首页
磁力链接怎么用
한국어
English
日本語
简体中文
繁體中文
[GigaCourse.Com] Udemy - Machine Learning in Python with 5 Machine Learning Projects
文件类型
收录时间
最后活跃
资源热度
文件大小
文件数量
视频
2021-10-11 00:30
2024-8-12 03:25
189
20.82 GB
381
磁力链接
magnet:?xt=urn:btih:98f5357292cb5751e089a270d7c1e4570d0cf166
迅雷链接
thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOjk4ZjUzNTcyOTJjYjU3NTFlMDg5YTI3MGQ3YzFlNDU3MGQwY2YxNjZaWg==
二维码链接
种子下载(838888不存储任何种子文件)
种子下载线路1(迅雷)--推荐
种子下载线路2(比特彗星)
种子下载线路3(torcache)
3条线路均为国内外知名下载网站种子链接,内容跟本站无关!
相关链接
GigaCourse
Com
Udemy
-
Machine
Learning
in
Python
with
5
Machine
Learning
Projects
文件列表
1. Python Fundamentals/1. Why should you learn Python.mp4
65.68MB
1. Python Fundamentals/10. Identity and Membership Operators.mp4
39.22MB
1. Python Fundamentals/12. Quiz Solution.mp4
34.21MB
1. Python Fundamentals/13. String Formatting.mp4
51.35MB
1. Python Fundamentals/14. String Methods.mp4
43.29MB
1. Python Fundamentals/15. User Input.mp4
41.04MB
1. Python Fundamentals/17. Quiz Solution.mp4
53.11MB
1. Python Fundamentals/18. If, elif, and else.mp4
65.9MB
1. Python Fundamentals/19. For and While.mp4
53.07MB
1. Python Fundamentals/2. Installing Python and Jupyter Notebook.mp4
33.48MB
1. Python Fundamentals/20. Break and Continue.mp4
40.72MB
1. Python Fundamentals/22. Quiz Solution.mp4
49.04MB
1. Python Fundamentals/3. Naming Convention for Variables.mp4
102.24MB
1. Python Fundamentals/4. Built in Data Types and Type Casting.mp4
119.86MB
1. Python Fundamentals/5. Scope of Variables.mp4
77.16MB
1. Python Fundamentals/7. Quiz Solution.mp4
46.52MB
1. Python Fundamentals/8. Arithmetic and Assignment Operators.mp4
78.04MB
1. Python Fundamentals/9. Comparison, Logical, and Bitwise Operators.mp4
62.41MB
10. Logistic Regression/1. Introduction to Logistic Regression.mp4
106.4MB
10. Logistic Regression/10. Industry Relevance of Logistic Regression.mp4
59.89MB
10. Logistic Regression/2. Implementing Logistic Regression using Sklearn.mp4
87.01MB
10. Logistic Regression/3. Feature Selection using RFECV.mp4
42.15MB
10. Logistic Regression/4. Hyperparameter tuning using Grid search.mp4
58.75MB
10. Logistic Regression/5. Applying Cross Validation.mp4
56.73MB
10. Logistic Regression/6. How to analyze performance of a classification model.mp4
146.18MB
10. Logistic Regression/7. Using accuracy score to analyze the performance of model.mp4
55.54MB
10. Logistic Regression/8. Using ROC-AUC score to analyze the performance of model.mp4
147.63MB
10. Logistic Regression/9. Real time prediction using logistic regression.mp4
74.65MB
11. Introduction to KNN, SVM, Naive Bayes/1. Introduction to Support Vector machines.mp4
108.17MB
11. Introduction to KNN, SVM, Naive Bayes/2. The kermel trick for support vector machine.mp4
70.38MB
11. Introduction to KNN, SVM, Naive Bayes/3. Implementing support vector machine using sklearn.mp4
67.43MB
11. Introduction to KNN, SVM, Naive Bayes/4. Introduction to K nearest neighbors.mp4
104.32MB
11. Introduction to KNN, SVM, Naive Bayes/5. Implementing KNN using Sklearn.mp4
33.23MB
11. Introduction to KNN, SVM, Naive Bayes/6. Introduction to Naive Bayes.mp4
174.72MB
11. Introduction to KNN, SVM, Naive Bayes/7. Implementing Naive Bayes using sklearn.mp4
61.96MB
11. Introduction to KNN, SVM, Naive Bayes/8. When should we apply SVM, KNN and Naive bayes.mp4
69.77MB
12. Tree Based Models/1. Intuition for decision trees.mp4
81.99MB
12. Tree Based Models/2. Attribute selection method- Gini Index and Entropy.mp4
218.66MB
12. Tree Based Models/3. Advantages and Issues with Decision trees.mp4
53.37MB
12. Tree Based Models/4. Implementing Decision tree using Sklearn.mp4
35.8MB
12. Tree Based Models/5. Understanding the concept of Bagging.mp4
65.99MB
12. Tree Based Models/6. Introduction to Random forest.mp4
68.09MB
12. Tree Based Models/7. Understanding the parameters of Random forest.mp4
53.66MB
12. Tree Based Models/8. Implementing random forest using Sklearn.mp4
47.88MB
13. Boosting Models/1. Understading the concept of boosting.mp4
57.14MB
13. Boosting Models/2. Intuition for Adaboost and Gradient Boosting.mp4
153.3MB
13. Boosting Models/3. Implementing AdaBoost using sklearn.mp4
90.82MB
13. Boosting Models/4. Implementing Gradient Boosting using sklearn.mp4
66.93MB
13. Boosting Models/5. Getting High level intuition for XGBoost.mp4
41.07MB
13. Boosting Models/6. Implementing XGBoost using sklearn.mp4
65.14MB
13. Boosting Models/7. Introudction to Ensembling techniques.mp4
134.02MB
14. Imbalanced Machine Learning/1. Why Imbalanced Data needs extra attention.mp4
53.62MB
14. Imbalanced Machine Learning/10. Implementing Synthetic Sampling using Imblearn.mp4
57.45MB
14. Imbalanced Machine Learning/11. Implementing Neighbors based Sampling using Imblearn.mp4
64.02MB
14. Imbalanced Machine Learning/12. Combination of Oversampling and Under sampling.mp4
55.76MB
14. Imbalanced Machine Learning/13. Implementing Ensemble Models for Imbalanced Data.mp4
54.88MB
14. Imbalanced Machine Learning/14. Introduction to XG Boost for Imbalanced Data.mp4
43.54MB
14. Imbalanced Machine Learning/15. Comparing the Results.mp4
41.5MB
14. Imbalanced Machine Learning/2. Using Resampling Techniques to Balance the Data.mp4
70.55MB
14. Imbalanced Machine Learning/3. Solving a Real World Problem.mp4
56.98MB
14. Imbalanced Machine Learning/4. Preparing the Data for Predictive Modelling.mp4
57.93MB
14. Imbalanced Machine Learning/5. Applying Logistic Regression using Sklearn.mp4
71.14MB
14. Imbalanced Machine Learning/6. Applying Random Forest using Sklearn.mp4
42.65MB
14. Imbalanced Machine Learning/8. Implementing Random Over Sampling using Imblearn.mp4
54.41MB
14. Imbalanced Machine Learning/9. Implementing Random Under Sampling using Imblearn.mp4
57.54MB
15. Introduction to Clustering Analysis/1. Introduction to Clustering.mp4
57.84MB
15. Introduction to Clustering Analysis/10. Clustering Multiple Dimensions.mp4
50.01MB
15. Introduction to Clustering Analysis/12. Introduction to Hierarchal Clustering.mp4
88.49MB
15. Introduction to Clustering Analysis/13. Introduction to Dendrograms.mp4
41.78MB
15. Introduction to Clustering Analysis/14. Implementing Hierarchial Clustering.mp4
52.35MB
15. Introduction to Clustering Analysis/15. Introduction to DBSCAN Clustering.mp4
52.38MB
15. Introduction to Clustering Analysis/16. Implementing DBSCAN Clustering.mp4
47.87MB
15. Introduction to Clustering Analysis/2. Types of Clustering.mp4
65.18MB
15. Introduction to Clustering Analysis/3. Applications of Clustering.mp4
55.95MB
15. Introduction to Clustering Analysis/5. Using the Elbow Method for Choosing the Best Value for K.mp4
67.06MB
15. Introduction to Clustering Analysis/6. Introduction to K Means Clustering.mp4
49.29MB
15. Introduction to Clustering Analysis/7. Solving a Real World Problem.mp4
71MB
15. Introduction to Clustering Analysis/8. Implementing K Means on the Mall Dataset.mp4
71.57MB
15. Introduction to Clustering Analysis/9. Using Silhouette Score to analyze the clusters.mp4
96.34MB
16. Dimensionality Reduction/1. Why High Dimensional Datasets are a Problem.mp4
79.22MB
16. Dimensionality Reduction/11. Introduction to Recursive Feature Selection.mp4
56.62MB
16. Dimensionality Reduction/12. Implementing Recursive Feature Selection.mp4
50.92MB
16. Dimensionality Reduction/13. Introduction the Boruta Algorithm.mp4
52.48MB
16. Dimensionality Reduction/14. Implementing the Boruta Algorithm.mp4
43.2MB
16. Dimensionality Reduction/16. Introduction to Principal Component Analysis.mp4
73.79MB
16. Dimensionality Reduction/17. Implementing PCA.mp4
55.52MB
16. Dimensionality Reduction/18. Introduction to t-SNE.mp4
81.27MB
16. Dimensionality Reduction/19. Implementing t-SNE.mp4
36.11MB
16. Dimensionality Reduction/2. Methods to solve the problem of High Dimensionality.mp4
57.16MB
16. Dimensionality Reduction/20. Introduction to Linear Discriminant Analysis.mp4
48.9MB
16. Dimensionality Reduction/21. Implementing LDA.mp4
36.74MB
16. Dimensionality Reduction/22. Difference between PCA, t-SNE, and LDA.mp4
64.79MB
16. Dimensionality Reduction/3. Solving a Real World Problem.mp4
98.82MB
16. Dimensionality Reduction/5. Introduction to Correlation using Heatmap.mp4
71.4MB
16. Dimensionality Reduction/6. Removing Highly Correlated Columns using Correlation.mp4
48.87MB
16. Dimensionality Reduction/8. Introduction to Variance Inflation Filtering.mp4
48.66MB
16. Dimensionality Reduction/9. Implementing VIF using statsmodel.mp4
47.84MB
17. Recommendation Engines/1. Introduction to Recommender systems.mp4
40.53MB
17. Recommendation Engines/11. Quiz Solution.mp4
48.5MB
17. Recommendation Engines/12. Introduction to Collaborative Filtering.mp4
80.86MB
17. Recommendation Engines/13. Preprocessing the Data for Collaborative Filtering.mp4
72.39MB
17. Recommendation Engines/14. Implementation of User Based Collaborative Filtering.mp4
62.15MB
17. Recommendation Engines/15. Interpreting the Results obtained from User Based Filtering.mp4
63.59MB
17. Recommendation Engines/16. Implementation of Item Based Collaborative Filtering.mp4
63.55MB
17. Recommendation Engines/18. Quiz Solution.mp4
55.62MB
17. Recommendation Engines/19. Introduction to SVD.mp4
112.02MB
17. Recommendation Engines/2. What are it's Use Cases.mp4
45.05MB
17. Recommendation Engines/20. Implementing SVD using Surprise.mp4
40.63MB
17. Recommendation Engines/21. Interpreting Results Obtained from SVD.mp4
46.01MB
17. Recommendation Engines/22. Comparing Content, and Collaborative Based Filtering.mp4
61.99MB
17. Recommendation Engines/24. Quiz Solution.mp4
47.94MB
17. Recommendation Engines/25. Case Study for Netflix.mp4
56.38MB
17. Recommendation Engines/26. Case Study for Youtube.mp4
58.14MB
17. Recommendation Engines/3. Types of Recommender Systems.mp4
56.54MB
17. Recommendation Engines/4. Evaluating Recommender Systems.mp4
53.15MB
17. Recommendation Engines/5. Introduction to Content Based Filtering.mp4
59MB
17. Recommendation Engines/6. Preprocessing the Data for Content Based Filtering.mp4
76.67MB
17. Recommendation Engines/7. Filtering Movies Based on Genres.mp4
58.73MB
17. Recommendation Engines/8. Introduction to Transactional Encoder.mp4
63.39MB
17. Recommendation Engines/9. Recommending Similar Movies to Watch.mp4
56.31MB
18. Time Series Forecasting/1. What is a Time Series Data.mp4
34.91MB
18. Time Series Forecasting/10. Time Series Decomposition.mp4
89.93MB
18. Time Series Forecasting/11. Splitting Time Series Data.mp4
63.5MB
18. Time Series Forecasting/13. Basic Forecasting Techniques.mp4
55.48MB
18. Time Series Forecasting/14. Metrics for Time series Forecasting.mp4
78.7MB
18. Time Series Forecasting/15. Simple Moving Averages.mp4
50.11MB
18. Time Series Forecasting/16. Simple Exponential Smoothing.mp4
66.62MB
18. Time Series Forecasting/17. Holt and Holt Winter Exponential Smoothing.mp4
73.13MB
18. Time Series Forecasting/19. Introduction to Auto Regressive Models.mp4
34.71MB
18. Time Series Forecasting/2. Types of Forecasting.mp4
45.3MB
18. Time Series Forecasting/20. Checking for Stationarity Part 1.mp4
65MB
18. Time Series Forecasting/21. Checking for Stationarity using Statistical Methods Part 2.mp4
75.44MB
18. Time Series Forecasting/22. Checking for Stationary Implementation.mp4
38.1MB
18. Time Series Forecasting/23. Converting Non-Stationary Series into Stationary.mp4
48.1MB
18. Time Series Forecasting/24. Converting Non-Stationary Series into Stationary Implementation.mp4
48.17MB
18. Time Series Forecasting/25. Auto Correlation and Partial Correlation.mp4
76.85MB
18. Time Series Forecasting/26. Auto Correlation and Partial Correlation Implementation.mp4
38.48MB
18. Time Series Forecasting/27. The Simple Auto Regressive Model.mp4
63.42MB
18. Time Series Forecasting/28. The Simple Auto Regressive Model Implementation.mp4
64.98MB
18. Time Series Forecasting/29. Moving Average Model.mp4
35.3MB
18. Time Series Forecasting/3. Regression Vs Time Series.mp4
82.95MB
18. Time Series Forecasting/30. Moving Average Model Implementation.mp4
23.23MB
18. Time Series Forecasting/32. Understanding ARMA Model.mp4
56.79MB
18. Time Series Forecasting/33. Implementing ARMA Model.mp4
48.21MB
18. Time Series Forecasting/34. Understanding ARIMA Model.mp4
55.87MB
18. Time Series Forecasting/35. Implementing ARIMA Model.mp4
33.2MB
18. Time Series Forecasting/36. Understanding SARIMA Model.mp4
69.94MB
18. Time Series Forecasting/37. Implementing SARIMA Model.mp4
38.13MB
18. Time Series Forecasting/39. Understanding ARIMAX Model.mp4
66.51MB
18. Time Series Forecasting/4. Applications of Time Series.mp4
47.29MB
18. Time Series Forecasting/40. Implementing ARIMAX Model.mp4
44.76MB
18. Time Series Forecasting/41. Understanding SARIMAX Model.mp4
43.84MB
18. Time Series Forecasting/42. Implementing SARIMAX Model.mp4
59.96MB
18. Time Series Forecasting/44. How to Choose the Right Model.mp4
35.14MB
18. Time Series Forecasting/45. Choosing the Right for Model Smaller Datasets.mp4
52.3MB
18. Time Series Forecasting/46. Choosing the Right Model for Larger Datasets.mp4
36.31MB
18. Time Series Forecasting/47. Best Practices while Choosing a Time series Model..mp4
43.02MB
18. Time Series Forecasting/49. Why do we Evaluate Performance.mp4
31.77MB
18. Time Series Forecasting/5. Components of Time Series.mp4
51.96MB
18. Time Series Forecasting/50. Mean Forecast Error.mp4
52.91MB
18. Time Series Forecasting/51. Mean Absolute Error.mp4
35.56MB
18. Time Series Forecasting/52. Mean Absolute Percentage Error.mp4
29.76MB
18. Time Series Forecasting/53. Root Mean Squared Error.mp4
29.34MB
18. Time Series Forecasting/7. Getting Time Series data.mp4
71.08MB
18. Time Series Forecasting/8. Handling Missing Values.mp4
116.47MB
18. Time Series Forecasting/9. Handling Outlier Values.mp4
64.43MB
19. Employee Promotion Prediction/1. Setting up the Environment.mp4
41.71MB
19. Employee Promotion Prediction/10. Feature Engineering.mp4
50.43MB
19. Employee Promotion Prediction/11. Categorical Encoding.mp4
37.44MB
19. Employee Promotion Prediction/12. Data Processing.mp4
67.65MB
19. Employee Promotion Prediction/13. Feature Scaling.mp4
42.28MB
19. Employee Promotion Prediction/14. Predictive Modelling.mp4
44.65MB
19. Employee Promotion Prediction/15. Performance Analysis.mp4
77.16MB
19. Employee Promotion Prediction/16. Improvements Possible.mp4
41.87MB
19. Employee Promotion Prediction/17. Major Takeaways from the Project.mp4
28.99MB
19. Employee Promotion Prediction/2. Understanding the Dataset.mp4
95.88MB
19. Employee Promotion Prediction/3. Understanding the Problem Statement.mp4
59.78MB
19. Employee Promotion Prediction/4. Performing Descriptive Statistics.mp4
61.68MB
19. Employee Promotion Prediction/5. Missing Values Treatment.mp4
38.66MB
19. Employee Promotion Prediction/6. Outlier Values Treatment.mp4
42.49MB
19. Employee Promotion Prediction/7. Univariate Analysis.mp4
53.13MB
19. Employee Promotion Prediction/8. Bivariate Analysis.mp4
37.16MB
19. Employee Promotion Prediction/9. Multivariate Analysis.mp4
39.94MB
2. Python for Data Analysis/1. Differences between Lists and Tuples.mp4
48.66MB
2. Python for Data Analysis/11. Quiz Solution.mp4
38.27MB
2. Python for Data Analysis/12. Introduction to Stacks and Queues.mp4
48.69MB
2. Python for Data Analysis/13. Implementing Stacks and Queues using Lists.mp4
36.5MB
2. Python for Data Analysis/14. Implementing Stacks and Queues using Deque.mp4
41.6MB
2. Python for Data Analysis/16. Quiz Solution.mp4
39.51MB
2. Python for Data Analysis/17. Time Complexity.mp4
120.13MB
2. Python for Data Analysis/18. Linear Search.mp4
95.52MB
2. Python for Data Analysis/19. Binary Search.mp4
109.54MB
2. Python for Data Analysis/2. Operations on Lists.mp4
44.4MB
2. Python for Data Analysis/20. Bubble Sort.mp4
75.55MB
2. Python for Data Analysis/21. Insertion and Selection Sort.mp4
120MB
2. Python for Data Analysis/22. Merge Sort.mp4
115.44MB
2. Python for Data Analysis/24. Quiz Solution.mp4
73.24MB
2. Python for Data Analysis/3. Operations on Tuples.mp4
27.44MB
2. Python for Data Analysis/5. Quiz Solution.mp4
37.09MB
2. Python for Data Analysis/6. Introduction to Dictionaries.mp4
66.83MB
2. Python for Data Analysis/7. Nested Dictionaries.mp4
60.55MB
2. Python for Data Analysis/8. Introduction to Sets.mp4
75.49MB
2. Python for Data Analysis/9. Set Operations.mp4
58.59MB
20. Predicting Health Expense of Customers/1. Setting up the Environment.mp4
50.12MB
20. Predicting Health Expense of Customers/10. Applying Gradient Boosting Model.mp4
70.38MB
20. Predicting Health Expense of Customers/11. Creating Ensembles of Models.mp4
57.07MB
20. Predicting Health Expense of Customers/12. Comparing Performance of these Models.mp4
36.54MB
20. Predicting Health Expense of Customers/13. More things to Try.mp4
48.69MB
20. Predicting Health Expense of Customers/14. Major Takeaways from the Project.mp4
57.64MB
20. Predicting Health Expense of Customers/2. Understanding the Dataset.mp4
104.05MB
20. Predicting Health Expense of Customers/3. Understanding the Problem Statement.mp4
61.8MB
20. Predicting Health Expense of Customers/4. Performing Univariate Analysis.mp4
89.75MB
20. Predicting Health Expense of Customers/5. Performing Bivariate Analysis.mp4
71.46MB
20. Predicting Health Expense of Customers/6. Performing Multivariate Analysis.mp4
85.97MB
20. Predicting Health Expense of Customers/7. Preparing the data for Modelling.mp4
90.86MB
20. Predicting Health Expense of Customers/8. Applying Linear Regression Model.mp4
128.08MB
20. Predicting Health Expense of Customers/9. Applying Random Forest Model.mp4
54.39MB
21. Determining Whether a Person should be Granted Loan/1. Understanding the Problem Statement.mp4
45.49MB
21. Determining Whether a Person should be Granted Loan/10. Applying Logistic Regression.mp4
52.39MB
21. Determining Whether a Person should be Granted Loan/11. Applying Gradient Boosting.mp4
38.62MB
21. Determining Whether a Person should be Granted Loan/12. Summary.mp4
44.17MB
21. Determining Whether a Person should be Granted Loan/2. Setting up the Environment.mp4
68.6MB
21. Determining Whether a Person should be Granted Loan/3. Understanding the Dataset.mp4
41.13MB
21. Determining Whether a Person should be Granted Loan/4. Performing Descriptive Statistics.mp4
75.32MB
21. Determining Whether a Person should be Granted Loan/5. Data Cleaning.mp4
66.97MB
21. Determining Whether a Person should be Granted Loan/6. Univariate Data Visualizations.mp4
65.17MB
21. Determining Whether a Person should be Granted Loan/7. Bivariate Data Analysis.mp4
70.21MB
21. Determining Whether a Person should be Granted Loan/8. Preparing the Data for Modelling.mp4
42.83MB
21. Determining Whether a Person should be Granted Loan/9. Applying Resampling.mp4
56.96MB
22. Optimizing Agricultural Production/1. Setting up the Environment.mp4
46.43MB
22. Optimizing Agricultural Production/10. Summarizing the Key-Points.mp4
40.45MB
22. Optimizing Agricultural Production/2. Understanding the Dataset.mp4
55.18MB
22. Optimizing Agricultural Production/3. Understanding the Problem Statement.mp4
35.4MB
22. Optimizing Agricultural Production/4. Performing Descriptive Statistics.mp4
73.57MB
22. Optimizing Agricultural Production/5. Analyzing Agricultural Conditions.mp4
39.18MB
22. Optimizing Agricultural Production/6. Clustering Similar Crops.mp4
63.62MB
22. Optimizing Agricultural Production/7. Visualizing the Hidden Patterns.mp4
27.79MB
22. Optimizing Agricultural Production/8. Predictive Modelling.mp4
40.38MB
22. Optimizing Agricultural Production/9. Real Time Predictions.mp4
27.66MB
3. Python Functions Deep Dive/1. Introduction to Functions.mp4
40.22MB
3. Python Functions Deep Dive/10. List, set, and Dictionary Comprehensions.mp4
54.58MB
3. Python Functions Deep Dive/12. Quiz Solution.mp4
40.26MB
3. Python Functions Deep Dive/13. Introduction to Aggregate Functions.mp4
30.63MB
3. Python Functions Deep Dive/14. Introduction to Analytical Functions.mp4
34.68MB
3. Python Functions Deep Dive/16. Quiz Solution.mp4
38.19MB
3. Python Functions Deep Dive/17. Solving the Factorial Problem using Recursion.mp4
55.38MB
3. Python Functions Deep Dive/18. Solving the Fibonacci Problem using Recursion.mp4
62.68MB
3. Python Functions Deep Dive/2. Default Parameters in Functions.mp4
53.96MB
3. Python Functions Deep Dive/20. Quiz Solution.mp4
38.06MB
3. Python Functions Deep Dive/21. Introduction to Classes and Objects.mp4
39.53MB
3. Python Functions Deep Dive/22. Inheritance.mp4
32.49MB
3. Python Functions Deep Dive/23. Encapsulation.mp4
62.2MB
3. Python Functions Deep Dive/24. Polymorphism.mp4
46.25MB
3. Python Functions Deep Dive/26. Quiz Solution.mp4
40.47MB
3. Python Functions Deep Dive/3. Positional Arguments.mp4
32.11MB
3. Python Functions Deep Dive/4. Keyword Arguments.mp4
36.24MB
3. Python Functions Deep Dive/5. Python Modules.mp4
42.7MB
3. Python Functions Deep Dive/7. Quiz Solution.mp4
47.69MB
3. Python Functions Deep Dive/8. Lambda Functions.mp4
53.14MB
3. Python Functions Deep Dive/9. Filter, Map, and Zip Functions.mp4
79.87MB
4. Python for Data Science/1. Introduction to datetime.mp4
37.49MB
4. Python for Data Science/10. Sets for Regular Expressions.mp4
56.13MB
4. Python for Data Science/12. Quiz Solution.mp4
32.82MB
4. Python for Data Science/13. Array Creation using Numpy.mp4
50.91MB
4. Python for Data Science/14. Mathematical Operations using Numpy.mp4
36.44MB
4. Python for Data Science/15. Built-in Functions in Numpy.mp4
39.99MB
4. Python for Data Science/17. Quiz Solution.mp4
57.6MB
4. Python for Data Science/18. Reading Datasets using Pandas.mp4
65.75MB
4. Python for Data Science/19. Plotting Data in Pandas.mp4
35.74MB
4. Python for Data Science/2. The date and time class.mp4
33.55MB
4. Python for Data Science/20. Indexing, Selecting, and Filtering Data using Pandas.mp4
68.92MB
4. Python for Data Science/21. Merging and Concatenating DataFrames.mp4
76.57MB
4. Python for Data Science/22. Lambda, Map, and Apply Functions.mp4
37.2MB
4. Python for Data Science/24. Quiz Solution.mp4
54.71MB
4. Python for Data Science/3. The datetime class.mp4
22.57MB
4. Python for Data Science/4. The timedelta class.mp4
19.36MB
4. Python for Data Science/6. Quiz Solution.mp4
44.07MB
4. Python for Data Science/7. Meta Characters for Regular Expressions.mp4
74.03MB
4. Python for Data Science/8. Built-in Functions for Regular Expressions.mp4
37.57MB
4. Python for Data Science/9. Special Characters for Regular Expressions.mp4
40.92MB
5. Data Cleaning/1. Causes and Impact of Missing Values.mp4
64.37MB
5. Data Cleaning/10. Finding out Outliers from the Data.mp4
63.24MB
5. Data Cleaning/11. Using Winsorization to deal with Outliers.mp4
50.55MB
5. Data Cleaning/12. Deleting and Capping the Outliers.mp4
60.76MB
5. Data Cleaning/13. Dealing with Outliers in a real-world scenario.mp4
50.9MB
5. Data Cleaning/15. Quiz Solution.mp4
56.09MB
5. Data Cleaning/16. Introduction to reindex, set_index, reset_index, and sort_index Functions.mp4
44.7MB
5. Data Cleaning/17. Introduction to Replace and Droplevel Function.mp4
32.98MB
5. Data Cleaning/18. Introduction to Split and Strip Function.mp4
37.82MB
5. Data Cleaning/19. Introduction to Stack, and Unstack Functions.mp4
25.39MB
5. Data Cleaning/2. Types of Missing Values.mp4
61.82MB
5. Data Cleaning/20. Introduction to Melt, Explode, and Squeeze Functions.mp4
41.38MB
5. Data Cleaning/21. Data Cleaning on Big Mart Dataset.mp4
38.3MB
5. Data Cleaning/22. Data Cleaning on Movie Dataset.mp4
37.3MB
5. Data Cleaning/23. Data Cleaning on Melbourne Housing Dataset.mp4
42.14MB
5. Data Cleaning/24. Data Cleaning on Naukri Dataset.mp4
106.25MB
5. Data Cleaning/3. When should we delete the Missing values.mp4
79.62MB
5. Data Cleaning/4. Imputing the Missing Values using the Business Logic.mp4
73.91MB
5. Data Cleaning/5. Imputing Missing Values using MeanMedianMode.mp4
55.96MB
5. Data Cleaning/6. Imputing Missing Values in a real-time scenario.mp4
82.55MB
5. Data Cleaning/8. Quiz Solution.mp4
49.16MB
5. Data Cleaning/9. How Outliers can be harmful for Machine Learning Models.mp4
69.04MB
6. Data Visualizations/1. Univariate Analysis.mp4
57.06MB
6. Data Visualizations/10. Statistical Charts.mp4
38.38MB
6. Data Visualizations/11. Polar Charts.mp4
29.3MB
6. Data Visualizations/12. Subplots.mp4
34.8MB
6. Data Visualizations/13. 3D Charts.mp4
24.57MB
6. Data Visualizations/14. Waffle Charts.mp4
29.36MB
6. Data Visualizations/15. Maps.mp4
30.72MB
6. Data Visualizations/17. Quiz Solution.mp4
48.84MB
6. Data Visualizations/18. Animation with Bubbleplot.mp4
47.79MB
6. Data Visualizations/19. Animation with Facets.mp4
26.71MB
6. Data Visualizations/2. Bivariate Analysis.mp4
45MB
6. Data Visualizations/20. Animation with Scatter Maps.mp4
22.65MB
6. Data Visualizations/21. Animation with Choropleth Maps.mp4
30.58MB
6. Data Visualizations/23. Quiz Solution.mp4
34.58MB
6. Data Visualizations/24. Introduction to Ipywidgets.mp4
38.56MB
6. Data Visualizations/25. Interactive Univariate Analysis.mp4
29.89MB
6. Data Visualizations/26. Interactive Bivariate Analysis.mp4
33.86MB
6. Data Visualizations/27. Interactive Multivariate Analysis.mp4
29.18MB
6. Data Visualizations/29. Quiz Solution.mp4
53.83MB
6. Data Visualizations/3. Multivariate Analysis.mp4
70.84MB
6. Data Visualizations/30. Sunburst Charts.mp4
33.14MB
6. Data Visualizations/31. Parallel Co-ordinate Charts.mp4
22.97MB
6. Data Visualizations/32. Funnel Charts.mp4
39.14MB
6. Data Visualizations/33. Gantt Charts.mp4
25.09MB
6. Data Visualizations/34. Ternary Charts.mp4
20.37MB
6. Data Visualizations/35. Tree Maps.mp4
21.46MB
6. Data Visualizations/36. Network Charts.mp4
39.75MB
6. Data Visualizations/38. Quiz Solution.mp4
38.52MB
6. Data Visualizations/5. Quiz Solution.mp4
47.09MB
6. Data Visualizations/6. Scatter Plots.mp4
45.16MB
6. Data Visualizations/7. Charts with Colorscale.mp4
31.82MB
6. Data Visualizations/8. Bar, Line, and Area Charts.mp4
48.54MB
6. Data Visualizations/9. Facet Grids.mp4
37.93MB
7. Feature Engineering/1. Introduction to Feature Engineering.mp4
60.04MB
7. Feature Engineering/10. Finding the Words, Characters, and Punctuation Count.mp4
36.29MB
7. Feature Engineering/11. Counting Nouns and Verbs in the Text.mp4
31.42MB
7. Feature Engineering/12. Counting Adjectives, Adverb, and Pronouns.mp4
23.71MB
7. Feature Engineering/13. Introduction to Assign and Update Functions.mp4
36.13MB
7. Feature Engineering/14. Introduction to at_time and between_time Functions.mp4
30.23MB
7. Feature Engineering/15. Introduction to nlargest and nsmallest Functions.mp4
35.33MB
7. Feature Engineering/16. Introduction to Expanding Function.mp4
28.43MB
7. Feature Engineering/17. Introduction to Cumulative Functions.mp4
31.11MB
7. Feature Engineering/19. Quiz Solution.mp4
51.21MB
7. Feature Engineering/2. Removing Unnecessary Columns.mp4
56.87MB
7. Feature Engineering/20. Feature Engineering on Employee Data.mp4
57.14MB
7. Feature Engineering/21. Feature Engineering on FIFA Data.mp4
44.76MB
7. Feature Engineering/22. Feature Engineering on Hotel Reviews.mp4
35.06MB
7. Feature Engineering/23. Feature Engineering on Marketing Data.mp4
58.59MB
7. Feature Engineering/24. Feature Engineering on Titanic Data.mp4
49.63MB
7. Feature Engineering/26. Quiz Solution.mp4
64.84MB
7. Feature Engineering/3. Decomposing Time and Date Features.mp4
38.3MB
7. Feature Engineering/4. Decomposing Categorical Features.mp4
38.28MB
7. Feature Engineering/5. Binning Numerical Features.mp4
59.36MB
7. Feature Engineering/6. Aggregating Features.mp4
56.88MB
7. Feature Engineering/7. Introduction to Feature Engineering on Text Data.mp4
33.83MB
7. Feature Engineering/8. Reading and Summarizing the Text.mp4
30.48MB
7. Feature Engineering/9. Finding the Length, Polarity and Subjectivity.mp4
73.01MB
8. Data Processing/1. Types of Encoding Techniques.mp4
60.89MB
8. Data Processing/10. Log transformation.mp4
28.02MB
8. Data Processing/11. BoxCox transformation.mp4
32.52MB
8. Data Processing/13. Train, Test and Validation Split.mp4
44.24MB
8. Data Processing/14. Standardization and Normalization.mp4
39.71MB
8. Data Processing/2. Label Encoding.mp4
33.54MB
8. Data Processing/3. Feature Mapping for Ordinal Variables.mp4
29.02MB
8. Data Processing/4. OneHot Encoding.mp4
34.58MB
8. Data Processing/5. Binary and BaseN Encoding.mp4
33.22MB
8. Data Processing/6. Mean and Frequency Encoding.mp4
22.84MB
8. Data Processing/8. Introduction to Skewness and Normal Distribution.mp4
37.55MB
8. Data Processing/9. Square and Cube Root Transformation.mp4
39.42MB
9. Linear Regression/1. Introduction to Linear Regression.mp4
81.22MB
9. Linear Regression/10. Industry relevance of linear regression.mp4
49.88MB
9. Linear Regression/2. Implementing Linear Regression using Sklearn.mp4
73.45MB
9. Linear Regression/3. Feature Selection using RFECV.mp4
85.91MB
9. Linear Regression/4. Data Transformation with Linear Regression.mp4
57.52MB
9. Linear Regression/5. Applying Cross Validation.mp4
105.62MB
9. Linear Regression/6. Analyzing the performance of Regression models.mp4
108.97MB
9. Linear Regression/7. R2 score and adjuted R2 score intuition.mp4
107.03MB
9. Linear Regression/8. MAE, RMSE, R2 and Adjusted R2 in code.mp4
49MB
9. Linear Regression/9. Applying real time prediction on our model.mp4
107.61MB
友情提示
不会用的朋友看这里 把磁力链接复制到离线下载,或者bt下载软件里即可下载文件,或者直接复制迅雷链接到迅雷里下载! 亲,你造吗?将网页分享给您的基友,下载的人越多速度越快哦!
违规内容投诉邮箱:
[email protected]
概述 838888磁力搜索是一个磁力链接搜索引擎,是学术研究的副产品,用于解决资源过度分散的问题 它通过BitTorrent协议加入DHT网络,实时的自动采集数据,仅存储文件的标题、大小、文件列表、文件标识符(磁力链接)等基础信息 838888磁力搜索不下载任何真实资源,无法判断资源的合法性及真实性,使用838888磁力搜索服务的用户需自行鉴别内容的真伪 838888磁力搜索不上传任何资源,不提供Tracker服务,不提供种子文件的下载,这意味着838888磁力搜索 838888磁力搜索是一个完全合法的系统