100% Off Coupon - Machine Learning in Python with 5 Machine Learning Projects

What you’ll learn

Theory and practical implementation of linear regression using sklearn
Theory and practical implementation of logistic regression using sklearn
Feature selection using RFECV
Data transformation with linear and logistic regression.
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
Variance Filtering
PCA & LDA
Content and Collaborative based filtering
Singular Value Decomposition
Different algorithms used for Time Series forecasting
Case studies

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