Ensemble Methods in Machine Learning: Voting Techniques

This is where Ensemble Methods come in handy! Rather than just relying on one Decision Tree and hoping we made the right decision at each split, Ensemble Methods allow us to take a sample of Decision Trees into account, calculate which features to use or questions to ask at each split, and make a final predictor based on the aggregated results of the sampled Decision Trees.

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Learn Machine Learning here with ease from basics. Many examples are discussed to explain topic with clear understanding.
This section contains following topics in detail:
* What is Machine Learning?
* Machine Learning- Analytics
* Descriptive Analytics
* Predictive Analytics
* Prescriptive Analytics
* Relation between Artificial Intelligence and Machine Learning
* Importance of Machine Learning
* Evolution of Machine Learning
* Benefits of Machine Learning
* Real Life Examples of Machine Learning
* Types of Machine Learning
* Supervised Learning
* Unsupervised Learing
* Classification
* Classification Examples
* Technique to solve Classification Problems
* Decision Tree
* Formula of Entropy
* k- Nearest Neighbor(kNN) Algorithm
* Support Vector Machine(SVM)
* How Support Vector Machine Works?
* What is best Hyperplane?
* How to separate Vectors?
* Kernel Function in Support Vector Machine(SVM)
* Regression
* Types of Regression
* Linear Regression
* How to determine Weights and Bias?
* Logistic Regression
* Ensemble
* About Ensemble Method
* Ensemble by Voting
* Boosting
* Random Forest
* Association Analysis
* Key Metrics in Associaton Analysis
* Support- Association Rule
* Confidence- Association Rule
* Lift- Association Rule
* Apriori Algorithm
* Mining Rules
* Clustering
* Types of Clustering Techniques
* Types of Cluster
* Implementation of Clustering
* Process of Finding Clusters
* k means Clustering
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