Udemy Statistics For Data Science and Machine Learning with Python BOOKWARE-SOFTiMAGE
Udemy Statistics For Data Science and Machine Learning with Python BOOKWARE-SOFTiMAGE | Apps-Tutorials | BMP,MP4 | 2.37 GiB
1 108 kb/s 1280×720 | AAC 128 kb/s 2 CH
NFO:
Udemy.Statistics.For.Data.Science.and.Machine.Learning.with.Python
.BOOKWARE-SOFTiMAGE
RELEASE DATE....: 11/2022
RELEASE TYPE....: tut
RELEASE FORMAT..: vid
https://www.udemy.com/course/
python-statistical-methods-machine-learning-data-science/
SOFTiMAGE is currently looking for: nothing but global peace!
File List:
101M 1. Introduction 38M 1. Introduction/1. Overview of Course Curriculum.mp4 25M 1. Introduction/2. Installing Jupyter Notebook Environment.mp4 33M 1. Introduction/3. How to Download Exercises & Course Notebooks.mp4 6.4M 1. Introduction/3.1 Course Notebooks 8.0K 1. Introduction/3.1 Course Notebooks/.ipynb_checkpoints 4.0K 1. Introduction/3.1 Course Notebooks/.ipynb_checkpoints/11.5 11.6 Evaluation Metrics for Classification Models-checkpoint.ipynb 16K 1. Introduction/3.1 Course Notebooks/0.0 Table of Contents.ipynb 12K 1. Introduction/3.1 Course Notebooks/10.1 Confidence Interval for Continuous Data .ipynb 4.0K 1. Introduction/3.1 Course Notebooks/10.2 C.I for Classification Models Accuracy.ipynb 4.0K 1. Introduction/3.1 Course Notebooks/10.3 Bootstrapping For Unknown Distributions.ipynb 24K 1. Introduction/3.1 Course Notebooks/10.4 Nonparametric Confidence Interval with Bootstrapping .ipynb 24K 1. Introduction/3.1 Course Notebooks/10.5 Exercise Confidence Intervals.ipynb 100K 1. Introduction/3.1 Course Notebooks/11.2 Overfitting and Underfitting.ipynb 12K 1. Introduction/3.1 Course Notebooks/11.3 Information Criteria for Model Evaluation.ipynb 24K 1. Introduction/3.1 Course Notebooks/11.4 Evaluation Metrics for Regression Models.ipynb 4.0K 1. Introduction/3.1 Course Notebooks/11.5 11.6 Evaluation Metrics for Classification Models.ipynb 12K 1. Introduction/3.1 Course Notebooks/11.7 Application in Data Science.ipynb 20K 1. Introduction/3.1 Course Notebooks/11.8 Exercise Evaluating Machine Learning Models.ipynb 24K 1. Introduction/3.1 Course Notebooks/12.1 Hold Out Validation - Train Test Split.ipynb 24K 1. Introduction/3.1 Course Notebooks/12.2 K-Fold Cross-Validation.ipynb 8.0K 1. Introduction/3.1 Course Notebooks/12.3 Leave-One-Out Cross-Validation (LOOCV).ipynb 44K 1. Introduction/3.1 Course Notebooks/12.4 Application in Data Science.ipynb 16K 1. Introduction/3.1 Course Notebooks/12.5 Exercise Validation Techniques in Machine Learning.ipynb 8.0K 1. Introduction/3.1 Course Notebooks/2.1 Built-in Data Structures - Tuple and List.ipynb 8.0K 1. Introduction/3.1 Course Notebooks/2.2 Built-in Data Structures - Dictionary and Set.ipynb 8.0K 1. Introduction/3.1 Course Notebooks/2.3 Numpy Array.ipynb 16K 1. Introduction/3.1 Course Notebooks/2.4 Pandas Series and Dataframes.ipynb 16K 1. Introduction/3.1 Course Notebooks/2.5 Data Types (Numeric or Categorical).ipynb 12K 1. Introduction/3.1 Course Notebooks/2.6 Exercise Create Data Structures in Python.ipynb 8.0K 1. Introduction/3.1 Course Notebooks/3.1 Mean (Average).ipynb 8.0K 1. Introduction/3.1 Course Notebooks/3.2 Weighted Average.ipynb 8.0K 1. Introduction/3.1 Course Notebooks/3.3 Median.ipynb 12K 1. Introduction/3.1 Course Notebooks/3.4 Population vs. Sample.ipynb 8.0K 1. Introduction/3.1 Course Notebooks/3.5 Application in Data Science.ipynb 8.0K 1. Introduction/3.1 Course Notebooks/3.6 Exercise Calculate Central Tendency Measures.ipynb 8.0K 1. Introduction/3.1 Course Notebooks/4.1 Range.ipynb 16K 1. Introduction/3.1 Course Notebooks/4.2 Variance and Standard Deviation.ipynb 16K 1. Introduction/3.1 Course Notebooks/4.3 Percentile & Quartile.ipynb 48K 1. Introduction/3.1 Course Notebooks/4.4 Outlier-Part 1.ipynb 16K 1. Introduction/3.1 Course Notebooks/4.5 Outlier-Part 2.ipynb 24K 1. Introduction/3.1 Course Notebooks/4.6 Sampling Error.ipynb 56K 1. Introduction/3.1 Course Notebooks/4.7 Application in Data Science.ipynb 12K 1. Introduction/3.1 Course Notebooks/4.8 Exercise Calculate Variability Measures.ipynb 68K 1. Introduction/3.1 Course Notebooks/5.1 Box Plots.ipynb 104K 1. Introduction/3.1 Course Notebooks/5.2 Violin Plot.ipynb 84K 1. Introduction/3.1 Course Notebooks/5.3 Histogram and Density Plots.ipynb 72K 1. Introduction/3.1 Course Notebooks/5.4 Bar Plot for Categorical Data.ipynb 108K 1. Introduction/3.1 Course Notebooks/5.5 Pie Chart for Categorical Data.ipynb 88K 1. Introduction/3.1 Course Notebooks/5.6 Application in Data Science.ipynb 84K 1. Introduction/3.1 Course Notebooks/5.7 Exercise Exploring Data Distribution.ipynb 12K 1. Introduction/3.1 Course Notebooks/6.1 Correlation and Covariance Coefficients.ipynb 256K 1. Introduction/3.1 Course Notebooks/6.2 Correlation Using Scatterplot .ipynb 288K 1. Introduction/3.1 Course Notebooks/6.3 Mapping with Scatterplots.ipynb 300K 1. Introduction/3.1 Course Notebooks/6.4 Heat Maps.ipynb 260K 1. Introduction/3.1 Course Notebooks/6.5 Application in Data Science.ipynb 88K 1. Introduction/3.1 Course Notebooks/6.6 Exercise Create Mapped Scatterplots and Heat Maps.ipynb 4.0K 1. Introduction/3.1 Course Notebooks/7.1 Description of The Project.ipynb 168K 1. Introduction/3.1 Course Notebooks/7.2 Solution walkthrough of the Project.ipynb 12K 1. Introduction/3.1 Course Notebooks/8.1 Random Sampling and Bias.ipynb 20K 1. Introduction/3.1 Course Notebooks/8.2 Central Limit Theorem.ipynb 76K 1. Introduction/3.1 Course Notebooks/8.3 Normal distribution.ipynb 124K 1. Introduction/3.1 Course Notebooks/8.4 Normality Tests for Real-World Data.ipynb 156K 1. Introduction/3.1 Course Notebooks/8.5 Skewed Data Real life Distributions.ipynb 12K 1. Introduction/3.1 Course Notebooks/8.6 Probability A practical Introduction.ipynb 152K 1. Introduction/3.1 Course Notebooks/8.7 Common Probability Distributions.ipynb 80K 1. Introduction/3.1 Course Notebooks/8.8 Exercise Normal Distribution and Skewness.ipynb 128K 1. Introduction/3.1 Course Notebooks/9.1 Data Scaling Standardization.ipynb 80K 1. Introduction/3.1 Course Notebooks/9.2 Data Scaling Normalization.ipynb 144K 1. Introduction/3.1 Course Notebooks/9.3 Log and Square Root Transformations.ipynb 128K 1. Introduction/3.1 Course Notebooks/9.4 Power Transformation (PowerTransformer).ipynb 76K 1. Introduction/3.1 Course Notebooks/9.5 Application in Data Science.ipynb 76K 1. Introduction/3.1 Course Notebooks/9.6 Exercise Data Scaling and Transformation.ipynb 92K 1. Introduction/3.1 Course Notebooks/Final Project.ipynb 2.4M 1. Introduction/3.1 Course Notebooks/data 4.0K 1. Introduction/3.1 Course Notebooks/data/Advertising.csv 28K 1. Introduction/3.1 Course Notebooks/data/Credit.csv 36K 1. Introduction/3.1 Course Notebooks/data/Heart.csv 4.0K 1. Introduction/3.1 Course Notebooks/data/admission.csv 152K 1. Introduction/3.1 Course Notebooks/data/apple.csv 4.0K 1. Introduction/3.1 Course Notebooks/data/ch2example1.csv 4.0K 1. Introduction/3.1 Course Notebooks/data/crime.csv 44K 1. Introduction/3.1 Course Notebooks/data/default.csv 16K 1. Introduction/3.1 Course Notebooks/data/disease.csv 592K 1. Introduction/3.1 Course Notebooks/data/distributions.csv 4.0K 1. Introduction/3.1 Course Notebooks/data/ex1.csv 4.0K 1. Introduction/3.1 Course Notebooks/data/ex2.csv 4.0K 1. Introduction/3.1 Course Notebooks/data/ex3.csv 4.0K 1. Introduction/3.1 Course Notebooks/data/ex4.csv 4.0K 1. Introduction/3.1 Course Notebooks/data/ex5.csv 8.0K 1. Introduction/3.1 Course Notebooks/data/ex6.csv 36K 1. Introduction/3.1 Course Notebooks/data/ex7.csv 8.0K 1. Introduction/3.1 Course Notebooks/data/ex8.csv 160K 1. Introduction/3.1 Course Notebooks/data/google.csv 8.0K 1. Introduction/3.1 Course Notebooks/data/heartdisease.csv 12K 1. Introduction/3.1 Course Notebooks/data/heartdisease2.csv 4.0K 1. Introduction/3.1 Course Notebooks/data/heatmap.csv 68K 1. Introduction/3.1 Course Notebooks/data/housing.csv 136K 1. Introduction/3.1 Course Notebooks/data/hyundai.csv 8.0K 1. Introduction/3.1 Course Notebooks/data/income.csv 56K 1. Introduction/3.1 Course Notebooks/data/insurance.csv 24K 1. Introduction/3.1 Course Notebooks/data/loans.csv 352K 1. Introduction/3.1 Course Notebooks/data/loans_income.csv 104K 1. Introduction/3.1 Course Notebooks/data/marketing.csv 4.0K 1. Introduction/3.1 Course Notebooks/data/median.csv 44K 1. Introduction/3.1 Course Notebooks/data/radiation.csv 4.0K 1. Introduction/3.1 Course Notebooks/data/regression.csv 72K 1. Introduction/3.1 Course Notebooks/data/scores.csv 8.0K 1. Introduction/3.1 Course Notebooks/data/stocks.csv 12K 1. Introduction/3.1 Course Notebooks/data/transform_df.csv 348K 1. Introduction/3.1 Course Notebooks/data/wage.csv 4.0K 1. Introduction/3.1 Course Notebooks/data/weighted-mean.csv 40K 1. Introduction/3.1 Course Notebooks/img 0 1. Introduction/3.1 Course Notebooks/img/cmaps.bmp 36K 1. Introduction/3.1 Course Notebooks/img/cmaps.png 169M 10. Confidence Intervals (CI) 35M 10. Confidence Intervals (CI)/1. C.I for Continuous Data.mp4 31M 10. Confidence Intervals (CI)/2. C.I for Classification Data.mp4 37M 10. Confidence Intervals (CI)/3. Bootstrapping For Unknown Distributions.mp4 39M 10. Confidence Intervals (CI)/4. Nonparametric Confidence Interval with Bootstrapping.mp4 29M 10. Confidence Intervals (CI)/5. Exercise Create Confidence Interval.mp4 353M 11. Evaluation Metrics for Machine Learning 38M 11. Evaluation Metrics for Machine Learning/1. Bias vs. Variance.mp4 69M 11. Evaluation Metrics for Machine Learning/2. Overfitting and Underfitting.mp4 51M 11. Evaluation Metrics for Machine Learning/3. Information Criteria for Model Selection.mp4 43M 11. Evaluation Metrics for Machine Learning/4. Evaluation Metrics for Regression Models.mp4 29M 11. Evaluation Metrics for Machine Learning/5. Evaluation Metrics for Classification Models _Part One.mp4 40M 11. Evaluation Metrics for Machine Learning/6. Evaluation Metrics for Classification Models – Part Two.mp4 54M 11. Evaluation Metrics for Machine Learning/7. Application in Data Science.mp4 32M 11. Evaluation Metrics for Machine Learning/8. Exercise Evaluating Machine Learning Models.mp4 195M 12. Model Validation Techniques in Machine Learning 52M 12. Model Validation Techniques in Machine Learning/1. Hold Out Validation - TrainTest Split.mp4 37M 12. Model Validation Techniques in Machine Learning/2. K-Fold Cross-Validation.mp4 28M 12. Model Validation Techniques in Machine Learning/3. Leave-One-Out Cross-Validation (LOOCV).mp4 44M 12. Model Validation Techniques in Machine Learning/4. Application in Data Science.mp4 35M 12. Model Validation Techniques in Machine Learning/5. Exercise Validation Techniques in Machine Learning.mp4 177M 13. Final project 48M 13. Final project/1. Project Description.mp4 37M 13. Final project/2. Walk-through Solution of the Project – Part One.mp4 44M 13. Final project/3. Walk-through Solution of the Project – Part Two.mp4 50M 13. Final project/4. Walk-through Solution of the Project – Part Three.mp4 141M 2. Data Types and Structures 22M 2. Data Types and Structures/1. Built-in Data Structures - Tuple and List.mp4 16M 2. Data Types and Structures/2. Built-in Data Structures - Dictionary and Set.mp4 24M 2. Data Types and Structures/3. Numpy Arrays.mp4 33M 2. Data Types and Structures/4. Pandas Series and Dataframes.mp4 29M 2. Data Types and Structures/5. Data Types (Numeric or Categorical).mp4 18M 2. Data Types and Structures/6. Exercise Create Data Structures in Python.mp4 131M 3. Exploratory Data Analysis (1) Central Tendency Measures 29M 3. Exploratory Data Analysis (1) Central Tendency Measures/1. Mean (Average).mp4 24M 3. Exploratory Data Analysis (1) Central Tendency Measures/2. Weighted Average.mp4 19M 3. Exploratory Data Analysis (1) Central Tendency Measures/3. Median.mp4 31M 3. Exploratory Data Analysis (1) Central Tendency Measures/4. Population vs. Sample.mp4 16M 3. Exploratory Data Analysis (1) Central Tendency Measures/5. Application in Data Science.mp4 16M 3. Exploratory Data Analysis (1) Central Tendency Measures/6. Exercise Calculate Central Tendency Measures.mp4 207M 4. Exploratory Data Analysis (2) Variability Measures 20M 4. Exploratory Data Analysis (2) Variability Measures/1. Range.mp4 24M 4. Exploratory Data Analysis (2) Variability Measures/2. Variance and Standard Deviation.mp4 33M 4. Exploratory Data Analysis (2) Variability Measures/3. Percentile & Quartile.mp4 27M 4. Exploratory Data Analysis (2) Variability Measures/4. Outlier – part 1.mp4 25M 4. Exploratory Data Analysis (2) Variability Measures/5. Outlier – part 2.mp4 38M 4. Exploratory Data Analysis (2) Variability Measures/6. Sampling Error.mp4 24M 4. Exploratory Data Analysis (2) Variability Measures/7. Application in Data Science.mp4 19M 4. Exploratory Data Analysis (2) Variability Measures/8. Exercise Calculate Variability Measures.mp4 182M 5. Visualizing Data Distributions 29M 5. Visualizing Data Distributions/1. Box Plot.mp4 21M 5. Visualizing Data Distributions/2. Violin Plot.mp4 29M 5. Visualizing Data Distributions/3. Histogram and Density Plot.mp4 26M 5. Visualizing Data Distributions/4. Bar Plot for Categorical Data.mp4 19M 5. Visualizing Data Distributions/5. Pie Chart for Categorical Data.mp4 36M 5. Visualizing Data Distributions/6. Application in Data Science.mp4 25M 5. Visualizing Data Distributions/7. Exercise Exploring Data Distribution.mp4 180M 6. Correlation, Scatterplots, and Heat Maps 24M 6. Correlation, Scatterplots, and Heat Maps/1. Correlation and Covariance Coefficients.mp4 35M 6. Correlation, Scatterplots, and Heat Maps/2. Correlation Using Scatter plot.mp4 22M 6. Correlation, Scatterplots, and Heat Maps/3. Mapping with Scatter plots.mp4 35M 6. Correlation, Scatterplots, and Heat Maps/4. Heat Maps.mp4 42M 6. Correlation, Scatterplots, and Heat Maps/5. Application in Data Science.mp4 24M 6. Correlation, Scatterplots, and Heat Maps/6. Exercise Create Mapped Scatterplots and Heat Maps.mp4 56M 7. Capstone Project for Exploratory Analysis 20M 7. Capstone Project for Exploratory Analysis/1. Project Description.mp4 37M 7. Capstone Project for Exploratory Analysis/2. Solution walk-through of The Project.mp4 266M 8. Data Distributions and Data Sampling 32M 8. Data Distributions and Data Sampling/1. Random Sampling and Bias.mp4 29M 8. Data Distributions and Data Sampling/2. Central Limit Theorem.mp4 31M 8. Data Distributions and Data Sampling/3. Normal distribution.mp4 39M 8. Data Distributions and Data Sampling/4. Normality Tests for Real-World Data.mp4 48M 8. Data Distributions and Data Sampling/5. Skewed Data Real-life Distributions.mp4 16M 8. Data Distributions and Data Sampling/6. Probability A Practical Introduction.mp4 49M 8. Data Distributions and Data Sampling/7. Common Probability Distributions.mp4 25M 8. Data Distributions and Data Sampling/8. Exercise Normal Distribution and Skewness.mp4 205M 9. Data Scaling and Transformation 46M 9. Data Scaling and Transformation/1. Data Scaling Standardization.mp4 28M 9. Data Scaling and Transformation/2. Data Scaling Normalization.mp4 32M 9. Data Scaling and Transformation/3. Log and Square Root Transformations.mp4 40M 9. Data Scaling and Transformation/4. Power Transformation (PowerTransformer).mp4 39M 9. Data Scaling and Transformation/5. Application in Data Science.mp4 24M 9. Data Scaling and Transformation/6. Exercise Data Scaling and Transformation.mp4 2.4G total
File: 1. Overview of Course Curriculum.mp4 Size: 39042143 bytes (37.23 MiB), duration: 00:04:41, avg.bitrate: 1112 kb/s Audio: aac, 44100 Hz, stereo (und) Video: h264, yuv420p, 1280x720, 30.00 fps(r) (und)
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Keywords: Udemy, Statistics, For, Data, Science, and, Machine, Learning, with, Python, BOOKWARE, SOFTiMAGEDownload from Nitroflare
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