Never stop talking " STOP the Gaza Genocide "
  • Lesson.No : 13
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  • Machine Learning Tutorial Python 12 - K Fold Cross Validation

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Course Lessons

  1. 1- Machine Learning Tutorial Python -1: What is Machine Learning?
  2. 2- Machine Learning Tutorial Python - 2: Linear Regression Single Variable
  3. 3- Machine Learning Tutorial Python - 3: Linear Regression Multiple Variables
  4. 4- Machine Learning Tutorial Python - 4: Gradient Descent and Cost Function
  5. 5- Machine Learning Tutorial Python - 5: Save Model Using Joblib And Pickle
  6. 6- Machine Learning Tutorial Python - 6: Dummy Variables & One Hot Encoding
  7. 7- Machine Learning Tutorial Python - 7: Training and Testing Data
  8. 8- Machine Learning Tutorial Python - 8: Logistic Regression (Binary Classification)
  9. 9- Machine Learning Tutorial Python - 8 Logistic Regression (Multiclass Classification)
  10. 10- Machine Learning Tutorial Python - 9 Decision Tree
  11. 11- Machine Learning Tutorial Python - 10 Support Vector Machine (SVM)
  12. 12- Machine Learning Tutorial Python - 11 Random Forest
  13. 13- Machine Learning Tutorial Python 12 - K Fold Cross Validation
  14. 14- Machine Learning Tutorial Python - 13: K Means Clustering Algorithm
  15. 15- Machine Learning Tutorial Python - 14: Naive Bayes Classifier Algorithm Part 1
  16. 16- Machine Learning Tutorial Python - 15: Naive Bayes Classifier Algorithm Part 2
  17. 17- Machine Learning Tutorial Python - 16: Hyper parameter Tuning (GridSearchCV)
  18. 18- Machine Learning Tutorial Python - 17: L1 and L2 Regularization Lasso, Ridge Regression
  19. 19- Machine Learning Tutorial Python - 18: K nearest neighbors classification with python code
  20. 20- Machine Learning Tutorial Python - 19: Principal Component Analysis (PCA) with Python Code
  21. 21- Machine Learning Tutorial Python - 20: Bias vs Variance In Machine Learning
  22. 22- Machine Learning Tutorial Python - 21: Ensemble Learning - Bagging
  23. 23- Machine Learning & Data Science Project - 1 : Introduction (Real Estate Price Prediction Project)
  24. 24- Machine Learning & Data Science Project - 2 : Data Cleaning (Real Estate Price Prediction Project)
  25. 25- Machine Learning & Data Science Project - 3 : Feature Engineering (Real Estate Price Prediction)
  26. 26- Machine Learning & Data Science Project - 4 : Outlier Removal (Real Estate Price Prediction Project)
  27. 27- Machine Learning & Data Science Project - 5 : Model Building (Real Estate Price Prediction Project)
  28. 28- Machine Learning & Data Science Project - 6 : Python Flask Server (Real Estate Price Prediction)
  29. 29- Machine Learning & Data Science Project - 7 : Website or UI (Real Estate Price Prediction Project)
  30. 30- Deploy machine learning model to production AWS (Amazon EC2 instance)
  31. 31- Data Science & Machine Learning Project - Part 1 Introduction Image Classification
  32. 32- Data Science & Machine Learning Project - Part 2 Data Collection Image Classification
  33. 33- Data Science & Machine Learning Project - Part 3 Data Cleaning Image Classification
  34. 34- Data Science & Machine Learning Project - Part 4 Feature Engineering Image Classification
  35. 35- Data Science & Machine Learning Project - Part 5 Training a Model Image Classification
  36. 36- Data Science & Machine Learning Project - Part 6 Flask Server Image Classification
  37. 37- Data Science & Machine Learning Project - Part 7 Build Website Image Classification
  38. 38- Data Science & Machine Learning Project - Part 8 Deployment & Exercise Image Classification
  39. 39- What is feature engineering Feature Engineering Tutorial Python # 1
  40. 40- Outlier detection and removal using percentile Feature engineering tutorial python # 2
  41. 41- Outlier detection and removal: z score, standard deviation Feature engineering tutorial python # 3
  42. 42- Outlier detection and removal using IQR Feature engineering tutorial python # 4