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2022 Machine Learning A to Z : 5 Machine Learning Projects

#artificialintelligence

Evaluation metrics to analyze the performance of models Industry relevance of linear and logistic regression Mathematics behind KNN, SVM and Naive Bayes algorithms Implementation of KNN, SVM and Naive Bayes using sklearn Attribute selection methods- Gini Index and Entropy Mathematics behind Decision trees and random forest Boosting algorithms:- Adaboost, Gradient Boosting and XgBoost Different Algorithms for Clustering Different methods to deal with imbalanced data Correlation Filtering Content and Collaborative based filtering Singular Value Decomposition Different algorithms used for Time Series forecasting Hands on Real-World examples. To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language. To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language. This course is a perfect fit for you. This course will take you step by step into the world of Machine Learning.