Data Science & Machine Learning Using Python Data Science and Machine Learning Using Python Course Content 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 1 of 3
