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Programming and Reporting
Python
104.3.6 Creating Graphs in Python
104.3.5 Box Plots and Outlier Detection using Python
104.3.4 Percentiles & Quartiles in Python
104.3.3 Dispersion Measures in Python
104.3.2 Descriptive Statistics : Mean and Median
104.3.1 Data Sampling in Python
104.2.8 Joining and Merging datasets in Python
104.2.7 Identifying and Removing Duplicate values from dataset in Python
104.2.6 Sorting the data in python
104.2.5 Subsetting data with variable filter condition in Python
Course Types
Predictive Modeling & Machine Learning
204.6.9 Digit Recognition using SVM
203.7.9 Boosting Conclusion
203.7.8 Practice : Boosting
203.7.7 Boosting
203.7.6 Practice : Random Forest
203.7.5 The Random Forest
203.7.4 The Bagging Algorithm
203.7.3 Types of Ensemble Models
203.7.2 Ensemble Models
203.7.1 Random Forests and Boosting : Wisdom of Crowd
Bigdata
301.1.1-Introduction To Bigdata
301.1.2-Big Data
301.1.3-Uses of Big Data
301.1.4-Handling Big Data
301.1.5-MapReduce and Network Programming
301.1.6-Basics of MapReduce
301.1.7-Basics of Hadoop
301.1.8-Lab of Hadoop
301.2.1-Map Reduce – Basics
301.2.2-Map Reduce for word count
Programming and Reporting
Predictive Modeling & Machine Learning
Python
204.7.1 Random Forests and Boosting : Wisdom of Crowd
204.7.2 Ensemble Models
204.7.3 Types of Ensemble Models
204.7.4 The Bagging Algorithm
204.7.5 The Random Forest
204.7.6 Practice : Random Forest
204.7.7 Boosting
204.7.8 Practice : Boosting
204.7.9 Boosting Conclusion
204.6.8 SVM : Advantages Disadvantages and Applications
R
204.6.9 Digit Recognition using SVM
203.7.9 Boosting Conclusion
203.7.8 Practice : Boosting
203.7.7 Boosting
203.7.6 Practice : Random Forest
203.7.5 The Random Forest
203.7.4 The Bagging Algorithm
203.7.3 Types of Ensemble Models
203.7.2 Ensemble Models
203.7.1 Random Forests and Boosting : Wisdom of Crowd
Quiz
10th November 2020
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