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[Udemy] Machine Learning in Python with 5 Machine Learning Projects (04.2021)
magnet:?xt=urn:btih:36c15402f90efe9321553cef2808f4fdf12abdef&dn=[Udemy] Machine Learning in Python with 5 Machine Learning Projects (04.2021)
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文件列表详情
36c15402f90efe9321553cef2808f4fdf12abdef
infohash:
381
文件数量
20.82 GB
文件大小
2021-12-22 11:20
创建日期
2024-6-26 07:35
最后访问
相关分词
Udemy
Machine
Learning
in
Python
with
5
Machine
Learning
Projects
04
2021
1. Python Fundamentals/1. Why should you learn Python.mp4 65.68 MB
1. Python Fundamentals/10. Identity and Membership Operators.mp4 39.22 MB
1. Python Fundamentals/12. Quiz Solution.mp4 34.21 MB
1. Python Fundamentals/13. String Formatting.mp4 51.35 MB
1. Python Fundamentals/14. String Methods.mp4 43.29 MB
1. Python Fundamentals/15. User Input.mp4 41.04 MB
1. Python Fundamentals/17. Quiz Solution.mp4 53.11 MB
1. Python Fundamentals/18. If, elif, and else.mp4 65.9 MB
1. Python Fundamentals/19. For and While.mp4 53.07 MB
1. Python Fundamentals/2. Installing Python and Jupyter Notebook.mp4 33.48 MB
1. Python Fundamentals/20. Break and Continue.mp4 40.72 MB
1. Python Fundamentals/22. Quiz Solution.mp4 49.04 MB
1. Python Fundamentals/3. Naming Convention for Variables.mp4 102.24 MB
1. Python Fundamentals/4. Built in Data Types and Type Casting.mp4 119.86 MB
1. Python Fundamentals/5. Scope of Variables.mp4 77.16 MB
1. Python Fundamentals/7. Quiz Solution.mp4 46.52 MB
1. Python Fundamentals/8. Arithmetic and Assignment Operators.mp4 78.04 MB
1. Python Fundamentals/9. Comparison, Logical, and Bitwise Operators.mp4 62.41 MB
10. Logistic Regression/1. Introduction to Logistic Regression.mp4 106.4 MB
10. Logistic Regression/10. Industry Relevance of Logistic Regression.mp4 59.89 MB
10. Logistic Regression/2. Implementing Logistic Regression using Sklearn.mp4 87.01 MB
10. Logistic Regression/3. Feature Selection using RFECV.mp4 42.15 MB
10. Logistic Regression/4. Hyperparameter tuning using Grid search.mp4 58.75 MB
10. Logistic Regression/5. Applying Cross Validation.mp4 56.73 MB
10. Logistic Regression/6. How to analyze performance of a classification model.mp4 146.18 MB
10. Logistic Regression/7. Using accuracy score to analyze the performance of model.mp4 55.54 MB
10. Logistic Regression/8. Using ROC-AUC score to analyze the performance of model.mp4 147.63 MB
10. Logistic Regression/9. Real time prediction using logistic regression.mp4 74.65 MB
11. Introduction to KNN, SVM, Naive Bayes/1. Introduction to Support Vector machines.mp4 108.17 MB
11. Introduction to KNN, SVM, Naive Bayes/2. The kermel trick for support vector machine.mp4 70.38 MB
11. Introduction to KNN, SVM, Naive Bayes/3. Implementing support vector machine using sklearn.mp4 67.43 MB
11. Introduction to KNN, SVM, Naive Bayes/4. Introduction to K nearest neighbors.mp4 104.32 MB
11. Introduction to KNN, SVM, Naive Bayes/5. Implementing KNN using Sklearn.mp4 33.23 MB
11. Introduction to KNN, SVM, Naive Bayes/6. Introduction to Naive Bayes.mp4 174.72 MB
11. Introduction to KNN, SVM, Naive Bayes/7. Implementing Naive Bayes using sklearn.mp4 61.96 MB
11. Introduction to KNN, SVM, Naive Bayes/8. When should we apply SVM, KNN and Naive bayes.mp4 69.77 MB
12. Tree Based Models/1. Intuition for decision trees.mp4 81.99 MB
12. Tree Based Models/2. Attribute selection method- Gini Index and Entropy.mp4 218.66 MB
12. Tree Based Models/3. Advantages and Issues with Decision trees.mp4 53.37 MB
12. Tree Based Models/4. Implementing Decision tree using Sklearn.mp4 35.8 MB
12. Tree Based Models/5. Understanding the concept of Bagging.mp4 65.99 MB
12. Tree Based Models/6. Introduction to Random forest.mp4 68.09 MB
12. Tree Based Models/7. Understanding the parameters of Random forest.mp4 53.66 MB
12. Tree Based Models/8. Implementing random forest using Sklearn.mp4 47.88 MB
13. Boosting Models/1. Understading the concept of boosting.mp4 57.14 MB
13. Boosting Models/2. Intuition for Adaboost and Gradient Boosting.mp4 153.3 MB
13. Boosting Models/3. Implementing AdaBoost using sklearn.mp4 90.82 MB
13. Boosting Models/4. Implementing Gradient Boosting using sklearn.mp4 66.93 MB
13. Boosting Models/5. Getting High level intuition for XGBoost.mp4 41.07 MB
13. Boosting Models/6. Implementing XGBoost using sklearn.mp4 65.14 MB
13. Boosting Models/7. Introudction to Ensembling techniques.mp4 134.02 MB
14. Imbalanced Machine Learning/1. Why Imbalanced Data needs extra attention.mp4 53.62 MB
14. Imbalanced Machine Learning/10. Implementing Synthetic Sampling using Imblearn.mp4 57.45 MB
14. Imbalanced Machine Learning/11. Implementing Neighbors based Sampling using Imblearn.mp4 64.02 MB
14. Imbalanced Machine Learning/12. Combination of Oversampling and Under sampling.mp4 55.76 MB
14. Imbalanced Machine Learning/13. Implementing Ensemble Models for Imbalanced Data.mp4 54.88 MB
14. Imbalanced Machine Learning/14. Introduction to XG Boost for Imbalanced Data.mp4 43.54 MB
14. Imbalanced Machine Learning/15. Comparing the Results.mp4 41.5 MB
14. Imbalanced Machine Learning/2. Using Resampling Techniques to Balance the Data.mp4 70.55 MB
14. Imbalanced Machine Learning/3. Solving a Real World Problem.mp4 56.98 MB
14. Imbalanced Machine Learning/4. Preparing the Data for Predictive Modelling.mp4 57.93 MB
14. Imbalanced Machine Learning/5. Applying Logistic Regression using Sklearn.mp4 71.14 MB
14. Imbalanced Machine Learning/6. Applying Random Forest using Sklearn.mp4 42.65 MB
14. Imbalanced Machine Learning/8. Implementing Random Over Sampling using Imblearn.mp4 54.41 MB
14. Imbalanced Machine Learning/9. Implementing Random Under Sampling using Imblearn.mp4 57.54 MB
15. Introduction to Clustering Analysis/1. Introduction to Clustering.mp4 57.84 MB
15. Introduction to Clustering Analysis/10. Clustering Multiple Dimensions.mp4 50.01 MB
15. Introduction to Clustering Analysis/12. Introduction to Hierarchal Clustering.mp4 88.49 MB
15. Introduction to Clustering Analysis/13. Introduction to Dendrograms.mp4 41.78 MB
15. Introduction to Clustering Analysis/14. Implementing Hierarchial Clustering.mp4 52.35 MB
15. Introduction to Clustering Analysis/15. Introduction to DBSCAN Clustering.mp4 52.38 MB
15. Introduction to Clustering Analysis/16. Implementing DBSCAN Clustering.mp4 47.87 MB
15. Introduction to Clustering Analysis/2. Types of Clustering.mp4 65.18 MB
15. Introduction to Clustering Analysis/3. Applications of Clustering.mp4 55.95 MB
15. Introduction to Clustering Analysis/5. Using the Elbow Method for Choosing the Best Value for K.mp4 67.06 MB
15. Introduction to Clustering Analysis/6. Introduction to K Means Clustering.mp4 49.29 MB
15. Introduction to Clustering Analysis/7. Solving a Real World Problem.mp4 71 MB
15. Introduction to Clustering Analysis/8. Implementing K Means on the Mall Dataset.mp4 71.57 MB
15. Introduction to Clustering Analysis/9. Using Silhouette Score to analyze the clusters.mp4 96.34 MB
16. Dimensionality Reduction/1. Why High Dimensional Datasets are a Problem.mp4 79.22 MB
16. Dimensionality Reduction/11. Introduction to Recursive Feature Selection.mp4 56.62 MB
16. Dimensionality Reduction/12. Implementing Recursive Feature Selection.mp4 50.92 MB
16. Dimensionality Reduction/13. Introduction the Boruta Algorithm.mp4 52.48 MB
16. Dimensionality Reduction/14. Implementing the Boruta Algorithm.mp4 43.2 MB
16. Dimensionality Reduction/16. Introduction to Principal Component Analysis.mp4 73.79 MB
16. Dimensionality Reduction/17. Implementing PCA.mp4 55.52 MB
16. Dimensionality Reduction/18. Introduction to t-SNE.mp4 81.27 MB
16. Dimensionality Reduction/19. Implementing t-SNE.mp4 36.11 MB
16. Dimensionality Reduction/2. Methods to solve the problem of High Dimensionality.mp4 57.16 MB
16. Dimensionality Reduction/20. Introduction to Linear Discriminant Analysis.mp4 48.9 MB
16. Dimensionality Reduction/21. Implementing LDA.mp4 36.74 MB
16. Dimensionality Reduction/22. Difference between PCA, t-SNE, and LDA.mp4 64.79 MB
16. Dimensionality Reduction/3. Solving a Real World Problem.mp4 98.82 MB
16. Dimensionality Reduction/5. Introduction to Correlation using Heatmap.mp4 71.4 MB
16. Dimensionality Reduction/6. Removing Highly Correlated Columns using Correlation.mp4 48.87 MB
16. Dimensionality Reduction/8. Introduction to Variance Inflation Filtering.mp4 48.66 MB
16. Dimensionality Reduction/9. Implementing VIF using statsmodel.mp4 47.84 MB
17. Recommendation Engines/1. Introduction to Recommender systems.mp4 40.53 MB
17. Recommendation Engines/11. Quiz Solution.mp4 48.5 MB
17. Recommendation Engines/12. Introduction to Collaborative Filtering.mp4 80.86 MB
17. Recommendation Engines/13. Preprocessing the Data for Collaborative Filtering.mp4 72.39 MB
17. Recommendation Engines/14. Implementation of User Based Collaborative Filtering.mp4 62.15 MB
17. Recommendation Engines/15. Interpreting the Results obtained from User Based Filtering.mp4 63.59 MB
17. Recommendation Engines/16. Implementation of Item Based Collaborative Filtering.mp4 63.55 MB
17. Recommendation Engines/18. Quiz Solution.mp4 55.62 MB
17. Recommendation Engines/19. Introduction to SVD.mp4 112.02 MB
17. Recommendation Engines/2. What are it's Use Cases.mp4 45.05 MB
17. Recommendation Engines/20. Implementing SVD using Surprise.mp4 40.63 MB
17. Recommendation Engines/21. Interpreting Results Obtained from SVD.mp4 46.01 MB
17. Recommendation Engines/22. Comparing Content, and Collaborative Based Filtering.mp4 61.99 MB
17. Recommendation Engines/24. Quiz Solution.mp4 47.94 MB
17. Recommendation Engines/25. Case Study for Netflix.mp4 56.38 MB
17. Recommendation Engines/26. Case Study for Youtube.mp4 58.14 MB
17. Recommendation Engines/3. Types of Recommender Systems.mp4 56.54 MB
17. Recommendation Engines/4. Evaluating Recommender Systems.mp4 53.15 MB
17. Recommendation Engines/5. Introduction to Content Based Filtering.mp4 59 MB
17. Recommendation Engines/6. Preprocessing the Data for Content Based Filtering.mp4 76.67 MB
17. Recommendation Engines/7. Filtering Movies Based on Genres.mp4 58.73 MB
17. Recommendation Engines/8. Introduction to Transactional Encoder.mp4 63.39 MB
17. Recommendation Engines/9. Recommending Similar Movies to Watch.mp4 56.31 MB
18. Time Series Forecasting/1. What is a Time Series Data.mp4 34.91 MB
18. Time Series Forecasting/10. Time Series Decomposition.mp4 89.93 MB
18. Time Series Forecasting/11. Splitting Time Series Data.mp4 63.5 MB
18. Time Series Forecasting/13. Basic Forecasting Techniques.mp4 55.48 MB
18. Time Series Forecasting/14. Metrics for Time series Forecasting.mp4 78.7 MB
18. Time Series Forecasting/15. Simple Moving Averages.mp4 50.11 MB
18. Time Series Forecasting/16. Simple Exponential Smoothing.mp4 66.62 MB
18. Time Series Forecasting/17. Holt and Holt Winter Exponential Smoothing.mp4 73.13 MB
18. Time Series Forecasting/19. Introduction to Auto Regressive Models.mp4 34.71 MB
18. Time Series Forecasting/2. Types of Forecasting.mp4 45.3 MB
18. Time Series Forecasting/20. Checking for Stationarity Part 1.mp4 65 MB
18. Time Series Forecasting/21. Checking for Stationarity using Statistical Methods Part 2.mp4 75.44 MB
18. Time Series Forecasting/22. Checking for Stationary Implementation.mp4 38.1 MB
18. Time Series Forecasting/23. Converting Non-Stationary Series into Stationary.mp4 48.1 MB
18. Time Series Forecasting/24. Converting Non-Stationary Series into Stationary Implementation.mp4 48.17 MB
18. Time Series Forecasting/25. Auto Correlation and Partial Correlation.mp4 76.85 MB
18. Time Series Forecasting/26. Auto Correlation and Partial Correlation Implementation.mp4 38.48 MB
18. Time Series Forecasting/27. The Simple Auto Regressive Model.mp4 63.42 MB
18. Time Series Forecasting/28. The Simple Auto Regressive Model Implementation.mp4 64.98 MB
18. Time Series Forecasting/29. Moving Average Model.mp4 35.3 MB
18. Time Series Forecasting/3. Regression Vs Time Series.mp4 82.95 MB
18. Time Series Forecasting/30. Moving Average Model Implementation.mp4 23.23 MB
18. Time Series Forecasting/32. Understanding ARMA Model.mp4 56.79 MB
18. Time Series Forecasting/33. Implementing ARMA Model.mp4 48.21 MB
18. Time Series Forecasting/34. Understanding ARIMA Model.mp4 55.87 MB
18. Time Series Forecasting/35. Implementing ARIMA Model.mp4 33.2 MB
18. Time Series Forecasting/36. Understanding SARIMA Model.mp4 69.94 MB
18. Time Series Forecasting/37. Implementing SARIMA Model.mp4 38.13 MB
18. Time Series Forecasting/39. Understanding ARIMAX Model.mp4 66.51 MB
18. Time Series Forecasting/4. Applications of Time Series.mp4 47.29 MB
18. Time Series Forecasting/40. Implementing ARIMAX Model.mp4 44.76 MB
18. Time Series Forecasting/41. Understanding SARIMAX Model.mp4 43.84 MB
18. Time Series Forecasting/42. Implementing SARIMAX Model.mp4 59.96 MB
18. Time Series Forecasting/44. How to Choose the Right Model.mp4 35.14 MB
18. Time Series Forecasting/45. Choosing the Right for Model Smaller Datasets.mp4 52.3 MB
18. Time Series Forecasting/46. Choosing the Right Model for Larger Datasets.mp4 36.31 MB
18. Time Series Forecasting/47. Best Practices while Choosing a Time series Model..mp4 43.02 MB
18. Time Series Forecasting/49. Why do we Evaluate Performance.mp4 31.77 MB
18. Time Series Forecasting/5. Components of Time Series.mp4 51.96 MB
18. Time Series Forecasting/50. Mean Forecast Error.mp4 52.91 MB
18. Time Series Forecasting/51. Mean Absolute Error.mp4 35.56 MB
18. Time Series Forecasting/52. Mean Absolute Percentage Error.mp4 29.76 MB
18. Time Series Forecasting/53. Root Mean Squared Error.mp4 29.34 MB
18. Time Series Forecasting/7. Getting Time Series data.mp4 71.08 MB
18. Time Series Forecasting/8. Handling Missing Values.mp4 116.47 MB
18. Time Series Forecasting/9. Handling Outlier Values.mp4 64.43 MB
19. Employee Promotion Prediction/1. Setting up the Environment.mp4 41.71 MB
19. Employee Promotion Prediction/10. Feature Engineering.mp4 50.43 MB
19. Employee Promotion Prediction/11. Categorical Encoding.mp4 37.44 MB
19. Employee Promotion Prediction/12. Data Processing.mp4 67.65 MB
19. Employee Promotion Prediction/13. Feature Scaling.mp4 42.28 MB
19. Employee Promotion Prediction/14. Predictive Modelling.mp4 44.65 MB
19. Employee Promotion Prediction/15. Performance Analysis.mp4 77.16 MB
19. Employee Promotion Prediction/16. Improvements Possible.mp4 41.87 MB
19. Employee Promotion Prediction/17. Major Takeaways from the Project.mp4 28.99 MB
19. Employee Promotion Prediction/2. Understanding the Dataset.mp4 95.88 MB
19. Employee Promotion Prediction/3. Understanding the Problem Statement.mp4 59.78 MB
19. Employee Promotion Prediction/4. Performing Descriptive Statistics.mp4 61.68 MB
19. Employee Promotion Prediction/5. Missing Values Treatment.mp4 38.66 MB
19. Employee Promotion Prediction/6. Outlier Values Treatment.mp4 42.49 MB
19. Employee Promotion Prediction/7. Univariate Analysis.mp4 53.13 MB
19. Employee Promotion Prediction/8. Bivariate Analysis.mp4 37.16 MB
19. Employee Promotion Prediction/9. Multivariate Analysis.mp4 39.94 MB
2. Python for Data Analysis/1. Differences between Lists and Tuples.mp4 48.66 MB
2. Python for Data Analysis/11. Quiz Solution.mp4 38.27 MB
2. Python for Data Analysis/12. Introduction to Stacks and Queues.mp4 48.69 MB
2. Python for Data Analysis/13. Implementing Stacks and Queues using Lists.mp4 36.5 MB
2. Python for Data Analysis/14. Implementing Stacks and Queues using Deque.mp4 41.6 MB
2. Python for Data Analysis/16. Quiz Solution.mp4 39.51 MB
2. Python for Data Analysis/17. Time Complexity.mp4 120.13 MB
2. Python for Data Analysis/18. Linear Search.mp4 95.52 MB
2. Python for Data Analysis/19. Binary Search.mp4 109.54 MB
2. Python for Data Analysis/2. Operations on Lists.mp4 44.4 MB
2. Python for Data Analysis/20. Bubble Sort.mp4 75.55 MB
2. Python for Data Analysis/21. Insertion and Selection Sort.mp4 120 MB
2. Python for Data Analysis/22. Merge Sort.mp4 115.44 MB
2. Python for Data Analysis/24. Quiz Solution.mp4 73.24 MB
2. Python for Data Analysis/3. Operations on Tuples.mp4 27.44 MB
2. Python for Data Analysis/5. Quiz Solution.mp4 37.09 MB
2. Python for Data Analysis/6. Introduction to Dictionaries.mp4 66.83 MB
2. Python for Data Analysis/7. Nested Dictionaries.mp4 60.55 MB
2. Python for Data Analysis/8. Introduction to Sets.mp4 75.49 MB
2. Python for Data Analysis/9. Set Operations.mp4 58.59 MB
20. Predicting Health Expense of Customers/1. Setting up the Environment.mp4 50.12 MB
20. Predicting Health Expense of Customers/10. Applying Gradient Boosting Model.mp4 70.38 MB
20. Predicting Health Expense of Customers/11. Creating Ensembles of Models.mp4 57.07 MB
20. Predicting Health Expense of Customers/12. Comparing Performance of these Models.mp4 36.54 MB
20. Predicting Health Expense of Customers/13. More things to Try.mp4 48.69 MB
20. Predicting Health Expense of Customers/14. Major Takeaways from the Project.mp4 57.64 MB
20. Predicting Health Expense of Customers/2. Understanding the Dataset.mp4 104.05 MB
20. Predicting Health Expense of Customers/3. Understanding the Problem Statement.mp4 61.8 MB
20. Predicting Health Expense of Customers/4. Performing Univariate Analysis.mp4 89.75 MB
20. Predicting Health Expense of Customers/5. Performing Bivariate Analysis.mp4 71.46 MB
20. Predicting Health Expense of Customers/6. Performing Multivariate Analysis.mp4 85.97 MB
20. Predicting Health Expense of Customers/7. Preparing the data for Modelling.mp4 90.86 MB
20. Predicting Health Expense of Customers/8. Applying Linear Regression Model.mp4 128.08 MB
20. Predicting Health Expense of Customers/9. Applying Random Forest Model.mp4 54.39 MB
21. Determining Whether a Person should be Granted Loan/1. Understanding the Problem Statement.mp4 45.49 MB
21. Determining Whether a Person should be Granted Loan/10. Applying Logistic Regression.mp4 52.39 MB
21. Determining Whether a Person should be Granted Loan/11. Applying Gradient Boosting.mp4 38.62 MB
21. Determining Whether a Person should be Granted Loan/12. Summary.mp4 44.17 MB
21. Determining Whether a Person should be Granted Loan/2. Setting up the Environment.mp4 68.6 MB
21. Determining Whether a Person should be Granted Loan/3. Understanding the Dataset.mp4 41.13 MB
21. Determining Whether a Person should be Granted Loan/4. Performing Descriptive Statistics.mp4 75.32 MB
21. Determining Whether a Person should be Granted Loan/5. Data Cleaning.mp4 66.97 MB
21. Determining Whether a Person should be Granted Loan/6. Univariate Data Visualizations.mp4 65.17 MB
21. Determining Whether a Person should be Granted Loan/7. Bivariate Data Analysis.mp4 70.21 MB
21. Determining Whether a Person should be Granted Loan/8. Preparing the Data for Modelling.mp4 42.83 MB
21. Determining Whether a Person should be Granted Loan/9. Applying Resampling.mp4 56.96 MB
22. Optimizing Agricultural Production/1. Setting up the Environment.mp4 46.43 MB
22. Optimizing Agricultural Production/10. Summarizing the Key-Points.mp4 40.45 MB
22. Optimizing Agricultural Production/2. Understanding the Dataset.mp4 55.18 MB
22. Optimizing Agricultural Production/3. Understanding the Problem Statement.mp4 35.4 MB
22. Optimizing Agricultural Production/4. Performing Descriptive Statistics.mp4 73.57 MB
22. Optimizing Agricultural Production/5. Analyzing Agricultural Conditions.mp4 39.18 MB
22. Optimizing Agricultural Production/6. Clustering Similar Crops.mp4 63.62 MB
22. Optimizing Agricultural Production/7. Visualizing the Hidden Patterns.mp4 27.79 MB
22. Optimizing Agricultural Production/8. Predictive Modelling.mp4 40.38 MB
22. Optimizing Agricultural Production/9. Real Time Predictions.mp4 27.66 MB
3. Python Functions Deep Dive/1. Introduction to Functions.mp4 40.22 MB
3. Python Functions Deep Dive/10. List, set, and Dictionary Comprehensions.mp4 54.58 MB
3. Python Functions Deep Dive/12. Quiz Solution.mp4 40.26 MB
3. Python Functions Deep Dive/13. Introduction to Aggregate Functions.mp4 30.63 MB
3. Python Functions Deep Dive/14. Introduction to Analytical Functions.mp4 34.68 MB
3. Python Functions Deep Dive/16. Quiz Solution.mp4 38.19 MB
3. Python Functions Deep Dive/17. Solving the Factorial Problem using Recursion.mp4 55.38 MB
3. Python Functions Deep Dive/18. Solving the Fibonacci Problem using Recursion.mp4 62.68 MB
3. Python Functions Deep Dive/2. Default Parameters in Functions.mp4 53.96 MB
3. Python Functions Deep Dive/20. Quiz Solution.mp4 38.06 MB
3. Python Functions Deep Dive/21. Introduction to Classes and Objects.mp4 39.53 MB
3. Python Functions Deep Dive/22. Inheritance.mp4 32.49 MB
3. Python Functions Deep Dive/23. Encapsulation.mp4 62.2 MB
3. Python Functions Deep Dive/24. Polymorphism.mp4 46.25 MB
3. Python Functions Deep Dive/26. Quiz Solution.mp4 40.47 MB
3. Python Functions Deep Dive/3. Positional Arguments.mp4 32.11 MB
3. Python Functions Deep Dive/4. Keyword Arguments.mp4 36.24 MB
3. Python Functions Deep Dive/5. Python Modules.mp4 42.7 MB
3. Python Functions Deep Dive/7. Quiz Solution.mp4 47.69 MB
3. Python Functions Deep Dive/8. Lambda Functions.mp4 53.14 MB
3. Python Functions Deep Dive/9. Filter, Map, and Zip Functions.mp4 79.87 MB
4. Python for Data Science/1. Introduction to datetime.mp4 37.49 MB
4. Python for Data Science/10. Sets for Regular Expressions.mp4 56.13 MB
4. Python for Data Science/12. Quiz Solution.mp4 32.82 MB
4. Python for Data Science/13. Array Creation using Numpy.mp4 50.91 MB
4. Python for Data Science/14. Mathematical Operations using Numpy.mp4 36.44 MB
4. Python for Data Science/15. Built-in Functions in Numpy.mp4 39.99 MB
4. Python for Data Science/17. Quiz Solution.mp4 57.6 MB
4. Python for Data Science/18. Reading Datasets using Pandas.mp4 65.75 MB
4. Python for Data Science/19. Plotting Data in Pandas.mp4 35.74 MB
4. Python for Data Science/2. The date and time class.mp4 33.55 MB
4. Python for Data Science/20. Indexing, Selecting, and Filtering Data using Pandas.mp4 68.92 MB
4. Python for Data Science/21. Merging and Concatenating DataFrames.mp4 76.57 MB
4. Python for Data Science/22. Lambda, Map, and Apply Functions.mp4 37.2 MB
4. Python for Data Science/24. Quiz Solution.mp4 54.71 MB
4. Python for Data Science/3. The datetime class.mp4 22.57 MB
4. Python for Data Science/4. The timedelta class.mp4 19.36 MB
4. Python for Data Science/6. Quiz Solution.mp4 44.07 MB
4. Python for Data Science/7. Meta Characters for Regular Expressions.mp4 74.03 MB
4. Python for Data Science/8. Built-in Functions for Regular Expressions.mp4 37.57 MB
4. Python for Data Science/9. Special Characters for Regular Expressions.mp4 40.92 MB
5. Data Cleaning/1. Causes and Impact of Missing Values.mp4 64.37 MB
5. Data Cleaning/10. Finding out Outliers from the Data.mp4 63.24 MB
5. Data Cleaning/11. Using Winsorization to deal with Outliers.mp4 50.55 MB
5. Data Cleaning/12. Deleting and Capping the Outliers.mp4 60.76 MB
5. Data Cleaning/13. Dealing with Outliers in a real-world scenario.mp4 50.9 MB
5. Data Cleaning/15. Quiz Solution.mp4 56.09 MB
5. Data Cleaning/16. Introduction to reindex, set_index, reset_index, and sort_index Functions.mp4 44.7 MB
5. Data Cleaning/17. Introduction to Replace and Droplevel Function.mp4 32.98 MB
5. Data Cleaning/18. Introduction to Split and Strip Function.mp4 37.82 MB
5. Data Cleaning/19. Introduction to Stack, and Unstack Functions.mp4 25.39 MB
5. Data Cleaning/2. Types of Missing Values.mp4 61.82 MB
5. Data Cleaning/20. Introduction to Melt, Explode, and Squeeze Functions.mp4 41.38 MB
5. Data Cleaning/21. Data Cleaning on Big Mart Dataset.mp4 38.3 MB
5. Data Cleaning/22. Data Cleaning on Movie Dataset.mp4 37.3 MB
5. Data Cleaning/23. Data Cleaning on Melbourne Housing Dataset.mp4 42.14 MB
5. Data Cleaning/24. Data Cleaning on Naukri Dataset.mp4 106.25 MB
5. Data Cleaning/3. When should we delete the Missing values.mp4 79.62 MB
5. Data Cleaning/4. Imputing the Missing Values using the Business Logic.mp4 73.91 MB
5. Data Cleaning/5. Imputing Missing Values using MeanMedianMode.mp4 55.96 MB
5. Data Cleaning/6. Imputing Missing Values in a real-time scenario.mp4 82.55 MB
5. Data Cleaning/8. Quiz Solution.mp4 49.16 MB
5. Data Cleaning/9. How Outliers can be harmful for Machine Learning Models.mp4 69.04 MB
6. Data Visualizations/1. Univariate Analysis.mp4 57.06 MB
6. Data Visualizations/10. Statistical Charts.mp4 38.38 MB
6. Data Visualizations/11. Polar Charts.mp4 29.3 MB
6. Data Visualizations/12. Subplots.mp4 34.8 MB
6. Data Visualizations/13. 3D Charts.mp4 24.57 MB
6. Data Visualizations/14. Waffle Charts.mp4 29.36 MB
6. Data Visualizations/15. Maps.mp4 30.72 MB
6. Data Visualizations/17. Quiz Solution.mp4 48.84 MB
6. Data Visualizations/18. Animation with Bubbleplot.mp4 47.79 MB
6. Data Visualizations/19. Animation with Facets.mp4 26.71 MB
6. Data Visualizations/2. Bivariate Analysis.mp4 45 MB
6. Data Visualizations/20. Animation with Scatter Maps.mp4 22.65 MB
6. Data Visualizations/21. Animation with Choropleth Maps.mp4 30.58 MB
6. Data Visualizations/23. Quiz Solution.mp4 34.58 MB
6. Data Visualizations/24. Introduction to Ipywidgets.mp4 38.56 MB
6. Data Visualizations/25. Interactive Univariate Analysis.mp4 29.89 MB
6. Data Visualizations/26. Interactive Bivariate Analysis.mp4 33.86 MB
6. Data Visualizations/27. Interactive Multivariate Analysis.mp4 29.18 MB
6. Data Visualizations/29. Quiz Solution.mp4 53.83 MB
6. Data Visualizations/3. Multivariate Analysis.mp4 70.84 MB
6. Data Visualizations/30. Sunburst Charts.mp4 33.14 MB
6. Data Visualizations/31. Parallel Co-ordinate Charts.mp4 22.97 MB
6. Data Visualizations/32. Funnel Charts.mp4 39.14 MB
6. Data Visualizations/33. Gantt Charts.mp4 25.09 MB
6. Data Visualizations/34. Ternary Charts.mp4 20.37 MB
6. Data Visualizations/35. Tree Maps.mp4 21.46 MB
6. Data Visualizations/36. Network Charts.mp4 39.75 MB
6. Data Visualizations/38. Quiz Solution.mp4 38.52 MB
6. Data Visualizations/5. Quiz Solution.mp4 47.09 MB
6. Data Visualizations/6. Scatter Plots.mp4 45.16 MB
6. Data Visualizations/7. Charts with Colorscale.mp4 31.82 MB
6. Data Visualizations/8. Bar, Line, and Area Charts.mp4 48.54 MB
6. Data Visualizations/9. Facet Grids.mp4 37.93 MB
7. Feature Engineering/1. Introduction to Feature Engineering.mp4 60.04 MB
7. Feature Engineering/10. Finding the Words, Characters, and Punctuation Count.mp4 36.29 MB
7. Feature Engineering/11. Counting Nouns and Verbs in the Text.mp4 31.42 MB
7. Feature Engineering/12. Counting Adjectives, Adverb, and Pronouns.mp4 23.71 MB
7. Feature Engineering/13. Introduction to Assign and Update Functions.mp4 36.13 MB
7. Feature Engineering/14. Introduction to at_time and between_time Functions.mp4 30.23 MB
7. Feature Engineering/15. Introduction to nlargest and nsmallest Functions.mp4 35.33 MB
7. Feature Engineering/16. Introduction to Expanding Function.mp4 28.43 MB
7. Feature Engineering/17. Introduction to Cumulative Functions.mp4 31.11 MB
7. Feature Engineering/19. Quiz Solution.mp4 51.21 MB
7. Feature Engineering/2. Removing Unnecessary Columns.mp4 56.87 MB
7. Feature Engineering/20. Feature Engineering on Employee Data.mp4 57.14 MB
7. Feature Engineering/21. Feature Engineering on FIFA Data.mp4 44.76 MB
7. Feature Engineering/22. Feature Engineering on Hotel Reviews.mp4 35.06 MB
7. Feature Engineering/23. Feature Engineering on Marketing Data.mp4 58.59 MB
7. Feature Engineering/24. Feature Engineering on Titanic Data.mp4 49.63 MB
7. Feature Engineering/26. Quiz Solution.mp4 64.84 MB
7. Feature Engineering/3. Decomposing Time and Date Features.mp4 38.3 MB
7. Feature Engineering/4. Decomposing Categorical Features.mp4 38.28 MB
7. Feature Engineering/5. Binning Numerical Features.mp4 59.36 MB
7. Feature Engineering/6. Aggregating Features.mp4 56.88 MB
7. Feature Engineering/7. Introduction to Feature Engineering on Text Data.mp4 33.83 MB
7. Feature Engineering/8. Reading and Summarizing the Text.mp4 30.48 MB
7. Feature Engineering/9. Finding the Length, Polarity and Subjectivity.mp4 73.01 MB
8. Data Processing/1. Types of Encoding Techniques.mp4 60.89 MB
8. Data Processing/10. Log transformation.mp4 28.02 MB
8. Data Processing/11. BoxCox transformation.mp4 32.52 MB
8. Data Processing/13. Train, Test and Validation Split.mp4 44.24 MB
8. Data Processing/14. Standardization and Normalization.mp4 39.71 MB
8. Data Processing/2. Label Encoding.mp4 33.54 MB
8. Data Processing/3. Feature Mapping for Ordinal Variables.mp4 29.02 MB
8. Data Processing/4. OneHot Encoding.mp4 34.58 MB
8. Data Processing/5. Binary and BaseN Encoding.mp4 33.22 MB
8. Data Processing/6. Mean and Frequency Encoding.mp4 22.84 MB
8. Data Processing/8. Introduction to Skewness and Normal Distribution.mp4 37.55 MB
8. Data Processing/9. Square and Cube Root Transformation.mp4 39.42 MB
9. Linear Regression/1. Introduction to Linear Regression.mp4 81.22 MB
9. Linear Regression/10. Industry relevance of linear regression.mp4 49.88 MB
9. Linear Regression/2. Implementing Linear Regression using Sklearn.mp4 73.45 MB
9. Linear Regression/3. Feature Selection using RFECV.mp4 85.91 MB
9. Linear Regression/4. Data Transformation with Linear Regression.mp4 57.52 MB
9. Linear Regression/5. Applying Cross Validation.mp4 105.62 MB
9. Linear Regression/6. Analyzing the performance of Regression models.mp4 108.97 MB
9. Linear Regression/7. R2 score and adjuted R2 score intuition.mp4 107.03 MB
9. Linear Regression/8. MAE, RMSE, R2 and Adjusted R2 in code.mp4 49 MB
9. Linear Regression/9. Applying real time prediction on our model.mp4 107.61 MB
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