Udemy Statistics For Data Science and Machine Learning with Python

Posted on 21 Nov 18:02 | by AD-TEAM | 31 views
Udemy Statistics For Data Science and Machine Learning with Python


Udemy.Statistics.For.Data.Science.and.Machine.Learning.with.Python. SOFTiMAGE
Language: English
Files Type: mp4, sfv, bmp, csv, ipynb| Size: 2.27 GB
Video: 07:20:38 | 1280X720 | 588 Kbps
Audio: mp4a-40-2 | 128 Kbps | AAC
Genre:eLearning





Videos Files :
1. Overview of Course Curriculum.mp4 (37.23 MB)
2. Installing Jupyter Notebook Environment.mp4 (24.78 MB)
3. How to Download Exercises & Course Notebooks.mp4 (32.57 MB)
1. C.I for Continuous Data.mp4 (34.73 MB)
2. C.I for Classification Data.mp4 (30.11 MB)
3. Bootstrapping For Unknown Distributions.mp4 (36.27 MB)
4. Nonparametric Confidence Interval with Bootstrapping.mp4 (38.23 MB)
5. Exercise Create Confidence Interval.mp4 (28.89 MB)
1. Bias vs. Variance.mp4 (37.52 MB)
2. Overfitting and Underfitting.mp4 (68.11 MB)
3. Information Criteria for Model Selection.mp4 (50.9 MB)
4. Evaluation Metrics for Regression Models.mp4 (42.97 MB)
5. Evaluation Metrics for Classification Models Part One.mp4 (28.05 MB)
6. Evaluation Metrics for Classification Models – Part Two.mp4 (39.13 MB)
7. Application in Data Science.mp4 (53.99 MB)
8. Exercise Evaluating Machine Learning Models.mp4 (31.69 MB)
1. Hold Out Validation TrainTest Split.mp4 (51.95 MB)
2. K Fold Cross Validation.mp4 (37 MB)
3. Leave One Out Cross Validation (LOOCV).mp4 (27.25 MB)
4. Application in Data Science.mp4 (43.81 MB)
5. Exercise Validation Techniques in Machine Learning.mp4 (34.62 MB)
1. Project Description.mp4 (47.6 MB)
2. Walk through Solution of the Project – Part One.mp4 (36.23 MB)
3. Walk through Solution of the Project – Part Two.mp4 (43.08 MB)
4. Walk through Solution of the Project – Part Three.mp4 (49.8 MB)
1. Built in Data Structures Tuple and List.mp4 (21.78 MB)
2. Built in Data Structures Dictionary and Set.mp4 (15.8 MB)
3. Numpy Arrays.mp4 (23.27 MB)
4. Pandas Series and Dataframes.mp4 (32.47 MB)
5. Data Types (Numeric or Categorical).mp4 (28.96 MB)
6. Exercise Create Data Structures in Python.mp4 (17.86 MB)
1. Mean (Average).mp4 (28.2 MB)
2. Weighted Average.mp4 (23.04 MB)
3. Median.mp4 (18.08 MB)
4. Population vs. Sample.mp4 (30.72 MB)
5. Application in Data Science.mp4 (15.43 MB)
6. Exercise Calculate Central Tendency Measures.mp4 (15.41 MB)
1. Range.mp4 (19.26 MB)
2. Variance and Standard Deviation.mp4 (23.31 MB)
3. Percentile & Quartile.mp4 (32.14 MB)
4. Outlier – part 1.mp4 (26.77 MB)
5. Outlier – part 2.mp4 (24.57 MB)
6. Sampling Error.mp4 (37.33 MB)
7. Application in Data Science.mp4 (23.99 MB)
8. Exercise Calculate Variability Measures.mp4 (18.79 MB)
1. Box Plot.mp4 (28.65 MB)
2. Violin Plot.mp4 (20.06 MB)
3. Histogram and Density Plot.mp4 (28.81 MB)
4. Bar Plot for Categorical Data.mp4 (25.11 MB)
5. Pie Chart for Categorical Data.mp4 (18.44 MB)
6. Application in Data Science.mp4 (35.41 MB)
7. Exercise Exploring Data Distribution.mp4 (24.99 MB)
1. Correlation and Covariance Coefficients.mp4 (23.36 MB)
2. Correlation Using Scatter plot.mp4 (34.04 MB)
3. Mapping with Scatter plots.mp4 (21.96 MB)
4. Heat Maps.mp4 (34.82 MB)
5. Application in Data Science.mp4 (41.84 MB)
6. Exercise Create Mapped Scatterplots and Heat Maps.mp4 (23.87 MB)
1. Project Description.mp4 (19.96 MB)
2. Solution walk through of The Project.mp4 (36 MB)
1. Random Sampling and Bias.mp4 (31.41 MB)
2. Central Limit Theorem.mp4 (28.43 MB)
3. Normal distribution.mp4 (30.47 MB)
4. Normality Tests for Real World Data.mp4 (38.72 MB)
5. Skewed Data Real life Distributions.mp4 (47.45 MB)
6. Probability A Practical Introduction.mp4 (15.87 MB)
7. Common Probability Distributions.mp4 (48.7 MB)
8. Exercise Normal Distribution and Skewness.mp4 (24.55 MB)
1. Data Scaling Standardization.mp4 (45.1 MB)
2. Data Scaling Normalization.mp4 (27.01 MB)
3. Log and Square Root Transformations.mp4 (31.81 MB)
4. Power Transformation (PowerTransformer).mp4 (39.18 MB)
5. Application in Data Science.mp4 (38.29 MB)
6. Exercise Data Scaling and Transformation.mp4 (23.45 MB)



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