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[DesireCourse.Net] Udemy - Complete Data Science Training with Python for Data Analysis
magnet:?xt=urn:btih:6ba6895d7d716420f653594b54e1e102e8ca79ac&dn=[DesireCourse.Net] Udemy - Complete Data Science Training with Python for Data Analysis
磁力链接详情
Hash值:
6ba6895d7d716420f653594b54e1e102e8ca79ac
点击数:
206
文件大小:
1.95 GB
文件数量:
118
创建日期:
2020-10-29 07:47
最后访问:
2024-9-20 05:37
访问标签:
DesireCourse
Net
Udemy
-
Complete
Data
Science
Training
with
Python
for
Data
Analysis
文件列表详情
1. Introduction to the Data Science in Python Bootcamp/1. What is Data Science.mp4 17.39 MB
1. Introduction to the Data Science in Python Bootcamp/2. Introduction to the Course Instructor.m4v 55.61 MB
1. Introduction to the Data Science in Python Bootcamp/4. Introduction to the Python Data Science Tool.mp4 25.02 MB
1. Introduction to the Data Science in Python Bootcamp/5. For Mac Users.mp4 10.22 MB
1. Introduction to the Data Science in Python Bootcamp/6. Introduction to the Python Data Science Environment.mp4 40.32 MB
1. Introduction to the Data Science in Python Bootcamp/7. Some Miscellaneous IPython Usage Facts.mp4 12.01 MB
1. Introduction to the Data Science in Python Bootcamp/8. Online iPython Interpreter.mp4 7.73 MB
1. Introduction to the Data Science in Python Bootcamp/9. Conclusion to Section 1.mp4 6.48 MB
10. Unsupervised Learning in Python/1. Unsupervised Classification- Some Basic Ideas.mp4 6.17 MB
10. Unsupervised Learning in Python/10. Principal Component Analysis (PCA)-Practical Implementation.mp4 9.06 MB
10. Unsupervised Learning in Python/11. Conclusions to Section 10.mp4 5.49 MB
10. Unsupervised Learning in Python/2. KMeans-theory.mp4 5.15 MB
10. Unsupervised Learning in Python/3. KMeans-implementation on the iris data.mp4 19.54 MB
10. Unsupervised Learning in Python/4. Quantifying KMeans Clustering Performance.mp4 9.57 MB
10. Unsupervised Learning in Python/5. KMeans Clustering with Real Data.mp4 12.08 MB
10. Unsupervised Learning in Python/6. How Do We Select the Number of Clusters.mp4 19.04 MB
10. Unsupervised Learning in Python/7. Hierarchical Clustering-theory.mp4 10.23 MB
10. Unsupervised Learning in Python/8. Hierarchical Clustering-practical.mp4 29.39 MB
10. Unsupervised Learning in Python/9. Principal Component Analysis (PCA)-Theory.mp4 5.91 MB
11. Supervised Learning/1. What is This Section About.mp4 24.88 MB
11. Supervised Learning/10. knn-Classification.mp4 18.2 MB
11. Supervised Learning/11. knn-Regression.mp4 8.38 MB
11. Supervised Learning/12. Gradient Boosting-classification.mp4 15.04 MB
11. Supervised Learning/13. Gradient Boosting-regression.mp4 10.9 MB
11. Supervised Learning/14. Voting Classifier.mp4 9.53 MB
11. Supervised Learning/15. Conclusions to Section 11.mp4 7.23 MB
11. Supervised Learning/2. Data Preparation for Supervised Learning.mp4 28.28 MB
11. Supervised Learning/3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.mp4 24 MB
11. Supervised Learning/4. Using Logistic Regression as a Classification Model.mp4 20.64 MB
11. Supervised Learning/5. RF-Classification.mp4 28.48 MB
11. Supervised Learning/6. RF-Regression.mp4 23.63 MB
11. Supervised Learning/7. SVM- Linear Classification.mp4 7.39 MB
11. Supervised Learning/8. SVM- Non Linear Classification.mp4 5.12 MB
11. Supervised Learning/9. Support Vector Regression.mp4 10.19 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/1. Theory Behind ANN and DNN.mp4 22.56 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/10. Specify the Activation Function.mp4 6.21 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/11. H2O Deep Learning For Predictions.mp4 12 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/12. Conclusions to Section 12.mp4 5.16 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/2. Perceptrons for Binary Classification.mp4 10.05 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/3. Getting Started with ANN-binary classification.mp4 8.46 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/4. Multi-label classification with MLP.mp4 13.49 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/5. Regression with MLP.mp4 9.02 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/6. MLP with PCA on a Large Dataset.mp4 19.25 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/8. Start with H20.mp4 12.12 MB
12. Artificial Neural Networks (ANN) and Deep Learning (DL)/9. Default H2O Deep Learning Algorithm.mp4 8.23 MB
13. Miscellaneous Lectures Information/2. Read in Data from Online CSV.mp4 6.66 MB
13. Miscellaneous Lectures Information/3. Read Data from a Database.mp4 12.26 MB
13. Miscellaneous Lectures Information/4. Naive Bayes Classification.m4v 28.16 MB
13. Miscellaneous Lectures Information/5. Data Imputation.m4v 44.84 MB
2. Introduction to Python Pre-Requisites for Data Science/2. Different Types of Data Used in Statistical ML Analysis.mp4 9.36 MB
2. Introduction to Python Pre-Requisites for Data Science/3. Different Types of Data Used Programatically.mp4 7.74 MB
2. Introduction to Python Pre-Requisites for Data Science/4. Python Data Science Packages To Be Used.mp4 7.93 MB
2. Introduction to Python Pre-Requisites for Data Science/5. Conclusions to Section 2.mp4 4.88 MB
3. Introduction to Numpy/1. Numpy Introduction.mp4 8.7 MB
3. Introduction to Numpy/10. Conclusion to Section 3.mp4 6.17 MB
3. Introduction to Numpy/2. Create Numpy Arrays.mp4 20.91 MB
3. Introduction to Numpy/3. Numpy Operations.mp4 36.71 MB
3. Introduction to Numpy/4. Matrix Arithmetic and Linear Systems.mp4 15.83 MB
3. Introduction to Numpy/5. Numpy for Basic Vector Arithmetric.mp4 11.75 MB
3. Introduction to Numpy/6. Numpy for Basic Matrix Arithmetic.mp4 13.89 MB
3. Introduction to Numpy/7. Broadcasting with Numpy.mp4 8.95 MB
3. Introduction to Numpy/8. Solve Equations with Numpy.mp4 11.44 MB
3. Introduction to Numpy/9. Numpy for Statistical Operation.mp4 14.95 MB
4. Introduction to Pandas/1. Data Structures in Python.mp4 25.07 MB
4. Introduction to Pandas/3. Read in CSV Data Using Pandas.mp4 15.32 MB
4. Introduction to Pandas/4. Read in Excel Data Using Pandas.mp4 11.38 MB
4. Introduction to Pandas/5. Reading in JSON Data.mp4 18.72 MB
4. Introduction to Pandas/6. Read in HTML Data.mp4 51.31 MB
4. Introduction to Pandas/7. Conclusion to Section 4.mp4 5.4 MB
5. Data Pre-ProcessingWrangling/1. Rationale behind this section.mp4 8.11 MB
5. Data Pre-ProcessingWrangling/10. Rank and Sort Data.mp4 24.32 MB
5. Data Pre-ProcessingWrangling/11. Concatenate.mp4 23.74 MB
5. Data Pre-ProcessingWrangling/12. Merging and Joining Data Frames.mp4 28.8 MB
5. Data Pre-ProcessingWrangling/13. Conclusion to Section 5.mp4 5.39 MB
5. Data Pre-ProcessingWrangling/2. Removing NAsNo Values From Our Data.mp4 19.29 MB
5. Data Pre-ProcessingWrangling/3. Basic Data Handling Starting with Conditional Data Selection.mp4 14.85 MB
5. Data Pre-ProcessingWrangling/4. Drop ColumnRow.mp4 15.7 MB
5. Data Pre-ProcessingWrangling/5. Subset and Index Data.mp4 28 MB
5. Data Pre-ProcessingWrangling/6. Basic Data Grouping Based on Qualitative Attributes.mp4 26.62 MB
5. Data Pre-ProcessingWrangling/7. Crosstabulation.mp4 10.88 MB
5. Data Pre-ProcessingWrangling/8. Reshaping.mp4 24.27 MB
5. Data Pre-ProcessingWrangling/9. Pivoting.mp4 24.04 MB
6. Introduction to Data Visualizations/1. What is Data Visualization.mp4 20.72 MB
6. Introduction to Data Visualizations/2. Some Theoretical Principles Behind Data Visualization.mp4 16.56 MB
6. Introduction to Data Visualizations/3. Histograms-Visualize the Distribution of Continuous Numerical Variables.mp4 29.41 MB
6. Introduction to Data Visualizations/4. Boxplots-Visualize the Distribution of Continuous Numerical Variables.mp4 13.44 MB
6. Introduction to Data Visualizations/5. Scatter Plot-Visualize the Relationship Between 2 Continuous Variables.mp4 29.82 MB
6. Introduction to Data Visualizations/6. Barplot.mp4 53.81 MB
6. Introduction to Data Visualizations/7. Pie Chart.mp4 12.8 MB
6. Introduction to Data Visualizations/8. Line Chart.mp4 37.09 MB
6. Introduction to Data Visualizations/9. Conclusions to Section 6.mp4 5.83 MB
7. Statistical Data Analysis-Basic/1. What is Statistical Data Analysis.mp4 25.29 MB
7. Statistical Data Analysis-Basic/10. Standard Normal Distribution and Z-scores.mp4 9.81 MB
7. Statistical Data Analysis-Basic/11. Confidence Interval-Theory.mp4 13.72 MB
7. Statistical Data Analysis-Basic/12. Confidence Interval-Calculation.mp4 13.65 MB
7. Statistical Data Analysis-Basic/13. Conclusions to Section 7.mp4 3.82 MB
7. Statistical Data Analysis-Basic/2. Some Pointers on Collecting Data for Statistical Studies.mp4 20.9 MB
7. Statistical Data Analysis-Basic/4. Explore the Quantitative Data Descriptive Statistics.mp4 17.39 MB
7. Statistical Data Analysis-Basic/5. Grouping Summarizing Data by Categories.mp4 33.05 MB
7. Statistical Data Analysis-Basic/6. Visualize Descriptive Statistics-Boxplots.mp4 11.5 MB
7. Statistical Data Analysis-Basic/7. Common Terms Relating to Descriptive Statistics.mp4 11.6 MB
7. Statistical Data Analysis-Basic/8. Data Distribution- Normal Distribution.mp4 9.6 MB
7. Statistical Data Analysis-Basic/9. Check for Normal Distribution.mp4 16.47 MB
8. Statistical Inference Relationship Between Variables/1. What is Hypothesis Testing.mp4 13.41 MB
8. Statistical Inference Relationship Between Variables/10. Polynomial Regression.mp4 9.23 MB
8. Statistical Inference Relationship Between Variables/11. GLM Generalized Linear Model.mp4 11.84 MB
8. Statistical Inference Relationship Between Variables/12. Logistic Regression.mp4 28.78 MB
8. Statistical Inference Relationship Between Variables/13. Conclusions to Section 8.mp4 4.94 MB
8. Statistical Inference Relationship Between Variables/2. Test the Difference Between Two Groups.mp4 17.78 MB
8. Statistical Inference Relationship Between Variables/3. Test the Difference Between More Than Two Groups.mp4 28.28 MB
8. Statistical Inference Relationship Between Variables/4. Explore the Relationship Between Two Quantitative Variables.mp4 9.44 MB
8. Statistical Inference Relationship Between Variables/5. Correlation Analysis.mp4 20.73 MB
8. Statistical Inference Relationship Between Variables/6. Linear Regression-Theory.mp4 24.87 MB
8. Statistical Inference Relationship Between Variables/7. Linear Regression-Implementation in Python.mp4 30.15 MB
8. Statistical Inference Relationship Between Variables/8. Conditions of Linear Regression.mp4 2.98 MB
8. Statistical Inference Relationship Between Variables/9. Conditions of Linear Regression-Check in Python.mp4 33.36 MB
9. Machine Learning for Data Science/1. How is Machine Learning Different from Statistical Data Analysis.mp4 13.71 MB
9. Machine Learning for Data Science/2. What is Machine Learning (ML) About Some Theoretical Pointers.mp4 15.75 MB
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