[TutsNode.net] - Machine Learning & Data Science with Python & Kaggle A-Z

  20. Competition Section on Kaggle

   3. Quiz.html

  26. Introduction to Machine Learning with Real Hearth Attack Prediction Project

   7. Quiz.html

   6. Recognizing Variables In Dataset.mp4

   3. Notebook Design to be Used in the Project.mp4

  19. First Contact with Kaggle

   6. Quiz.html

   1. What is Kaggle.mp4

   3. Registering on Kaggle and Member Login Procedures.mp4

  1. First Contact with Machine Learning

   5. FAQ regarding Machine Learning.html

   1. What is Machine Learning.mp4

  34. Extra

  3. Evaluation Metrics in Machine Learning

   2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4

   3. Evaluating Performance Regression Error Metrics in Python.mp4

  4. Supervised Learning with Machine Learning

   1. What is Supervised Learning in Machine Learning.mp4

  6. Bias Variance Trade-Off in Machine Learning

   1. What is Bias Variance Trade-Off.mp4

  7. Logistic Regression Algorithm in Machine Learning A-Z

   3. Logistic Regression Algorithm with Python Part 2.mp4

   6. Logistic Regression Algorithm with Python Part 5.mp4

   4. Logistic Regression Algorithm with Python Part 3.mp4

  9. K Nearest Neighbors Algorithm in Machine Learning A-Z

   3. K Nearest Neighbors Algorithm with Python Part 2.mp4

   4. K Nearest Neighbors Algorithm with Python Part 3.mp4

  11. Decision Tree Algorithm in Machine Learning A-Z

   3. Decision Tree Algorithm with Python Part 2.mp4

   1. Decision Tree Algorithm Theory.mp4

   2. Decision Tree Algorithm with Python Part 1.mp4

  13. Support Vector Machine Algorithm in Machine Learning A-Z

   4. Support Vector Machine Algorithm with Python Part 3.mp4

   5. Support Vector Machine Algorithm with Python Part 4.mp4

   1. Support Vector Machine Algorithm Theory.mp4

  14. Unsupervised Learning with Machine Learning

   1. Unsupervised Learning Overview.mp4

  15. K Means Clustering Algorithm in Machine Learning A-Z

   2. K Means Clustering Algorithm with Python Part 1.mp4

   5. K Means Clustering Algorithm with Python Part 4.mp4

   1. K Means Clustering Algorithm Theory.mp4

  16. Hierarchical Clustering Algorithm in machine learning data science

   2. Hierarchical Clustering Algorithm with Python Part 1.mp4

   1. Hierarchical Clustering Algorithm Theory.mp4

  18. Recommender System Algorithm in Machine Learning A-Z

   1. What is the Recommender System Part 1.mp4

  21. Dataset Section on Kaggle

   1. Datasets on Kaggle.mp4

  22. Code Section on Kaggle

   3. Examining the Code Section in Kaggle Lesson 3.mp4

   1. Examining the Code Section in Kaggle Lesson 1.mp4

  23. Discussion Section on Kaggle

   1. What is Discussion on Kaggle.mp4

  24. Other Most Used Options on Kaggle

   2. Ranking Among Users on Kaggle.mp4

   3. Blog and Documentation Sections.mp4

  25. Details on Kaggle

   1. User Page Review on Kaggle.mp4

   4. What Should Be Done to Achieve Success in Kaggle.mp4

  27. First Organization

   3. Initial analysis on the dataset.mp4

   2. Loading the Dataset.mp4

  28. Preparation For Exploratory Data Analysis (EDA)

   1. Examining Missing Values.mp4

  29. Exploratory Data Analysis (EDA) - Uni-variate Analysis

   4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4

   3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4

   2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4

  30. Exploratory Data Analysis (EDA) - Bi-variate Analysis

   8. Examining Statistics of Variables.mp4

   12. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4

   10. Creating a New DataFrame with the Melt() Function.mp4

   7. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4

   11. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4

   15. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4

   14. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4

   5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4

  31. Preparation for Modelling in Machine Learning

   4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4

   5. Dealing with Outliers – Thalach Variable.mp4

   2. Visualizing Outliers.mp4

   1. Dropping Columns with Low Correlation.mp4

   9. Applying One Hot Encoding Method to Categorical Variables.mp4

   10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4

  32. Modelling for machine learning

   4. Hyperparameter Optimization (with GridSearchCV).mp4

   3. Roc Curve and Area Under Curve (AUC).mp4

   7. Random Forest Algorithm.mp4

   5. Decision Tree Algorithm.mp4

  33. Conclusion

   1. Project Conclusion and Sharing.mp4

  2. Installations for Python

   3. Installing Anaconda Distribution for Linux.mp4

   4. Overview of Jupyter Notebook and Google Colab.mp4

  5. Linear Regression Algorithm in Machine Learning A-Z

   5. Linear Regression Algorithm With Python Part 4.mp4

   4. Linear Regression Algorithm With Python Part 3.mp4

  10. Hyperparameter Optimization

   1. Hyperparameter Optimization Theory.mp4

  12. Random Forest Algorithm in Machine Learning A-Z

   2. Random Forest Algorithm with Pyhon Part 1.mp4

  17. Principal Component Analysis (PCA) in Machine Learning A-Z

   4. Principal Component Analysis (PCA) with Python Part 3.mp4

   3. Principal Component Analysis (PCA) with Python Part 2.mp4

  8. K-fold Cross-Validation in Machine Learning A-Z

   1. K-Fold Cross-Validation Theory.mp4

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