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Course Types
Bigdata
301.1.3-Uses of Big Data
301.4.7-Joins
301.4.6-Filter & Sorting
301.4.4-Functions
301.4.3-Pig Latin Basic Operations
301.4.5-Group By
301.4.2-Pig Architecture, Data Types and Relation
301.1.8-Lab of Hadoop
301.1.7-Basics of Hadoop
301.1.6-Basics of MapReduce
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
Predictive Modeling & Machine Learning
Python
204.7.9 Boosting Conclusion
204.7.8 Practice : Boosting
204.7.7 Boosting
204.7.6 Practice : Random Forest
204.7.5 The Random Forest
204.7.4 The Bagging Algorithm
204.7.3 Types of Ensemble Models
204.7.2 Ensemble Models
204.7.1 Random Forests and Boosting : Wisdom of Crowd
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
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
Programming and Reporting
Quiz
10th November 2020
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