ML

Machine Learning –

  1. How to load Scikit-Learn dataset for Machine Learning?
  2. How to Create toy Dataset for Machine Learning in sklearn?
  3. How to Create a Baseline Regression Model in scikit Learn?
  4. How to Create a Baseline Classification Model in Scikit Learn?
  5. Introduction to Linear Regression in Machine Learning
  6. What are the assumptions of OLS Linear Regression?
  7. What happens when OLS Linear Regression Assumptions are Violated?
  8. What is Gradient Descent ? And How does it Works?
  9. Difference between Stochastic, Mini-batch and Batch Gradient Descent ?
  10. Introduction to Polynomial Regression in Machine Learning
  11. Introduction to Ridge Regression in Machine Learning
  12. A Gentle Introduction to Lasso Regression in Machine Learning
  13. A Gentle Introduction to Elastic Net in Machine Learning
  14. What is StandardScaler in Sklearn and How to use It?
  15. Rescale a Feature with MinMaxScaler in sklearn.
  16. How to Detect Outliers in a dataset in Python?
  17. How to Handle Outliers in a dataset in Python?
  18. OneHotEncoder – How to do One Hot Encoding in sklearn?
  19. 5 ways to Handle Imbalanced Dataset in Machine Learning
  20. How to remove Highly Correlated Features from a dataset?
  21. A Brief Introduction to K-Fold Cross Validation in Machine Learning
  22. Logistic Regression in Machine Learning
  23. Confusion Matrix – How to plot and Interpret Confusion Matrix?
  24. What is Precision, Recall and the Trade-off?
  25. What is f1 score in Machine Learning?
  26. What is ROC Curve in Machine Learning?
  27. A Gentle Introduction to Decision Tree in Machine Learning
  28. How to Train a Decision Tree Regressor in Sklearn?
  29. How to Visualize a Decision Tree Model?
  30. A Gentle Introduction to Random Forest in Machine Learning
  31. How to Train a Random Forest Regressor in Sklearn?
  32. How to Identify Important Features of a Random Forest Model?
  33. How to Select Important Features of a Random Forest Model?
  34. How to Evaluate Random Forest with Out-Of-Bag Errors?
  35. What is the difference Between Non-Parametric and Parametric Models?
  36. What is Regularization in ML? What is L1 and L2 Regularization?
  37. What is the Bias/Variance Trade-off in Machine Learning.
  38. Difference between Parameters and Hyperparameters in Machine Learning
  39. What is Overfitting and Underfitting in Machine Learning?
  40. Hyperparameter Optimization with Grid Search in Machine Learning
  41. Hyperparameter Tuning with Randomized Search in Machine Learning
  42. Feature Selection Using Variance Threshold in sklearn
  43. Feature Selection with SelectKBest in Scikit Learn.
  44. Feature Selection with Recursive Feature Elimination (RFECV)
  45. How to remove Highly Correlated Features from a dataset
  46. How to plot a Learning Curve in Machine Learning Python?
  47. How to Plot a Validation Curve in Machine Learning Python?
  48. Support Vector Machines in Machine Learning
  49. What is the Kernel Trick in Support Vector Machines?
  50. How to Use Support Vector Machines for Regression?
  51. Voting Classifiers in Machine Learning
  52. Bagging and Pasting in Machine Learning
  53. Extra-Trees in Machine Learning
  54. How to Build Machine Learning Pipeline with Scikit-Learn?

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