Machine Learning in Python Bootcamp with 5 Capstone Projects

Machine Learning in Python Bootcamp with 5 Capstone 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