Data Science & Machine Learning Using Python
Lecture 1: What is Machine Learning?
Lecture 2: Linear Regression Single Variable
Lecture 3: Linear Regression Multiple Variables
Lecture 4: Gradient Descent and Cost Function
Lecture 5: Save Model Using Joblib And Pickle
Lecture 6: Dummy Variables & One Hot Encoding
Lecture 7: Training and Testing Data
Lecture 8: Logistic Regression (Binary Classification)
Lecture 9: Logistic Regression (Multiclass Classification)
Lecture 10: Decision Tree
Lecture 11: Support Vector Machine (SVM)
Lecture 12: Random Forest
Lecture 13: Fold Cross Validation
Lecture 14: K Means Clustering Algorithm
Lecture 15: Naive Bayes Classifier Algorithm Part 1
Lecture 16: Naive Bayes Classifier Algorithm Part 2
Lecture 17: Hyper parameter Tuning (GridSearchCV)
Lecture 18: L1 and L2 Regularization | Lasso, Ridge Regression
Lecture 19: K nearest neighbours classification with Python code
Lecture 20: Principal Component Analysis (PCA) with Python Code
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Lecture 18: L1 and L2 Regularization | Lasso, Ridge Regression
Data Science & Machine Learning Using Python
Lecture 18: L1 and L2 Regularization | Lasso, Ridge Regression
Lecture 18: L1 and L2 Regularization | Lasso, Ridge Regression
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