[Udemy] Machine Learning in Python with 5 Machine Learning Projects (04.2021)

磁力链接/BT种子名称

[Udemy] Machine Learning in Python with 5 Machine Learning Projects (04.2021)

磁力链接/BT种子简介

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收录时间：2022-01-12
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文件列表

  • 12. Tree Based Models/2. Attribute selection method- Gini Index and Entropy.mp4 229.3 MB
  • 11. Introduction to KNN, SVM, Naive Bayes/6. Introduction to Naive Bayes.mp4 183.2 MB
  • 13. Boosting Models/2. Intuition for Adaboost and Gradient Boosting.mp4 160.7 MB
  • 10. Logistic Regression/8. Using ROC-AUC score to analyze the performance of model.mp4 154.8 MB
  • 10. Logistic Regression/6. How to analyze performance of a classification model.mp4 153.3 MB
  • 13. Boosting Models/7. Introudction to Ensembling techniques.mp4 140.5 MB
  • 20. Predicting Health Expense of Customers/8. Applying Linear Regression Model.mp4 134.3 MB
  • 2. Python for Data Analysis/17. Time Complexity.mp4 126.0 MB
  • 2. Python for Data Analysis/21. Insertion and Selection Sort.mp4 125.8 MB
  • 1. Python Fundamentals/4. Built in Data Types and Type Casting.mp4 125.7 MB
  • 18. Time Series Forecasting/8. Handling Missing Values.mp4 122.1 MB
  • 2. Python for Data Analysis/22. Merge Sort.mp4 121.0 MB
  • 17. Recommendation Engines/19. Introduction to SVD.mp4 117.5 MB
  • 2. Python for Data Analysis/19. Binary Search.mp4 114.9 MB
  • 9. Linear Regression/6. Analyzing the performance of Regression models.mp4 114.3 MB
  • 11. Introduction to KNN, SVM, Naive Bayes/1. Introduction to Support Vector machines.mp4 113.4 MB
  • 9. Linear Regression/9. Applying real time prediction on our model.mp4 112.8 MB
  • 9. Linear Regression/7. R2 score and adjuted R2 score intuition.mp4 112.2 MB
  • 10. Logistic Regression/1. Introduction to Logistic Regression.mp4 111.6 MB
  • 5. Data Cleaning/24. Data Cleaning on Naukri Dataset.mp4 111.4 MB
  • 9. Linear Regression/5. Applying Cross Validation.mp4 110.8 MB
  • 11. Introduction to KNN, SVM, Naive Bayes/4. Introduction to K nearest neighbors.mp4 109.4 MB
  • 20. Predicting Health Expense of Customers/2. Understanding the Dataset.mp4 109.1 MB
  • 1. Python Fundamentals/3. Naming Convention for Variables.mp4 107.2 MB
  • 16. Dimensionality Reduction/3. Solving a Real World Problem.mp4 103.6 MB
  • 15. Introduction to Clustering Analysis/9. Using Silhouette Score to analyze the clusters.mp4 101.0 MB
  • 19. Employee Promotion Prediction/2. Understanding the Dataset.mp4 100.5 MB
  • 2. Python for Data Analysis/18. Linear Search.mp4 100.2 MB
  • 20. Predicting Health Expense of Customers/7. Preparing the data for Modelling.mp4 95.3 MB
  • 13. Boosting Models/3. Implementing AdaBoost using sklearn.mp4 95.2 MB
  • 18. Time Series Forecasting/10. Time Series Decomposition.mp4 94.3 MB
  • 20. Predicting Health Expense of Customers/4. Performing Univariate Analysis.mp4 94.1 MB
  • 15. Introduction to Clustering Analysis/12. Introduction to Hierarchal Clustering.mp4 92.8 MB
  • 10. Logistic Regression/2. Implementing Logistic Regression using Sklearn.mp4 91.2 MB
  • 20. Predicting Health Expense of Customers/6. Performing Multivariate Analysis.mp4 90.1 MB
  • 9. Linear Regression/3. Feature Selection using RFECV.mp4 90.1 MB
  • 18. Time Series Forecasting/3. Regression Vs Time Series.mp4 87.0 MB
  • 5. Data Cleaning/6. Imputing Missing Values in a real-time scenario.mp4 86.6 MB
  • 12. Tree Based Models/1. Intuition for decision trees.mp4 86.0 MB
  • 16. Dimensionality Reduction/18. Introduction to t-SNE.mp4 85.2 MB
  • 9. Linear Regression/1. Introduction to Linear Regression.mp4 85.2 MB
  • 17. Recommendation Engines/12. Introduction to Collaborative Filtering.mp4 84.8 MB
  • 3. Python Functions Deep Dive/9. Filter, Map, and Zip Functions.mp4 83.7 MB
  • 5. Data Cleaning/3. When should we delete the Missing values.mp4 83.5 MB
  • 16. Dimensionality Reduction/1. Why High Dimensional Datasets are a Problem.mp4 83.1 MB
  • 18. Time Series Forecasting/14. Metrics for Time series Forecasting.mp4 82.5 MB
  • 1. Python Fundamentals/8. Arithmetic and Assignment Operators.mp4 81.8 MB
  • 19. Employee Promotion Prediction/15. Performance Analysis.mp4 80.9 MB
  • 1. Python Fundamentals/5. Scope of Variables.mp4 80.9 MB
  • 18. Time Series Forecasting/25. Auto Correlation and Partial Correlation.mp4 80.6 MB
  • 17. Recommendation Engines/6. Preprocessing the Data for Content Based Filtering.mp4 80.4 MB
  • 4. Python for Data Science/21. Merging and Concatenating DataFrames.mp4 80.3 MB
  • 2. Python for Data Analysis/20. Bubble Sort.mp4 79.2 MB
  • 2. Python for Data Analysis/8. Introduction to Sets.mp4 79.2 MB
  • 18. Time Series Forecasting/21. Checking for Stationarity using Statistical Methods Part 2.mp4 79.1 MB
  • 21. Determining Whether a Person should be Granted Loan/4. Performing Descriptive Statistics.mp4 79.0 MB
  • 10. Logistic Regression/9. Real time prediction using logistic regression.mp4 78.3 MB
  • 4. Python for Data Science/7. Meta Characters for Regular Expressions.mp4 77.6 MB
  • 5. Data Cleaning/4. Imputing the Missing Values using the Business Logic.mp4 77.5 MB
  • 16. Dimensionality Reduction/16. Introduction to Principal Component Analysis.mp4 77.4 MB
  • 22. Optimizing Agricultural Production/4. Performing Descriptive Statistics.mp4 77.1 MB
  • 9. Linear Regression/2. Implementing Linear Regression using Sklearn.mp4 77.0 MB
  • 2. Python for Data Analysis/24. Quiz Solution.mp4 76.8 MB
  • 18. Time Series Forecasting/17. Holt and Holt Winter Exponential Smoothing.mp4 76.7 MB
  • 7. Feature Engineering/9. Finding the Length, Polarity and Subjectivity.mp4 76.6 MB
  • 17. Recommendation Engines/13. Preprocessing the Data for Collaborative Filtering.mp4 75.9 MB
  • 15. Introduction to Clustering Analysis/8. Implementing K Means on the Mall Dataset.mp4 75.0 MB
  • 20. Predicting Health Expense of Customers/5. Performing Bivariate Analysis.mp4 74.9 MB
  • 16. Dimensionality Reduction/5. Introduction to Correlation using Heatmap.mp4 74.9 MB
  • 14. Imbalanced Machine Learning/5. Applying Logistic Regression using Sklearn.mp4 74.6 MB
  • 18. Time Series Forecasting/7. Getting Time Series data.mp4 74.5 MB
  • 15. Introduction to Clustering Analysis/7. Solving a Real World Problem.mp4 74.5 MB
  • 6. Data Visualizations/3. Multivariate Analysis.mp4 74.3 MB
  • 14. Imbalanced Machine Learning/2. Using Resampling Techniques to Balance the Data.mp4 74.0 MB
  • 20. Predicting Health Expense of Customers/10. Applying Gradient Boosting Model.mp4 73.8 MB
  • 11. Introduction to KNN, SVM, Naive Bayes/2. The kermel trick for support vector machine.mp4 73.8 MB
  • 21. Determining Whether a Person should be Granted Loan/7. Bivariate Data Analysis.mp4 73.6 MB
  • 18. Time Series Forecasting/36. Understanding SARIMA Model.mp4 73.3 MB
  • 11. Introduction to KNN, SVM, Naive Bayes/8. When should we apply SVM, KNN and Naive bayes.mp4 73.2 MB
  • 5. Data Cleaning/9. How Outliers can be harmful for Machine Learning Models.mp4 72.4 MB
  • 4. Python for Data Science/20. Indexing, Selecting, and Filtering Data using Pandas.mp4 72.3 MB
  • 21. Determining Whether a Person should be Granted Loan/2. Setting up the Environment.mp4 71.9 MB
  • 12. Tree Based Models/6. Introduction to Random forest.mp4 71.4 MB
  • 19. Employee Promotion Prediction/12. Data Processing.mp4 70.9 MB
  • 11. Introduction to KNN, SVM, Naive Bayes/3. Implementing support vector machine using sklearn.mp4 70.7 MB
  • 15. Introduction to Clustering Analysis/5. Using the Elbow Method for Choosing the Best Value for K.mp4 70.3 MB
  • 21. Determining Whether a Person should be Granted Loan/5. Data Cleaning.mp4 70.2 MB
  • 13. Boosting Models/4. Implementing Gradient Boosting using sklearn.mp4 70.2 MB
  • 2. Python for Data Analysis/6. Introduction to Dictionaries.mp4 70.1 MB
  • 18. Time Series Forecasting/16. Simple Exponential Smoothing.mp4 69.9 MB
  • 18. Time Series Forecasting/39. Understanding ARIMAX Model.mp4 69.7 MB
  • 12. Tree Based Models/5. Understanding the concept of Bagging.mp4 69.2 MB
  • 1. Python Fundamentals/18. If, elif, and else.mp4 69.1 MB
  • 4. Python for Data Science/18. Reading Datasets using Pandas.mp4 68.9 MB
  • 1. Python Fundamentals/1. Why should you learn Python.mp4 68.9 MB
  • 15. Introduction to Clustering Analysis/2. Types of Clustering.mp4 68.4 MB
  • 21. Determining Whether a Person should be Granted Loan/6. Univariate Data Visualizations.mp4 68.3 MB
  • 13. Boosting Models/6. Implementing XGBoost using sklearn.mp4 68.3 MB
  • 18. Time Series Forecasting/20. Checking for Stationarity Part 1.mp4 68.2 MB
  • 18. Time Series Forecasting/28. The Simple Auto Regressive Model Implementation.mp4 68.1 MB
  • 7. Feature Engineering/26. Quiz Solution.mp4 68.0 MB
  • 16. Dimensionality Reduction/22. Difference between PCA, t-SNE, and LDA.mp4 67.9 MB
  • 18. Time Series Forecasting/9. Handling Outlier Values.mp4 67.6 MB
  • 5. Data Cleaning/1. Causes and Impact of Missing Values.mp4 67.5 MB
  • 14. Imbalanced Machine Learning/11. Implementing Neighbors based Sampling using Imblearn.mp4 67.1 MB
  • 22. Optimizing Agricultural Production/6. Clustering Similar Crops.mp4 66.7 MB
  • 17. Recommendation Engines/15. Interpreting the Results obtained from User Based Filtering.mp4 66.7 MB
  • 17. Recommendation Engines/16. Implementation of Item Based Collaborative Filtering.mp4 66.6 MB
  • 18. Time Series Forecasting/11. Splitting Time Series Data.mp4 66.6 MB
  • 18. Time Series Forecasting/27. The Simple Auto Regressive Model.mp4 66.5 MB
  • 17. Recommendation Engines/8. Introduction to Transactional Encoder.mp4 66.5 MB
  • 5. Data Cleaning/10. Finding out Outliers from the Data.mp4 66.3 MB
  • 3. Python Functions Deep Dive/18. Solving the Fibonacci Problem using Recursion.mp4 65.7 MB
  • 1. Python Fundamentals/9. Comparison, Logical, and Bitwise Operators.mp4 65.4 MB
  • 3. Python Functions Deep Dive/23. Encapsulation.mp4 65.2 MB
  • 17. Recommendation Engines/14. Implementation of User Based Collaborative Filtering.mp4 65.2 MB
  • 17. Recommendation Engines/22. Comparing Content, and Collaborative Based Filtering.mp4 65.0 MB
  • 11. Introduction to KNN, SVM, Naive Bayes/7. Implementing Naive Bayes using sklearn.mp4 65.0 MB
  • 5. Data Cleaning/2. Types of Missing Values.mp4 64.8 MB
  • 20. Predicting Health Expense of Customers/3. Understanding the Problem Statement.mp4 64.8 MB
  • 19. Employee Promotion Prediction/4. Performing Descriptive Statistics.mp4 64.7 MB
  • 8. Data Processing/1. Types of Encoding Techniques.mp4 63.9 MB
  • 5. Data Cleaning/12. Deleting and Capping the Outliers.mp4 63.7 MB
  • 2. Python for Data Analysis/7. Nested Dictionaries.mp4 63.5 MB
  • 7. Feature Engineering/1. Introduction to Feature Engineering.mp4 63.0 MB
  • 18. Time Series Forecasting/42. Implementing SARIMAX Model.mp4 62.9 MB
  • 10. Logistic Regression/10. Industry Relevance of Logistic Regression.mp4 62.8 MB
  • 19. Employee Promotion Prediction/3. Understanding the Problem Statement.mp4 62.7 MB
  • 7. Feature Engineering/5. Binning Numerical Features.mp4 62.2 MB
  • 17. Recommendation Engines/5. Introduction to Content Based Filtering.mp4 61.9 MB
  • 10. Logistic Regression/4. Hyperparameter tuning using Grid search.mp4 61.6 MB
  • 17. Recommendation Engines/7. Filtering Movies Based on Genres.mp4 61.6 MB
  • 7. Feature Engineering/23. Feature Engineering on Marketing Data.mp4 61.4 MB
  • 2. Python for Data Analysis/9. Set Operations.mp4 61.4 MB
  • 17. Recommendation Engines/26. Case Study for Youtube.mp4 61.0 MB
  • 14. Imbalanced Machine Learning/4. Preparing the Data for Predictive Modelling.mp4 60.7 MB
  • 15. Introduction to Clustering Analysis/1. Introduction to Clustering.mp4 60.6 MB
  • 20. Predicting Health Expense of Customers/14. Major Takeaways from the Project.mp4 60.4 MB
  • 4. Python for Data Science/17. Quiz Solution.mp4 60.4 MB
  • 14. Imbalanced Machine Learning/9. Implementing Random Under Sampling using Imblearn.mp4 60.3 MB
  • 9. Linear Regression/4. Data Transformation with Linear Regression.mp4 60.3 MB
  • 14. Imbalanced Machine Learning/10. Implementing Synthetic Sampling using Imblearn.mp4 60.2 MB
  • 16. Dimensionality Reduction/2. Methods to solve the problem of High Dimensionality.mp4 59.9 MB
  • 13. Boosting Models/1. Understading the concept of boosting.mp4 59.9 MB
  • 7. Feature Engineering/20. Feature Engineering on Employee Data.mp4 59.9 MB
  • 20. Predicting Health Expense of Customers/11. Creating Ensembles of Models.mp4 59.8 MB
  • 6. Data Visualizations/1. Univariate Analysis.mp4 59.8 MB
  • 14. Imbalanced Machine Learning/3. Solving a Real World Problem.mp4 59.7 MB
  • 21. Determining Whether a Person should be Granted Loan/9. Applying Resampling.mp4 59.7 MB
  • 7. Feature Engineering/6. Aggregating Features.mp4 59.6 MB
  • 7. Feature Engineering/2. Removing Unnecessary Columns.mp4 59.6 MB
  • 18. Time Series Forecasting/32. Understanding ARMA Model.mp4 59.5 MB
  • 10. Logistic Regression/5. Applying Cross Validation.mp4 59.5 MB
  • 16. Dimensionality Reduction/11. Introduction to Recursive Feature Selection.mp4 59.4 MB
  • 17. Recommendation Engines/3. Types of Recommender Systems.mp4 59.3 MB
  • 17. Recommendation Engines/25. Case Study for Netflix.mp4 59.1 MB
  • 17. Recommendation Engines/9. Recommending Similar Movies to Watch.mp4 59.0 MB
  • 4. Python for Data Science/10. Sets for Regular Expressions.mp4 58.9 MB
  • 5. Data Cleaning/15. Quiz Solution.mp4 58.8 MB
  • 5. Data Cleaning/5. Imputing Missing Values using MeanMedianMode.mp4 58.7 MB
  • 15. Introduction to Clustering Analysis/3. Applications of Clustering.mp4 58.7 MB
  • 18. Time Series Forecasting/34. Understanding ARIMA Model.mp4 58.6 MB
  • 14. Imbalanced Machine Learning/12. Combination of Oversampling and Under sampling.mp4 58.5 MB
  • 17. Recommendation Engines/18. Quiz Solution.mp4 58.3 MB
  • 10. Logistic Regression/7. Using accuracy score to analyze the performance of model.mp4 58.2 MB
  • 16. Dimensionality Reduction/17. Implementing PCA.mp4 58.2 MB
  • 18. Time Series Forecasting/13. Basic Forecasting Techniques.mp4 58.2 MB
  • 3. Python Functions Deep Dive/17. Solving the Factorial Problem using Recursion.mp4 58.1 MB
  • 22. Optimizing Agricultural Production/2. Understanding the Dataset.mp4 57.9 MB
  • 14. Imbalanced Machine Learning/13. Implementing Ensemble Models for Imbalanced Data.mp4 57.5 MB
  • 4. Python for Data Science/24. Quiz Solution.mp4 57.4 MB
  • 3. Python Functions Deep Dive/10. List, set, and Dictionary Comprehensions.mp4 57.2 MB
  • 14. Imbalanced Machine Learning/8. Implementing Random Over Sampling using Imblearn.mp4 57.1 MB
  • 20. Predicting Health Expense of Customers/9. Applying Random Forest Model.mp4 57.0 MB
  • 3. Python Functions Deep Dive/2. Default Parameters in Functions.mp4 56.6 MB
  • 6. Data Visualizations/29. Quiz Solution.mp4 56.4 MB
  • 12. Tree Based Models/7. Understanding the parameters of Random forest.mp4 56.3 MB
  • 14. Imbalanced Machine Learning/1. Why Imbalanced Data needs extra attention.mp4 56.2 MB
  • 12. Tree Based Models/3. Advantages and Issues with Decision trees.mp4 56.0 MB
  • 17. Recommendation Engines/4. Evaluating Recommender Systems.mp4 55.7 MB
  • 3. Python Functions Deep Dive/8. Lambda Functions.mp4 55.7 MB
  • 19. Employee Promotion Prediction/7. Univariate Analysis.mp4 55.7 MB
  • 1. Python Fundamentals/17. Quiz Solution.mp4 55.7 MB
  • 1. Python Fundamentals/19. For and While.mp4 55.6 MB
  • 18. Time Series Forecasting/50. Mean Forecast Error.mp4 55.5 MB
  • 16. Dimensionality Reduction/13. Introduction the Boruta Algorithm.mp4 55.0 MB
  • 21. Determining Whether a Person should be Granted Loan/10. Applying Logistic Regression.mp4 54.9 MB
  • 15. Introduction to Clustering Analysis/15. Introduction to DBSCAN Clustering.mp4 54.9 MB
  • 15. Introduction to Clustering Analysis/14. Implementing Hierarchial Clustering.mp4 54.9 MB
  • 18. Time Series Forecasting/45. Choosing the Right for Model Smaller Datasets.mp4 54.8 MB
  • 18. Time Series Forecasting/5. Components of Time Series.mp4 54.5 MB
  • 1. Python Fundamentals/13. String Formatting.mp4 53.8 MB
  • 7. Feature Engineering/19. Quiz Solution.mp4 53.7 MB
  • 16. Dimensionality Reduction/12. Implementing Recursive Feature Selection.mp4 53.4 MB
  • 4. Python for Data Science/13. Array Creation using Numpy.mp4 53.4 MB
  • 5. Data Cleaning/13. Dealing with Outliers in a real-world scenario.mp4 53.4 MB
  • 5. Data Cleaning/11. Using Winsorization to deal with Outliers.mp4 53.0 MB
  • 19. Employee Promotion Prediction/10. Feature Engineering.mp4 52.9 MB
  • 20. Predicting Health Expense of Customers/1. Setting up the Environment.mp4 52.6 MB
  • 18. Time Series Forecasting/15. Simple Moving Averages.mp4 52.5 MB
  • 15. Introduction to Clustering Analysis/10. Clustering Multiple Dimensions.mp4 52.4 MB
  • 9. Linear Regression/10. Industry relevance of linear regression.mp4 52.3 MB
  • 7. Feature Engineering/24. Feature Engineering on Titanic Data.mp4 52.0 MB
  • 15. Introduction to Clustering Analysis/6. Introduction to K Means Clustering.mp4 51.7 MB
  • 5. Data Cleaning/8. Quiz Solution.mp4 51.5 MB
  • 1. Python Fundamentals/22. Quiz Solution.mp4 51.4 MB
  • 9. Linear Regression/8. MAE, RMSE, R2 and Adjusted R2 in code.mp4 51.4 MB
  • 16. Dimensionality Reduction/20. Introduction to Linear Discriminant Analysis.mp4 51.3 MB
  • 16. Dimensionality Reduction/6. Removing Highly Correlated Columns using Correlation.mp4 51.2 MB
  • 6. Data Visualizations/17. Quiz Solution.mp4 51.2 MB
  • 2. Python for Data Analysis/12. Introduction to Stacks and Queues.mp4 51.1 MB
  • 20. Predicting Health Expense of Customers/13. More things to Try.mp4 51.1 MB
  • 16. Dimensionality Reduction/8. Introduction to Variance Inflation Filtering.mp4 51.0 MB
  • 2. Python for Data Analysis/1. Differences between Lists and Tuples.mp4 51.0 MB
  • 6. Data Visualizations/8. Bar, Line, and Area Charts.mp4 50.9 MB
  • 17. Recommendation Engines/11. Quiz Solution.mp4 50.9 MB
  • 18. Time Series Forecasting/33. Implementing ARMA Model.mp4 50.6 MB
  • 18. Time Series Forecasting/24. Converting Non-Stationary Series into Stationary Implementation.mp4 50.5 MB
  • 18. Time Series Forecasting/23. Converting Non-Stationary Series into Stationary.mp4 50.4 MB
  • 17. Recommendation Engines/24. Quiz Solution.mp4 50.3 MB
  • 12. Tree Based Models/8. Implementing random forest using Sklearn.mp4 50.2 MB
  • 15. Introduction to Clustering Analysis/16. Implementing DBSCAN Clustering.mp4 50.2 MB
  • 16. Dimensionality Reduction/9. Implementing VIF using statsmodel.mp4 50.2 MB
  • 6. Data Visualizations/18. Animation with Bubbleplot.mp4 50.1 MB
  • 3. Python Functions Deep Dive/7. Quiz Solution.mp4 50.0 MB
  • 18. Time Series Forecasting/4. Applications of Time Series.mp4 49.6 MB
  • 6. Data Visualizations/5. Quiz Solution.mp4 49.4 MB
  • 1. Python Fundamentals/7. Quiz Solution.mp4 48.8 MB
  • 22. Optimizing Agricultural Production/1. Setting up the Environment.mp4 48.7 MB
  • 3. Python Functions Deep Dive/24. Polymorphism.mp4 48.5 MB
  • 17. Recommendation Engines/21. Interpreting Results Obtained from SVD.mp4 48.2 MB
  • 21. Determining Whether a Person should be Granted Loan/1. Understanding the Problem Statement.mp4 47.7 MB
  • 18. Time Series Forecasting/2. Types of Forecasting.mp4 47.5 MB
  • 6. Data Visualizations/6. Scatter Plots.mp4 47.4 MB
  • 17. Recommendation Engines/2. What are it's Use Cases.mp4 47.2 MB
  • 6. Data Visualizations/2. Bivariate Analysis.mp4 47.2 MB
  • 7. Feature Engineering/21. Feature Engineering on FIFA Data.mp4 46.9 MB
  • 18. Time Series Forecasting/40. Implementing ARIMAX Model.mp4 46.9 MB
  • 5. Data Cleaning/16. Introduction to reindex, set_index, reset_index, and sort_index Functions.mp4 46.9 MB
  • 19. Employee Promotion Prediction/14. Predictive Modelling.mp4 46.8 MB
  • 2. Python for Data Analysis/2. Operations on Lists.mp4 46.6 MB
  • 8. Data Processing/13. Train, Test and Validation Split.mp4 46.4 MB
  • 21. Determining Whether a Person should be Granted Loan/12. Summary.mp4 46.3 MB
  • 4. Python for Data Science/6. Quiz Solution.mp4 46.2 MB
  • 18. Time Series Forecasting/41. Understanding SARIMAX Model.mp4 46.0 MB
  • 14. Imbalanced Machine Learning/14. Introduction to XG Boost for Imbalanced Data.mp4 45.7 MB
  • 1. Python Fundamentals/14. String Methods.mp4 45.4 MB
  • 16. Dimensionality Reduction/14. Implementing the Boruta Algorithm.mp4 45.3 MB
  • 18. Time Series Forecasting/47. Best Practices while Choosing a Time series Model..mp4 45.1 MB
  • 21. Determining Whether a Person should be Granted Loan/8. Preparing the Data for Modelling.mp4 44.9 MB
  • 3. Python Functions Deep Dive/5. Python Modules.mp4 44.8 MB
  • 14. Imbalanced Machine Learning/6. Applying Random Forest using Sklearn.mp4 44.7 MB
  • 19. Employee Promotion Prediction/6. Outlier Values Treatment.mp4 44.6 MB
  • 19. Employee Promotion Prediction/13. Feature Scaling.mp4 44.3 MB
  • 10. Logistic Regression/3. Feature Selection using RFECV.mp4 44.2 MB
  • 5. Data Cleaning/23. Data Cleaning on Melbourne Housing Dataset.mp4 44.2 MB
  • 19. Employee Promotion Prediction/16. Improvements Possible.mp4 43.9 MB
  • 15. Introduction to Clustering Analysis/13. Introduction to Dendrograms.mp4 43.8 MB
  • 19. Employee Promotion Prediction/1. Setting up the Environment.mp4 43.7 MB
  • 2. Python for Data Analysis/14. Implementing Stacks and Queues using Deque.mp4 43.6 MB
  • 14. Imbalanced Machine Learning/15. Comparing the Results.mp4 43.5 MB
  • 5. Data Cleaning/20. Introduction to Melt, Explode, and Squeeze Functions.mp4 43.4 MB
  • 21. Determining Whether a Person should be Granted Loan/3. Understanding the Dataset.mp4 43.1 MB
  • 13. Boosting Models/5. Getting High level intuition for XGBoost.mp4 43.1 MB
  • 1. Python Fundamentals/15. User Input.mp4 43.0 MB
  • 4. Python for Data Science/9. Special Characters for Regular Expressions.mp4 42.9 MB
  • 1. Python Fundamentals/20. Break and Continue.mp4 42.7 MB
  • 17. Recommendation Engines/20. Implementing SVD using Surprise.mp4 42.6 MB
  • 17. Recommendation Engines/1. Introduction to Recommender systems.mp4 42.5 MB
  • 3. Python Functions Deep Dive/26. Quiz Solution.mp4 42.4 MB
  • 22. Optimizing Agricultural Production/10. Summarizing the Key-Points.mp4 42.4 MB
  • 22. Optimizing Agricultural Production/8. Predictive Modelling.mp4 42.3 MB
  • 3. Python Functions Deep Dive/12. Quiz Solution.mp4 42.2 MB
  • 3. Python Functions Deep Dive/1. Introduction to Functions.mp4 42.2 MB
  • 4. Python for Data Science/15. Built-in Functions in Numpy.mp4 41.9 MB
  • 19. Employee Promotion Prediction/9. Multivariate Analysis.mp4 41.9 MB
  • 6. Data Visualizations/36. Network Charts.mp4 41.7 MB
  • 8. Data Processing/14. Standardization and Normalization.mp4 41.6 MB
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  • 21. Determining Whether a Person should be Granted Loan/4. Performing Descriptive Statistics.srt 6.2 kB
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  • 11. Introduction to KNN, SVM, Naive Bayes/1. Introduction to Support Vector machines.srt 5.9 kB
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  • 2. Python for Data Analysis/19. Binary Search.srt 5.7 kB
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  • 17. Recommendation Engines/21. Interpreting Results Obtained from SVD.srt 5.5 kB
  • 16. Dimensionality Reduction/5. Introduction to Correlation using Heatmap.srt 5.5 kB
  • 11. Introduction to KNN, SVM, Naive Bayes/4. Introduction to K nearest neighbors.srt 5.5 kB
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  • 14. Imbalanced Machine Learning/5. Applying Logistic Regression using Sklearn.srt 5.2 kB
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  • 15. Introduction to Clustering Analysis/7. Solving a Real World Problem.srt 5.1 kB
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  • 14. Imbalanced Machine Learning/4. Preparing the Data for Predictive Modelling.srt 4.9 kB
  • 13. Boosting Models/4. Implementing Gradient Boosting using sklearn.srt 4.9 kB
  • 17. Recommendation Engines/12. Introduction to Collaborative Filtering.srt 4.9 kB
  • 15. Introduction to Clustering Analysis/12. Introduction to Hierarchal Clustering.srt 4.9 kB
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  • 6. Data Visualizations/1. Univariate Analysis.srt 4.9 kB
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  • 9. Linear Regression/1. Introduction to Linear Regression.srt 4.8 kB
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  • 2. Python for Data Analysis/9. Set Operations.srt 4.8 kB
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  • 10. Logistic Regression/7. Using accuracy score to analyze the performance of model.srt 4.8 kB
  • 2. Python for Data Analysis/6. Introduction to Dictionaries.srt 4.7 kB
  • 17. Recommendation Engines/14. Implementation of User Based Collaborative Filtering.srt 4.7 kB
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  • 18. Time Series Forecasting/13. Basic Forecasting Techniques.srt 4.7 kB
  • 12. Tree Based Models/1. Intuition for decision trees.srt 4.7 kB
  • 14. Imbalanced Machine Learning/11. Implementing Neighbors based Sampling using Imblearn.srt 4.7 kB
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  • 20. Predicting Health Expense of Customers/11. Creating Ensembles of Models.srt 4.6 kB
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  • 2. Python for Data Analysis/8. Introduction to Sets.srt 4.6 kB
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  • 16. Dimensionality Reduction/18. Introduction to t-SNE.srt 4.6 kB
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  • 6. Data Visualizations/29. Quiz Solution.srt 4.5 kB
  • 19. Employee Promotion Prediction/4. Performing Descriptive Statistics.srt 4.5 kB
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  • 21. Determining Whether a Person should be Granted Loan/6. Univariate Data Visualizations.srt 4.4 kB
  • 16. Dimensionality Reduction/1. Why High Dimensional Datasets are a Problem.srt 4.4 kB
  • 7. Feature Engineering/2. Removing Unnecessary Columns.srt 4.4 kB
  • 16. Dimensionality Reduction/17. Implementing PCA.srt 4.4 kB
  • 6. Data Visualizations/6. Scatter Plots.srt 4.4 kB
  • 18. Time Series Forecasting/21. Checking for Stationarity using Statistical Methods Part 2.srt 4.4 kB
  • 2. Python for Data Analysis/2. Operations on Lists.srt 4.4 kB
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  • 14. Imbalanced Machine Learning/2. Using Resampling Techniques to Balance the Data.srt 4.4 kB
  • 16. Dimensionality Reduction/12. Implementing Recursive Feature Selection.srt 4.3 kB
  • 14. Imbalanced Machine Learning/8. Implementing Random Over Sampling using Imblearn.srt 4.3 kB
  • 5. Data Cleaning/3. When should we delete the Missing values.srt 4.3 kB
  • 6. Data Visualizations/17. Quiz Solution.srt 4.3 kB
  • 14. Imbalanced Machine Learning/3. Solving a Real World Problem.srt 4.3 kB
  • 8. Data Processing/1. Types of Encoding Techniques.srt 4.2 kB
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  • 22. Optimizing Agricultural Production/6. Clustering Similar Crops.srt 4.2 kB
  • 6. Data Visualizations/38. Quiz Solution.srt 4.2 kB
  • 7. Feature Engineering/10. Finding the Words, Characters, and Punctuation Count.srt 4.2 kB
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  • 2. Python for Data Analysis/7. Nested Dictionaries.srt 4.2 kB
  • 18. Time Series Forecasting/25. Auto Correlation and Partial Correlation.srt 4.2 kB
  • 18. Time Series Forecasting/11. Splitting Time Series Data.srt 4.2 kB
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  • 6. Data Visualizations/5. Quiz Solution.srt 4.1 kB
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  • 18. Time Series Forecasting/16. Simple Exponential Smoothing.srt 4.1 kB
  • 15. Introduction to Clustering Analysis/10. Clustering Multiple Dimensions.srt 4.1 kB
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  • 14. Imbalanced Machine Learning/9. Implementing Random Under Sampling using Imblearn.srt 4.1 kB
  • 17. Recommendation Engines/9. Recommending Similar Movies to Watch.srt 4.1 kB
  • 11. Introduction to KNN, SVM, Naive Bayes/8. When should we apply SVM, KNN and Naive bayes.srt 4.1 kB
  • 3. Python Functions Deep Dive/20. Quiz Solution.srt 4.1 kB
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  • 5. Data Cleaning/20. Introduction to Melt, Explode, and Squeeze Functions.srt 4.0 kB
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  • 5. Data Cleaning/4. Imputing the Missing Values using the Business Logic.srt 3.9 kB
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  • 18. Time Series Forecasting/36. Understanding SARIMA Model.srt 3.9 kB
  • 15. Introduction to Clustering Analysis/6. Introduction to K Means Clustering.srt 3.9 kB
  • 11. Introduction to KNN, SVM, Naive Bayes/2. The kermel trick for support vector machine.srt 3.9 kB
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  • 14. Imbalanced Machine Learning/13. Implementing Ensemble Models for Imbalanced Data.srt 3.8 kB
  • 17. Recommendation Engines/22. Comparing Content, and Collaborative Based Filtering.srt 3.8 kB
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