Phases for Performing Machine Learning00:00:00
Pros and Cons00:00:00
Challenges and Limitations00:00:00
Practical Application of Machine Learning00:00:00
Complementing Fields of Machine Learning00:00:00
Machine Learning Model Flow00:00:00
How to Treat Data in Machine Learning00:00:00
Labeled and Unlabeled Data00:00:00
Statistical Learning Perspective00:00:00
Parametric and Non-Parametric Machine Learning Algorithms00:00:00
Types Of Machine Learning00:00:00
Supervised Learning00:00:00
Unsupervised Learning00:00:00
Performance Measures00:00:00
Receiver Operating characteristics(ROC) Curve00:00:00
How to Measure Purity?00:00:00
Bias-Variance Trade-Off00:00:00
Overfitting and Underfitting00:00:00
Unpredictable and data Formats00:00:00
Under Fitting00:00:00
Over fitting00:00:00
Data Instability00:00:00
Bootstrapping00:00:00
Jackknife00:00:00
Cross Validation00:00:00
Leave-p-out Cross-Validation(LpO CV)00:00:00
K-fold Cross-Validation00:00:00
Max Voting00:00:00
Averaging00:00:00
Weighted Average00:00:00
Bootstrap Aggregation(Bagging)00:00:00
Boosting00:00:00