Dr K P Naidu

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24. Curse of Dimensionality :Principal component analysis(PCA) with example and its implementation
24. Curse of Dimensionality :Principal component analysis(PCA) with example and its implementation
23. Introduction to ensemble learning, its techniques: Bagging, Boosting&stacking ,hard& soft voting
23. Introduction to ensemble learning, its techniques: Bagging, Boosting&stacking ,hard& soft voting
22. Linear classification and Nonlinear classification, Types of kernals in SVM
22. Linear classification and Nonlinear classification, Types of kernals in SVM
21. What is Hard Margin and Soft Margin in SVM
21. What is Hard Margin and Soft Margin in SVM
20. Support Vector Machine (SVM):
20. Support Vector Machine (SVM):
19. Metrics to evaluate Performance of classification model
19. Metrics to evaluate Performance of classification model
18. Metrics to evaluate Performance of Regression Model
18. Metrics to evaluate Performance of Regression Model
End to End Machine Learning Project : Build Machine Learning Model and deploy Model using Streamlit
End to End Machine Learning Project : Build Machine Learning Model and deploy Model using Streamlit
4. input widgets in streamlit
4. input widgets in streamlit
3. Create and run streamlit application
3. Create and run streamlit application
2.Install streamlit
2.Install streamlit
1. Introduction to streamlit
1. Introduction to streamlit
17. Polynomial Regression with example
17. Polynomial Regression with example
16. Build linear regression model using transformations for non linear data
16. Build linear regression model using transformations for non linear data
15. Linear Regression with example
15. Linear Regression with example
14. Introduction to linear regression
14. Introduction to linear regression
13. Constructing Decision Tree using ID3 Algorithm
13. Constructing Decision Tree using ID3 Algorithm
12. How to find information gain and gain ratio
12. How to find information gain and gain ratio
11. Decision Trees in ML: Metrics to find best attribute of decision tree with example_part 1
11. Decision Trees in ML: Metrics to find best attribute of decision tree with example_part 1
10. Introduction to decision trees
10. Introduction to decision trees
9. Naive Bayes Algorithm with example, adv and disadv of the algorithm
9. Naive Bayes Algorithm with example, adv and disadv of the algorithm
8. KNN algorithm with example
8. KNN algorithm with example
7. Distance based Methods in Machine Learning
7. Distance based Methods in Machine Learning
6. Main Challenges of Machine Learning
6. Main Challenges of Machine Learning
5. Artificial Intelligence vs Machine Learning vs Deep Learning
5. Artificial Intelligence vs Machine Learning vs Deep Learning
4. Applications of ML and types of features #machinelearning #types of features
4. Applications of ML and types of features #machinelearning #types of features
3. Types of Machine Learning.#machinelearning #artificialintelligence #datascience #deeplearning
3. Types of Machine Learning.#machinelearning #artificialintelligence #datascience #deeplearning
2. Difference b/w Traditional Compuer Systems and ML Systems?#machinelearning #datascience
2. Difference b/w Traditional Compuer Systems and ML Systems?#machinelearning #datascience
1. Definition of Machine Learning.#machinelearning #artificialintelligence #datascience
1. Definition of Machine Learning.#machinelearning #artificialintelligence #datascience
hive internal and external table
hive internal and external table