Organizing the content in a structured manner according to a typical syllabus for machine learning can help in understanding the progression of topics. Here's a suggested organization based on common machine learning syllabi: Machine Learning Syllabus Structure 1. Introduction to Machine Learning Video 7: Introduction to Machine Learning Definition Examples Applications of ML Why so popular by Mahesh Huddar 2. Fundamentals and Concepts Video 10: Concept Learning Concept Space Hypothesis Space Distinct Hypothesis Space Machine Learning Mahesh Video 11, 12, 13: FIND S Algorithm - Solved Examples by Mahesh Huddar Video 15: Consistent Hypothesis | Version Space | List Then Eliminate Algorithm by Mahesh Huddar 3. Learning Algorithms - Decision Trees Video 25, 26, 27, 28: How to find Entropy Given Probabilities, Information Gain, Gain in terms of Gini Index, and How to find Entropy | Information Gain | Gain in terms of Gini Index | Decision Tree by Mahesh Huddar Video 29, 30, 31, 32: ID3 Decision tree Learning Algorithm - Solved Numerical Examples by Mahesh Huddar Video 36: How to Avoid Overfitting in Decision Tree Learning | Machine Learning | Data Mining by Mahesh Huddar Video 37: How to handle Continuous Valued Attributes in Decision Tree | Machine Learning by Mahesh Huddar 4. Clustering Algorithms Video 5: K Means Clustering Algorithm | K Means Solved Numerical Example Euclidean Distance by Mahesh Huddar Video 92, 93: DBSCAN Clustering Algorithm Solved Numerical Example in Machine Learning Data Mining Mahesh Huddar 5. Neural Networks Video 48, 49, 50: Perceptron Training Rule for AND, OR Gates | Artificial Neural Networks Machine Learning by Mahesh Huddar Video 56, 57, 58, 59, 60: Back Propagation Algorithm Artificial Neural Network Algorithm Machine Learning by Mahesh Huddar Video 51: Perceptron Rule to design XOR Logic Gate Solved Example ANN Machine Learning by Mahesh Huddar 6. Instance-based Learning and K-Nearest Neighbors Video 106, 107, 108, 109: Solved Examples K Nearest Neighbors Algorithm in Machine Learning by Mahesh Huddar Video 111: Locally Weighted Regression Algorithm Instance-based learning Machine Learning by Dr. Mahesh Huddar 7. Model Evaluation and Validation Video 162, 163, 164: Confusion Matrix and Performance Metrics in Machine Learning by Mahesh Huddar Video 172: K-Fold Cross Validation, Stratified K-Fold, Leave-one-out, Leave-P-Out Cross Validation by Mahesh Huddar 8. Text Classification and NLP Video 90: Agglomerative Hierarchical Clustering Single link Complete link Clustering by Dr. Mahesh Huddar Video 183: Sentiment Analysis using Dictionaries SentiWordNet SentiWords and VADER by Dr Mahesh Huddar 9. Dimensionality Reduction Video 4: Principal Component Analysis | PCA | Dimensionality Reduction in Machine Learning by Mahesh Huddar 10. Association Rule Mining Video 149, 150, 151: Solved Examples Apriori Algorithm to find Strong Association Rules Data Mining Machine Learning by Mahesh Huddar 11. Ensembling Techniques Video 128: Ensemble Learning Techniques Voting Bagging Boosting Random Forest Stacking in ML by Mahesh Huddar 12. Miscellaneous Topics Video 39: Splitting Continuous Attribute using Gini Index in Decision Tree Machine Learning by Mahesh Huddar Video 180: Z-Score based Outlier or Anomaly detection and Removal in machine learning by Mahesh Huddar This structure follows a typical progression from introductory concepts to more advanced topics, covering fundamental algorithms, evaluation techniques, and applications. Adjustments can be made based on specific syllabus requirements and depth of coverage needed for each topic.
Thank you very much you saved my life very good explanation you are a legend
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Organizing the content in a structured manner according to a typical syllabus for machine learning can help in understanding the progression of topics. Here's a suggested organization based on common machine learning syllabi:
Machine Learning Syllabus Structure
1. Introduction to Machine Learning
Video 7: Introduction to Machine Learning Definition Examples Applications of ML Why so popular by Mahesh Huddar
2. Fundamentals and Concepts
Video 10: Concept Learning Concept Space Hypothesis Space Distinct Hypothesis Space Machine Learning Mahesh
Video 11, 12, 13: FIND S Algorithm - Solved Examples by Mahesh Huddar
Video 15: Consistent Hypothesis | Version Space | List Then Eliminate Algorithm by Mahesh Huddar
3. Learning Algorithms - Decision Trees
Video 25, 26, 27, 28: How to find Entropy Given Probabilities, Information Gain, Gain in terms of Gini Index, and How to find Entropy | Information Gain | Gain in terms of Gini Index | Decision Tree by Mahesh Huddar
Video 29, 30, 31, 32: ID3 Decision tree Learning Algorithm - Solved Numerical Examples by Mahesh Huddar
Video 36: How to Avoid Overfitting in Decision Tree Learning | Machine Learning | Data Mining by Mahesh Huddar
Video 37: How to handle Continuous Valued Attributes in Decision Tree | Machine Learning by Mahesh Huddar
4. Clustering Algorithms
Video 5: K Means Clustering Algorithm | K Means Solved Numerical Example Euclidean Distance by Mahesh Huddar
Video 92, 93: DBSCAN Clustering Algorithm Solved Numerical Example in Machine Learning Data Mining Mahesh Huddar
5. Neural Networks
Video 48, 49, 50: Perceptron Training Rule for AND, OR Gates | Artificial Neural Networks Machine Learning by Mahesh Huddar
Video 56, 57, 58, 59, 60: Back Propagation Algorithm Artificial Neural Network Algorithm Machine Learning by Mahesh Huddar
Video 51: Perceptron Rule to design XOR Logic Gate Solved Example ANN Machine Learning by Mahesh Huddar
6. Instance-based Learning and K-Nearest Neighbors
Video 106, 107, 108, 109: Solved Examples K Nearest Neighbors Algorithm in Machine Learning by Mahesh Huddar
Video 111: Locally Weighted Regression Algorithm Instance-based learning Machine Learning by Dr. Mahesh Huddar
7. Model Evaluation and Validation
Video 162, 163, 164: Confusion Matrix and Performance Metrics in Machine Learning by Mahesh Huddar
Video 172: K-Fold Cross Validation, Stratified K-Fold, Leave-one-out, Leave-P-Out Cross Validation by Mahesh Huddar
8. Text Classification and NLP
Video 90: Agglomerative Hierarchical Clustering Single link Complete link Clustering by Dr. Mahesh Huddar
Video 183: Sentiment Analysis using Dictionaries SentiWordNet SentiWords and VADER by Dr Mahesh Huddar
9. Dimensionality Reduction
Video 4: Principal Component Analysis | PCA | Dimensionality Reduction in Machine Learning by Mahesh Huddar
10. Association Rule Mining
Video 149, 150, 151: Solved Examples Apriori Algorithm to find Strong Association Rules Data Mining Machine Learning by Mahesh Huddar
11. Ensembling Techniques
Video 128: Ensemble Learning Techniques Voting Bagging Boosting Random Forest Stacking in ML by Mahesh Huddar
12. Miscellaneous Topics
Video 39: Splitting Continuous Attribute using Gini Index in Decision Tree Machine Learning by Mahesh Huddar
Video 180: Z-Score based Outlier or Anomaly detection and Removal in machine learning by Mahesh Huddar
This structure follows a typical progression from introductory concepts to more advanced topics, covering fundamental algorithms, evaluation techniques, and applications. Adjustments can be made based on specific syllabus requirements and depth of coverage needed for each topic.
Thank You
I will check and re-organise the content
Thank you so much sir for explaining so easily 😊
Most welcome
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Thank you so much sir.. i got it😍
Welcome 👍
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Thank you sir
Welcome
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sir can you arrange these lecture according to time wise
Kindly provide the link to the datasets
Big thanks!
Welcome
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what exactly is the difference between id3 and c4.5 algorithm except dividing with split_info?
id3 can only deal with discrete values whereas c4.5 is extended to also be able to deal with numerical values
No difference
There in ID3 we will not calculate split info and gain ratio here extra step is calculating gain ratio and split info
Sir id3 and c4.5 are different
Yes
w sir i got exam tmrw T_T
m
Thank you very much you saved my life good explanation you are a legend
Welcome
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@@MaheshHuddar What is the value of log2 here?
@@KuldeepSharma-tn9nw Its not a value, it is a function, specifically binary logarithm (log base 2)