[DesireCourse.Net] Udemy - Complete Data Science Training with Python for Data Analysis

Creation Time	Sept. 7, 2025, 8:43 p.m.
Last Access Time	Oct. 21, 2025, 8:35 p.m.
File Size	2.3 GB
Keywords	Training Complete for with DesireCourse - Analysis Udemy Python Science Net Data
Total Requests	29
Total Files	249
Magnet Link	Please wait ...
	Download (Magnet Link) Play (Watch Online)

1. Introduction to the Data Science in Python Bootcamp/3.1 scriptsLecture.zip.zip 308.0 MB
1. Introduction to the Data Science in Python Bootcamp/2. Introduction to the Course Instructor.m4v 55.6 MB
6. Introduction to Data Visualizations/6. Barplot.mp4 53.8 MB
4. Introduction to Pandas/6. Read in HTML Data.mp4 51.3 MB
13. Miscellaneous Lectures Information/5. Data Imputation.m4v 44.8 MB
1. Introduction to the Data Science in Python Bootcamp/6. Introduction to the Python Data Science Environment.mp4 40.3 MB
6. Introduction to Data Visualizations/8. Line Chart.mp4 37.1 MB
3. Introduction to Numpy/3. Numpy Operations.mp4 36.7 MB
8. Statistical Inference Relationship Between Variables/9. Conditions of Linear Regression-Check in Python.mp4 33.4 MB
7. Statistical Data Analysis-Basic/5. Grouping Summarizing Data by Categories.mp4 33.1 MB
8. Statistical Inference Relationship Between Variables/7. Linear Regression-Implementation in Python.mp4 30.2 MB
6. Introduction to Data Visualizations/5. Scatter Plot-Visualize the Relationship Between 2 Continuous Variables.mp4 29.8 MB
6. Introduction to Data Visualizations/3. Histograms-Visualize the Distribution of Continuous Numerical Variables.mp4 29.4 MB
10. Unsupervised Learning in Python/8. Hierarchical Clustering-practical.mp4 29.4 MB
5. Data Pre-ProcessingWrangling/12. Merging and Joining Data Frames.mp4 28.8 MB
8. Statistical Inference Relationship Between Variables/12. Logistic Regression.mp4 28.8 MB
11. Supervised Learning/5. RF-Classification.mp4 28.5 MB
11. Supervised Learning/2. Data Preparation for Supervised Learning.mp4 28.3 MB
8. Statistical Inference Relationship Between Variables/3. Test the Difference Between More Than Two Groups.mp4 28.3 MB
13. Miscellaneous Lectures Information/4. Naive Bayes Classification.m4v 28.2 MB
5. Data Pre-ProcessingWrangling/5. Subset and Index Data.mp4 28.0 MB
5. Data Pre-ProcessingWrangling/6. Basic Data Grouping Based on Qualitative Attributes.mp4 26.6 MB
7. Statistical Data Analysis-Basic/1. What is Statistical Data Analysis.mp4 25.3 MB
4. Introduction to Pandas/1. Data Structures in Python.mp4 25.1 MB
1. Introduction to the Data Science in Python Bootcamp/4. Introduction to the Python Data Science Tool.mp4 25.0 MB
11. Supervised Learning/1. What is This Section About.mp4 24.9 MB
8. Statistical Inference Relationship Between Variables/6. Linear Regression-Theory.mp4 24.9 MB
5. Data Pre-ProcessingWrangling/10. Rank and Sort Data.mp4 24.3 MB
5. Data Pre-ProcessingWrangling/8. Reshaping.mp4 24.3 MB
5. Data Pre-ProcessingWrangling/9. Pivoting.mp4 24.0 MB
11. Supervised Learning/3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.mp4 24.0 MB
5. Data Pre-ProcessingWrangling/11. Concatenate.mp4 23.7 MB
11. Supervised Learning/6. RF-Regression.mp4 23.6 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/1. Theory Behind ANN and DNN.mp4 22.6 MB
3. Introduction to Numpy/2. Create Numpy Arrays.mp4 20.9 MB
7. Statistical Data Analysis-Basic/2. Some Pointers on Collecting Data for Statistical Studies.mp4 20.9 MB
8. Statistical Inference Relationship Between Variables/5. Correlation Analysis.mp4 20.7 MB
6. Introduction to Data Visualizations/1. What is Data Visualization.mp4 20.7 MB
11. Supervised Learning/4. Using Logistic Regression as a Classification Model.mp4 20.6 MB
10. Unsupervised Learning in Python/3. KMeans-implementation on the iris data.mp4 19.5 MB
5. Data Pre-ProcessingWrangling/2. Removing NAsNo Values From Our Data.mp4 19.3 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/6. MLP with PCA on a Large Dataset.mp4 19.2 MB
10. Unsupervised Learning in Python/6. How Do We Select the Number of Clusters.mp4 19.0 MB
4. Introduction to Pandas/5. Reading in JSON Data.mp4 18.7 MB
11. Supervised Learning/10. knn-Classification.mp4 18.2 MB
8. Statistical Inference Relationship Between Variables/2. Test the Difference Between Two Groups.mp4 17.8 MB
1. Introduction to the Data Science in Python Bootcamp/1. What is Data Science.mp4 17.4 MB
7. Statistical Data Analysis-Basic/4. Explore the Quantitative Data Descriptive Statistics.mp4 17.4 MB
6. Introduction to Data Visualizations/2. Some Theoretical Principles Behind Data Visualization.mp4 16.6 MB
7. Statistical Data Analysis-Basic/9. Check for Normal Distribution.mp4 16.5 MB
3. Introduction to Numpy/4. Matrix Arithmetic and Linear Systems.mp4 15.8 MB
9. Machine Learning for Data Science/2. What is Machine Learning (ML) About Some Theoretical Pointers.mp4 15.8 MB
5. Data Pre-ProcessingWrangling/4. Drop ColumnRow.mp4 15.7 MB
4. Introduction to Pandas/3. Read in CSV Data Using Pandas.mp4 15.3 MB
11. Supervised Learning/12. Gradient Boosting-classification.mp4 15.0 MB
3. Introduction to Numpy/9. Numpy for Statistical Operation.mp4 14.9 MB
5. Data Pre-ProcessingWrangling/3. Basic Data Handling Starting with Conditional Data Selection.mp4 14.9 MB
3. Introduction to Numpy/6. Numpy for Basic Matrix Arithmetic.mp4 13.9 MB
7. Statistical Data Analysis-Basic/11. Confidence Interval-Theory.mp4 13.7 MB
9. Machine Learning for Data Science/1. How is Machine Learning Different from Statistical Data Analysis.mp4 13.7 MB
7. Statistical Data Analysis-Basic/12. Confidence Interval-Calculation.mp4 13.6 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/4. Multi-label classification with MLP.mp4 13.5 MB
6. Introduction to Data Visualizations/4. Boxplots-Visualize the Distribution of Continuous Numerical Variables.mp4 13.4 MB
8. Statistical Inference Relationship Between Variables/1. What is Hypothesis Testing.mp4 13.4 MB
6. Introduction to Data Visualizations/7. Pie Chart.mp4 12.8 MB
13. Miscellaneous Lectures Information/3. Read Data from a Database.mp4 12.3 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/8. Start with H20.mp4 12.1 MB
10. Unsupervised Learning in Python/5. KMeans Clustering with Real Data.mp4 12.1 MB
1. Introduction to the Data Science in Python Bootcamp/7. Some Miscellaneous IPython Usage Facts.mp4 12.0 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/11. H2O Deep Learning For Predictions.mp4 12.0 MB
8. Statistical Inference Relationship Between Variables/11. GLM Generalized Linear Model.mp4 11.8 MB
3. Introduction to Numpy/5. Numpy for Basic Vector Arithmetric.mp4 11.8 MB
7. Statistical Data Analysis-Basic/7. Common Terms Relating to Descriptive Statistics.mp4 11.6 MB
7. Statistical Data Analysis-Basic/6. Visualize Descriptive Statistics-Boxplots.mp4 11.5 MB
3. Introduction to Numpy/8. Solve Equations with Numpy.mp4 11.4 MB
4. Introduction to Pandas/4. Read in Excel Data Using Pandas.mp4 11.4 MB
11. Supervised Learning/13. Gradient Boosting-regression.mp4 10.9 MB
5. Data Pre-ProcessingWrangling/7. Crosstabulation.mp4 10.9 MB
10. Unsupervised Learning in Python/7. Hierarchical Clustering-theory.mp4 10.2 MB
1. Introduction to the Data Science in Python Bootcamp/5. For Mac Users.mp4 10.2 MB
11. Supervised Learning/9. Support Vector Regression.mp4 10.2 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/2. Perceptrons for Binary Classification.mp4 10.0 MB
7. Statistical Data Analysis-Basic/10. Standard Normal Distribution and Z-scores.mp4 9.8 MB
7. Statistical Data Analysis-Basic/8. Data Distribution- Normal Distribution.mp4 9.6 MB
10. Unsupervised Learning in Python/4. Quantifying KMeans Clustering Performance.mp4 9.6 MB
11. Supervised Learning/14. Voting Classifier.mp4 9.5 MB
8. Statistical Inference Relationship Between Variables/4. Explore the Relationship Between Two Quantitative Variables.mp4 9.4 MB
2. Introduction to Python Pre-Requisites for Data Science/2. Different Types of Data Used in Statistical ML Analysis.mp4 9.4 MB
8. Statistical Inference Relationship Between Variables/10. Polynomial Regression.mp4 9.2 MB
10. Unsupervised Learning in Python/10. Principal Component Analysis (PCA)-Practical Implementation.mp4 9.1 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/5. Regression with MLP.mp4 9.0 MB
3. Introduction to Numpy/7. Broadcasting with Numpy.mp4 9.0 MB
3. Introduction to Numpy/1. Numpy Introduction.mp4 8.7 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/3. Getting Started with ANN-binary classification.mp4 8.5 MB
11. Supervised Learning/11. knn-Regression.mp4 8.4 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/9. Default H2O Deep Learning Algorithm.mp4 8.2 MB
5. Data Pre-ProcessingWrangling/1. Rationale behind this section.mp4 8.1 MB
2. Introduction to Python Pre-Requisites for Data Science/4. Python Data Science Packages To Be Used.mp4 7.9 MB
2. Introduction to Python Pre-Requisites for Data Science/3. Different Types of Data Used Programatically.mp4 7.7 MB
1. Introduction to the Data Science in Python Bootcamp/8. Online iPython Interpreter.mp4 7.7 MB
11. Supervised Learning/7. SVM- Linear Classification.mp4 7.4 MB
11. Supervised Learning/15. Conclusions to Section 11.mp4 7.2 MB
13. Miscellaneous Lectures Information/2. Read in Data from Online CSV.mp4 6.7 MB
1. Introduction to the Data Science in Python Bootcamp/9. Conclusion to Section 1.mp4 6.5 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/10. Specify the Activation Function.mp4 6.2 MB
10. Unsupervised Learning in Python/1. Unsupervised Classification- Some Basic Ideas.mp4 6.2 MB
3. Introduction to Numpy/10. Conclusion to Section 3.mp4 6.2 MB
10. Unsupervised Learning in Python/9. Principal Component Analysis (PCA)-Theory.mp4 5.9 MB
6. Introduction to Data Visualizations/9. Conclusions to Section 6.mp4 5.8 MB
10. Unsupervised Learning in Python/11. Conclusions to Section 10.mp4 5.5 MB
4. Introduction to Pandas/7. Conclusion to Section 4.mp4 5.4 MB
5. Data Pre-ProcessingWrangling/13. Conclusion to Section 5.mp4 5.4 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/12. Conclusions to Section 12.mp4 5.2 MB
10. Unsupervised Learning in Python/2. KMeans-theory.mp4 5.1 MB
11. Supervised Learning/8. SVM- Non Linear Classification.mp4 5.1 MB
8. Statistical Inference Relationship Between Variables/13. Conclusions to Section 8.mp4 4.9 MB
2. Introduction to Python Pre-Requisites for Data Science/5. Conclusions to Section 2.mp4 4.9 MB
7. Statistical Data Analysis-Basic/13. Conclusions to Section 7.mp4 3.8 MB
8. Statistical Inference Relationship Between Variables/8. Conditions of Linear Regression.mp4 3.0 MB
6. Introduction to Data Visualizations/6. Barplot.vtt 22.3 KB
1. Introduction to the Data Science in Python Bootcamp/6. Introduction to the Python Data Science Environment.vtt 17.2 KB
3. Introduction to Numpy/3. Numpy Operations.vtt 15.0 KB
1. Introduction to the Data Science in Python Bootcamp/2. Introduction to the Course Instructor.vtt 13.5 KB
8. Statistical Inference Relationship Between Variables/9. Conditions of Linear Regression-Check in Python.vtt 12.6 KB
11. Supervised Learning/5. RF-Classification.vtt 12.2 KB
6. Introduction to Data Visualizations/5. Scatter Plot-Visualize the Relationship Between 2 Continuous Variables.vtt 12.2 KB
6. Introduction to Data Visualizations/8. Line Chart.vtt 12.1 KB
6. Introduction to Data Visualizations/3. Histograms-Visualize the Distribution of Continuous Numerical Variables.vtt 11.9 KB
8. Statistical Inference Relationship Between Variables/7. Linear Regression-Implementation in Python.vtt 11.5 KB
11. Supervised Learning/1. What is This Section About.vtt 11.5 KB
4. Introduction to Pandas/6. Read in HTML Data.vtt 11.1 KB
8. Statistical Inference Relationship Between Variables/12. Logistic Regression.vtt 11.1 KB
8. Statistical Inference Relationship Between Variables/3. Test the Difference Between More Than Two Groups.vtt 10.9 KB
5. Data Pre-ProcessingWrangling/12. Merging and Joining Data Frames.vtt 10.7 KB
11. Supervised Learning/3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.vtt 10.5 KB
7. Statistical Data Analysis-Basic/5. Grouping Summarizing Data by Categories.vtt 10.3 KB
1. Introduction to the Data Science in Python Bootcamp/4. Introduction to the Python Data Science Tool.vtt 10.1 KB
11. Supervised Learning/2. Data Preparation for Supervised Learning.vtt 10.1 KB
4. Introduction to Pandas/1. Data Structures in Python.vtt 10.0 KB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/1. Theory Behind ANN and DNN.vtt 9.9 KB
8. Statistical Inference Relationship Between Variables/6. Linear Regression-Theory.vtt 9.8 KB
6. Introduction to Data Visualizations/1. What is Data Visualization.vtt 9.8 KB
11. Supervised Learning/6. RF-Regression.vtt 9.7 KB
5. Data Pre-ProcessingWrangling/8. Reshaping.vtt 9.6 KB
7. Statistical Data Analysis-Basic/1. What is Statistical Data Analysis.vtt 9.6 KB
10. Unsupervised Learning in Python/8. Hierarchical Clustering-practical.vtt 9.5 KB
7. Statistical Data Analysis-Basic/2. Some Pointers on Collecting Data for Statistical Studies.vtt 9.1 KB
13. Miscellaneous Lectures Information/5. Data Imputation.vtt 9.0 KB
11. Supervised Learning/4. Using Logistic Regression as a Classification Model.vtt 8.7 KB
8. Statistical Inference Relationship Between Variables/5. Correlation Analysis.vtt 8.6 KB
5. Data Pre-ProcessingWrangling/9. Pivoting.vtt 8.4 KB
5. Data Pre-ProcessingWrangling/6. Basic Data Grouping Based on Qualitative Attributes.vtt 8.3 KB
11. Supervised Learning/10. knn-Classification.vtt 8.0 KB
5. Data Pre-ProcessingWrangling/11. Concatenate.vtt 8.0 KB
13. Miscellaneous Lectures Information/3. Read Data from a Database.vtt 7.8 KB
5. Data Pre-ProcessingWrangling/5. Subset and Index Data.vtt 7.8 KB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/6. MLP with PCA on a Large Dataset.vtt 7.6 KB
7. Statistical Data Analysis-Basic/4. Explore the Quantitative Data Descriptive Statistics.vtt 7.6 KB
10. Unsupervised Learning in Python/3. KMeans-implementation on the iris data.vtt 7.6 KB
8. Statistical Inference Relationship Between Variables/2. Test the Difference Between Two Groups.vtt 7.3 KB
5. Data Pre-ProcessingWrangling/10. Rank and Sort Data.vtt 7.3 KB
6. Introduction to Data Visualizations/2. Some Theoretical Principles Behind Data Visualization.vtt 7.1 KB
13. Miscellaneous Lectures Information/4. Naive Bayes Classification.vtt 6.8 KB
3. Introduction to Numpy/9. Numpy for Statistical Operation.vtt 6.7 KB
9. Machine Learning for Data Science/2. What is Machine Learning (ML) About Some Theoretical Pointers.vtt 6.6 KB
3. Introduction to Numpy/4. Matrix Arithmetic and Linear Systems.vtt 6.5 KB
5. Data Pre-ProcessingWrangling/2. Removing NAsNo Values From Our Data.vtt 6.4 KB
9. Machine Learning for Data Science/1. How is Machine Learning Different from Statistical Data Analysis.vtt 6.2 KB
11. Supervised Learning/12. Gradient Boosting-classification.vtt 6.0 KB
3. Introduction to Numpy/2. Create Numpy Arrays.vtt 5.9 KB
7. Statistical Data Analysis-Basic/11. Confidence Interval-Theory.vtt 5.9 KB
8. Statistical Inference Relationship Between Variables/1. What is Hypothesis Testing.vtt 5.8 KB
4. Introduction to Pandas/3. Read in CSV Data Using Pandas.vtt 5.8 KB
7. Statistical Data Analysis-Basic/12. Confidence Interval-Calculation.vtt 5.8 KB
7. Statistical Data Analysis-Basic/9. Check for Normal Distribution.vtt 5.6 KB
6. Introduction to Data Visualizations/7. Pie Chart.vtt 5.6 KB
7. Statistical Data Analysis-Basic/7. Common Terms Relating to Descriptive Statistics.vtt 5.5 KB
6. Introduction to Data Visualizations/4. Boxplots-Visualize the Distribution of Continuous Numerical Variables.vtt 5.5 KB
7. Statistical Data Analysis-Basic/6. Visualize Descriptive Statistics-Boxplots.vtt 5.2 KB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/11. H2O Deep Learning For Predictions.vtt 5.2 KB
8. Statistical Inference Relationship Between Variables/11. GLM Generalized Linear Model.vtt 5.2 KB
3. Introduction to Numpy/6. Numpy for Basic Matrix Arithmetic.vtt 5.2 KB
10. Unsupervised Learning in Python/7. Hierarchical Clustering-theory.vtt 5.0 KB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/4. Multi-label classification with MLP.vtt 4.8 KB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/2. Perceptrons for Binary Classification.vtt 4.7 KB
5. Data Pre-ProcessingWrangling/1. Rationale behind this section.vtt 4.6 KB
1. Introduction to the Data Science in Python Bootcamp/7. Some Miscellaneous IPython Usage Facts.vtt 4.5 KB
10. Unsupervised Learning in Python/5. KMeans Clustering with Real Data.vtt 4.5 KB
8. Statistical Inference Relationship Between Variables/4. Explore the Relationship Between Two Quantitative Variables.vtt 4.4 KB
10. Unsupervised Learning in Python/4. Quantifying KMeans Clustering Performance.vtt 4.4 KB
5. Data Pre-ProcessingWrangling/4. Drop ColumnRow.vtt 4.4 KB
11. Supervised Learning/9. Support Vector Regression.vtt 4.3 KB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/8. Start with H20.vtt 4.3 KB
10. Unsupervised Learning in Python/6. How Do We Select the Number of Clusters.vtt 4.2 KB
7. Statistical Data Analysis-Basic/10. Standard Normal Distribution and Z-scores.vtt 4.2 KB
3. Introduction to Numpy/8. Solve Equations with Numpy.vtt 4.2 KB
10. Unsupervised Learning in Python/10. Principal Component Analysis (PCA)-Practical Implementation.vtt 4.2 KB
5. Data Pre-ProcessingWrangling/3. Basic Data Handling Starting with Conditional Data Selection.vtt 4.1 KB
1. Introduction to the Data Science in Python Bootcamp/1. What is Data Science.vtt 4.0 KB
11. Supervised Learning/11. knn-Regression.vtt 4.0 KB
7. Statistical Data Analysis-Basic/8. Data Distribution- Normal Distribution.vtt 3.9 KB
1. Introduction to the Data Science in Python Bootcamp/5. For Mac Users.vtt 3.9 KB
13. Miscellaneous Lectures Information/2. Read in Data from Online CSV.vtt 3.9 KB
5. Data Pre-ProcessingWrangling/7. Crosstabulation.vtt 3.9 KB
3. Introduction to Numpy/1. Numpy Introduction.vtt 3.8 KB
2. Introduction to Python Pre-Requisites for Data Science/4. Python Data Science Packages To Be Used.vtt 3.8 KB
3. Introduction to Numpy/5. Numpy for Basic Vector Arithmetric.vtt 3.8 KB
3. Introduction to Numpy/7. Broadcasting with Numpy.vtt 3.8 KB
4. Introduction to Pandas/4. Read in Excel Data Using Pandas.vtt 3.8 KB
11. Supervised Learning/14. Voting Classifier.vtt 3.8 KB
8. Statistical Inference Relationship Between Variables/10. Polynomial Regression.vtt 3.7 KB
11. Supervised Learning/13. Gradient Boosting-regression.vtt 3.7 KB
2. Introduction to Python Pre-Requisites for Data Science/2. Different Types of Data Used in Statistical ML Analysis.vtt 3.7 KB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/5. Regression with MLP.vtt 3.5 KB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/3. Getting Started with ANN-binary classification.vtt 3.5 KB
1. Introduction to the Data Science in Python Bootcamp/8. Online iPython Interpreter.vtt 3.4 KB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/9. Default H2O Deep Learning Algorithm.vtt 3.4 KB
11. Supervised Learning/7. SVM- Linear Classification.vtt 3.2 KB
4. Introduction to Pandas/5. Reading in JSON Data.vtt 3.1 KB
1. Introduction to the Data Science in Python Bootcamp/9. Conclusion to Section 1.vtt 3.1 KB
2. Introduction to Python Pre-Requisites for Data Science/3. Different Types of Data Used Programatically.vtt 3.0 KB
10. Unsupervised Learning in Python/9. Principal Component Analysis (PCA)-Theory.vtt 3.0 KB
11. Supervised Learning/15. Conclusions to Section 11.vtt 2.9 KB
3. Introduction to Numpy/10. Conclusion to Section 3.vtt 2.5 KB
10. Unsupervised Learning in Python/2. KMeans-theory.vtt 2.5 KB
10. Unsupervised Learning in Python/11. Conclusions to Section 10.vtt 2.5 KB
2. Introduction to Python Pre-Requisites for Data Science/5. Conclusions to Section 2.vtt 2.4 KB
11. Supervised Learning/8. SVM- Non Linear Classification.vtt 2.3 KB
4. Introduction to Pandas/7. Conclusion to Section 4.vtt 2.2 KB
6. Introduction to Data Visualizations/9. Conclusions to Section 6.vtt 2.2 KB
5. Data Pre-ProcessingWrangling/13. Conclusion to Section 5.vtt 2.2 KB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/10. Specify the Activation Function.vtt 2.2 KB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/12. Conclusions to Section 12.vtt 2.1 KB
8. Statistical Inference Relationship Between Variables/13. Conclusions to Section 8.vtt 2.0 KB
8. Statistical Inference Relationship Between Variables/8. Conditions of Linear Regression.vtt 1.8 KB
10. Unsupervised Learning in Python/1. Unsupervised Classification- Some Basic Ideas.vtt 1.8 KB
7. Statistical Data Analysis-Basic/13. Conclusions to Section 7.vtt 1.6 KB
7. Statistical Data Analysis-Basic/3. Some Pointers on Exploring Quantitative Data.html 517 bytes
2. Introduction to Python Pre-Requisites for Data Science/1. Rationale Behind This Section.html 429 bytes
4. Introduction to Pandas/2. Read in Data.html 246 bytes
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/7. Start With Deep Neural Network (DNN).html 229 bytes
11. Supervised Learning/16. Section 11 Quiz.html 163 bytes
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/13. Section 12 Quiz.html 163 bytes
3. Introduction to Numpy/11. Section 3 Quiz.html 163 bytes
8. Statistical Inference Relationship Between Variables/14. Section 8 Quiz.html 163 bytes
13. Miscellaneous Lectures Information/1. Data For This Section.html 137 bytes
1. Introduction to the Data Science in Python Bootcamp/3. Data For the Course.html 98 bytes
[DesireCourse.Net].url 51 bytes
[CourseClub.Me].url 48 bytes

[DesireCourse.Net] Udemy - Complete Data Science Training with Python for Data Analysis

File List

Download Torrent

Related Resources

Copyright Infringement