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种子名称： [Udemy] Python for Data Science & Machine Learning from A-Z (01.2021)
文件类型： 视频
文件数目： 140个文件
文件大小： 7.32 GB
收录时间： 2022-8-17 16:52
已经下载： 3次
资源热度： 182
最近下载： 2024-5-17 03:13

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[Udemy] Python for Data Science & Machine Learning from A-Z (01.2021).torrent

1. Introduction/1. Who is This Course For.mp417.16MB

1. Introduction/2. Data Science + Machine Learning Marketplace.mp446.94MB

1. Introduction/3. Data Science Job Opportunities.mp429.42MB

1. Introduction/4. Data Science Job Roles.mp479.8MB

1. Introduction/5. What is a Data Scientist.mp4127.47MB

1. Introduction/6. How To Get a Data Science Job.mp4131.19MB

1. Introduction/7. Data Science Projects Overview.mp479.48MB

10. Data Loading & Exploration/1. Exploratory Data Analysis.mp450.56MB

11. Data Cleaning/1. Feature Scaling.mp419.38MB

11. Data Cleaning/2. Data Cleaning.mp430.21MB

12. Feature Selecting and Engineering/1. Feature Engineering.mp418.41MB

13. Linear and Logistic Regression/1. Linear Regression Intro.mp430.8MB

13. Linear and Logistic Regression/2. Gradient Descent.mp415.93MB

13. Linear and Logistic Regression/3. Linear Regression + Correlation Methods.mp4110.38MB

13. Linear and Logistic Regression/4. Linear Regression Implementation.mp417.86MB

13. Linear and Logistic Regression/5. Logistic Regression.mp48.9MB

14. K Nearest Neighbors/1. KNN Overview.mp412.89MB

14. K Nearest Neighbors/10. Feature scaling in KNN.mp449.39MB

14. K Nearest Neighbors/11. Curse of dimensionality.mp445.99MB

14. K Nearest Neighbors/12. KNN use cases.mp428.92MB

14. K Nearest Neighbors/13. KNN pros and cons.mp430.45MB

14. K Nearest Neighbors/2. parametric vs non-parametric models.mp415.63MB

14. K Nearest Neighbors/3. EDA on Iris Dataset.mp4161.88MB

14. K Nearest Neighbors/4. The KNN Intuition.mp48.09MB

14. K Nearest Neighbors/5. Implement the KNN algorithm from scratch.mp486.97MB

14. K Nearest Neighbors/6. Compare the result with the sklearn library.mp424.57MB

14. K Nearest Neighbors/7. Hyperparameter tuning using the cross-validation.mp490.3MB

14. K Nearest Neighbors/8. The decision boundary visualization.mp416.94MB

14. K Nearest Neighbors/9. Manhattan vs Euclidean Distance.mp430.49MB

15. Decision Trees/1. Decision Trees Section Overview.mp416.46MB

15. Decision Trees/10. Visualizing the tree.mp468.17MB

15. Decision Trees/11. Plot the features importance.mp431.67MB

15. Decision Trees/12. Decision Trees Hyper-parameters.mp481.27MB

15. Decision Trees/13. Pruning.mp4112.97MB

15. Decision Trees/14. [Optional] Gain Ration.mp419.18MB

15. Decision Trees/15. Decision Trees Pros and Cons.mp447.74MB

15. Decision Trees/16. [Project] Predict whether income exceeds $50Kyr - Overview.mp415.11MB

15. Decision Trees/2. EDA on Adult Dataset.mp4123.19MB

15. Decision Trees/3. What is Entropy and Information Gain.mp4136.08MB

15. Decision Trees/4. The Decision Tree ID3 algorithm from scratch Part 1.mp485.27MB

15. Decision Trees/5. The Decision Tree ID3 algorithm from scratch Part 2.mp463.96MB

15. Decision Trees/6. The Decision Tree ID3 algorithm from scratch Part 3.mp433.41MB

15. Decision Trees/7. ID3 - Putting Everything Together.mp4182.48MB

15. Decision Trees/8. Evaluating our ID3 implementation.mp4121.94MB

15. Decision Trees/9. Compare with Sklearn implementation.mp465.58MB

16. Ensemble Learning and Random Forests/1. Ensemble Learning Section Overview.mp416.07MB

16. Ensemble Learning and Random Forests/10. Random Forests Pros and Cons.mp419.69MB

16. Ensemble Learning and Random Forests/11. What is Boosting.mp435.44MB

16. Ensemble Learning and Random Forests/12. AdaBoost Part 1.mp425.53MB

16. Ensemble Learning and Random Forests/13. AdaBoost Part 2.mp485.94MB

16. Ensemble Learning and Random Forests/2. What is Ensemble Learning.mp491.97MB

16. Ensemble Learning and Random Forests/3. What is Bootstrap Sampling.mp455.88MB

16. Ensemble Learning and Random Forests/4. What is Bagging.mp429.48MB

16. Ensemble Learning and Random Forests/5. Out-of-Bag Error (OOB Error).mp442.03MB

16. Ensemble Learning and Random Forests/6. Implementing Random Forests from scratch Part 1.mp4202.55MB

16. Ensemble Learning and Random Forests/7. Implementing Random Forests from scratch Part 2.mp450.5MB

16. Ensemble Learning and Random Forests/8. Compare with sklearn implementation.mp427.65MB

16. Ensemble Learning and Random Forests/9. Random Forests Hyper-Parameters.mp439.67MB

17. Support Vector Machines/1. SVM Outline.mp435.31MB

17. Support Vector Machines/10. SMV - Project Overview.mp439.61MB

17. Support Vector Machines/2. SVM intuition.mp448.86MB

17. Support Vector Machines/3. Hard vs Soft Margins.mp465.64MB

17. Support Vector Machines/4. C hyper-parameter.mp421.06MB

17. Support Vector Machines/5. Kernel Trick.mp477.05MB

17. Support Vector Machines/6. SVM - Kernel Types.mp4126.38MB

17. Support Vector Machines/7. SVM with Linear Dataset (Iris).mp4101.56MB

17. Support Vector Machines/8. SVM with Non-linear Dataset.mp4111.55MB

17. Support Vector Machines/9. SVM with Regression.mp425MB

18. K-means/1. Unsupervised Machine Learning Intro.mp4100.92MB

18. K-means/2. Unsupervised Machine Learning Continued.mp483.13MB

18. K-means/3. Representing Clusters.mp4109.62MB

19. PCA/1. PCA Section Overview.mp431.77MB

19. PCA/10. PCA - Feature Scaling and Screen Plot.mp468.2MB

19. PCA/11. PCA - Supervised vs Unsupervised.mp435.79MB

19. PCA/12. PCA - Visualization.mp468.02MB

19. PCA/2. What is PCA.mp447.26MB

19. PCA/3. PCA Drawbacks.mp419.44MB

19. PCA/4. PCA Algorithm Steps (Mathematics).mp457.73MB

19. PCA/5. Covariance Matrix vs SVD.mp438.74MB

19. PCA/6. PCA - Main Applications.mp410.05MB

19. PCA/7. PCA - Image Compression.mp4249.92MB

19. PCA/8. PCA Data Preprocessing.mp4120.46MB

19. PCA/9. PCA - Biplot and the Screen Plot.mp4135.6MB

2. Data Science & Machine Learning Concepts/1. Why We Use Python.mp413.51MB

2. Data Science & Machine Learning Concepts/2. What is Data Science.mp487.99MB

2. Data Science & Machine Learning Concepts/3. What is Machine Learning.mp483.41MB

2. Data Science & Machine Learning Concepts/4. Machine Learning Concepts & Algorithms.mp477.98MB

2. Data Science & Machine Learning Concepts/5. What is Deep Learning.mp477.81MB

2. Data Science & Machine Learning Concepts/6. Machine Learning vs Deep Learning.mp475.92MB

20. Data Science Career/1. Creating A Data Science Resume.mp437.08MB

20. Data Science Career/2. Data Science Cover Letter.mp422.96MB

20. Data Science Career/3. How to Contact Recruiters.mp424.65MB

20. Data Science Career/4. Getting Started with Freelancing.mp430.24MB

20. Data Science Career/5. Top Freelance Websites.mp429.55MB

20. Data Science Career/6. Personal Branding.mp430.49MB

20. Data Science Career/7. Networking Do's and Don'ts.mp423.7MB

20. Data Science Career/8. Importance of a Website.mp415.37MB

3. Python For Data Science/1. What is Programming.mp418.35MB

3. Python For Data Science/10. Python Conditional Statements.mp454.61MB

3. Python For Data Science/11. Python For Loops and While Loops.mp425.61MB

3. Python For Data Science/12. Python Lists.mp421.44MB

3. Python For Data Science/13. More about Lists.mp460.42MB

3. Python For Data Science/14. Python Tuples.mp454.53MB

3. Python For Data Science/15. Python Dictionaries.mp4104.18MB

3. Python For Data Science/16. Python Sets.mp429.43MB

3. Python For Data Science/17. Compound Data Types & When to use each one.mp447.07MB

3. Python For Data Science/18. Python Functions.mp462.51MB

3. Python For Data Science/19. Object Oriented Programming in Python.mp470.25MB

3. Python For Data Science/2. Why Python for Data Science.mp416.32MB

3. Python For Data Science/3. What is Jupyter.mp414.57MB

3. Python For Data Science/4. What is Google Colab.mp48.26MB

3. Python For Data Science/5. Python Variables, Booleans and None.mp438.26MB

3. Python For Data Science/6. Getting Started with Google Colab.mp435.09MB

3. Python For Data Science/7. Python Operators.mp486.76MB

3. Python For Data Science/8. Python Numbers & Booleans.mp425.61MB

3. Python For Data Science/9. Python Strings.mp456.27MB

4. Statistics for Data Science/1. Intro To Statistics.mp421.24MB

4. Statistics for Data Science/2. Descriptive Statistics.mp421.48MB

4. Statistics for Data Science/3. Measure of Variability.mp438.2MB

4. Statistics for Data Science/4. Measure of Variability Continued.mp434.61MB

4. Statistics for Data Science/5. Measures of Variable Relationship.mp423.57MB

4. Statistics for Data Science/6. Inferential Statistics.mp445.01MB

4. Statistics for Data Science/7. Measure of Asymmetry.mp46.76MB

4. Statistics for Data Science/8. Sampling Distribution.mp426.46MB

5. Probability & Hypothesis Testing/1. What is Exactly is Probability.mp427.17MB

5. Probability & Hypothesis Testing/2. Expected Values.mp414.72MB

5. Probability & Hypothesis Testing/3. Relative Frequency.mp432.69MB

5. Probability & Hypothesis Testing/4. Hypothesis Testing Overview.mp460.59MB

6. NumPy Data Analysis/1. Intro NumPy Array Data Types.mp434.67MB

6. NumPy Data Analysis/2. NumPy Arrays.mp432.33MB

6. NumPy Data Analysis/3. NumPy Arrays Basics.mp439.98MB

6. NumPy Data Analysis/4. NumPy Array Indexing.mp434.74MB

6. NumPy Data Analysis/5. NumPy Array Computations.mp416.96MB

6. NumPy Data Analysis/6. Broadcasting.mp417.86MB

7. Pandas Data Analysis/1. Introduction to Pandas.mp446.83MB

7. Pandas Data Analysis/2. Introduction to Pandas Continued.mp471.1MB

8. Python Data Visualization/1. Data Visualization Overview.mp473.08MB

8. Python Data Visualization/2. Different Data Visualization Libraries in Python.mp415.95MB

8. Python Data Visualization/3. Python Data Visualization Implementation.mp427.43MB

9. Machine Learning/1. Introduction To Machine Learning.mp498.71MB