attributes.mp4

26.62 MB

  6. Basic Data Grouping Based on Qualitative Attributes.vtt

8.31 KB

  7. Crosstabulation.mp4

10.88 MB

  7. Crosstabulation.vtt

3.85 KB

  8. Reshaping.mp4

24.27 MB

  8. Reshaping.vtt

9.59 KB

  9. Pivoting.mp4

24.04 MB

  9. Pivoting.vtt

8.41 KB

 6. Introduction to Data Visualizations

  1. What is Data Visualization.mp4

20.72 MB

  1. What is Data Visualization.vtt

9.80 KB

  2. Some Theoretical Principles Behind Data Visualization.mp4

16.56 MB

  2. Some Theoretical Principles Behind Data Visualization.vtt

7.14 KB

  3. Histograms-Visualize the Distribution of Continuous Numerical Variables.mp4

29.41 MB

  3. Histograms-Visualize the Distribution of Continuous Numerical Variables.vtt

11.93 KB

  4. Boxplots-Visualize the Distribution of Continuous Numerical Variables.mp4

13.44 MB

  4. Boxplots-Visualize the Distribution of Continuous Numerical Variables.vtt

5.48 KB

  5. Scatter Plot-Visualize the Relationship Between 2 Continuous Variables.mp4

29.82 MB

  5. Scatter Plot-Visualize the Relationship Between 2 Continuous Variables.vtt

12.17 KB

  6. Barplot.mp4

53.81 MB

  6. Barplot.vtt

22.32 KB

  7. Pie Chart.mp4

12.80 MB

  7. Pie Chart.vtt

5.59 KB

  8. Line Chart.mp4

37.09 MB

  8. Line Chart.vtt

12.05 KB

  9. Conclusions to Section 6.mp4

5.83 MB

  9. Conclusions to Section 6.vtt

2.24 KB

 7. Statistical Data Analysis-Basic

  1. What is Statistical Data Analysis.mp4

25.29 MB

  1. What is Statistical Data Analysis.vtt

9.55 KB

  10. Standard Normal Distribution and Z-scores.mp4

9.81 MB

  10. Standard Normal Distribution and Z-scores.vtt

4.19 KB

  11. Confidence Interval-Theory.mp4

13.72 MB

  11. Confidence Interval-Theory.vtt

5.86 KB

  12. Confidence Interval-Calculation.mp4

13.65 MB

  12. Confidence Interval-Calculation.vtt

5.76 KB

  13. Conclusions to Section 7.mp4

3.82 MB

  13. Conclusions to Section 7.vtt

1.56 KB

  2. Some Pointers on Collecting Data for Statistical Studies.mp4

20.90 MB

  2. Some Pointers on Collecting Data for Statistical Studies.vtt

9.06 KB

  3. Some Pointers on Exploring Quantitative Data.html

0.50 KB

  4. Explore the Quantitative Data Descriptive Statistics.mp4

17.39 MB

  4. Explore the Quantitative Data Descriptive Statistics.vtt

7.64 KB

  5. Grouping Summarizing Data by Categories.mp4

33.05 MB

  5. Grouping Summarizing Data by Categories.vtt

10.28 KB

  6. Visualize Descriptive Statistics-Boxplots.mp4

11.50 MB

  6. Visualize Descriptive Statistics-Boxplots.vtt

5.23 KB

  7. Common Terms Relating to Descriptive Statistics.mp4

11.60 MB

  7. Common Terms Relating to Descriptive Statistics.vtt

5.55 KB

  8. Data Distribution- Normal Distribution.mp4

9.60 MB

  8. Data Distribution- Normal Distribution.vtt

3.89 KB

  9. Check for Normal Distribution.mp4

16.47 MB

  9. Check for Normal Distribution.vtt

5.63 KB

 8. Statistical Inference Relationship Between Variables

  1. What is Hypothesis Testing.mp4

13.41 MB

  1. What is Hypothesis Testing.vtt

5.83 KB

  10. Polynomial Regression.mp4

9.23 MB

  10. Polynomial Regression.vtt

3.74 KB

  11. GLM Generalized Linear Model.mp4

11.84 MB

  11. GLM Generalized Linear Model.vtt

5.16 KB

  12. Logistic Regression.mp4

28.78 MB

  12. Logistic Regression.vtt

11.12 KB

  13. Conclusions to Section 8.mp4

4.94 MB

  13. Conclusions to Section 8.vtt

2.03 KB

  14. Section 8 Quiz.html

0.16 KB

  2. Test the Difference Between Two Groups.mp4

17.78 MB

  2. Test the Difference Between Two Groups.vtt

7.30 KB

  3. Test the Difference Between More Than Two Groups.mp4

28.28 MB

  3. Test the Difference Between More Than Two Groups.vtt

10.95 KB

  4. Explore the Relationship Between Two Quantitative Variables.mp4

9.44 MB

  4. Explore the Relationship Between Two Quantitative Variables.vtt

4.41 KB

  5. Correlation Analysis.mp4

20.73 MB

  5. Correlation Analysis.vtt

8.63 KB

  6. Linear Regression-Theory.mp4

24.87 MB

  6. Linear Regression-Theory.vtt

9.84 KB

  7. Linear Regression-Implementation in Python.mp4

30.15 MB

  7. Linear Regression-Implementation in Python.vtt

11.51 KB

  8. Conditions of Linear Regression.mp4

2.98 MB

  8. Conditions of Linear Regression.vtt

1.82 KB

  9. Conditions of Linear Regression-Check in Python.mp4

33.36 MB

  9. Conditions of Linear Regression-Check in Python.vtt

12.56 KB

 9. Machine Learning for Data Science

  1. How is Machine Learning Different from Statistical Data Analysis.mp4

13.71 MB

  1. How is Machine Learning Different from Statistical Data Analysis.vtt

6.18 KB

  2. What is Machine Learning (ML) About Some Theoretical Pointers.mp4

15.75 MB

  2. What is Machine Learning (ML) About Some Theoretical Pointers.vtt

6.57 KB
 

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