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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
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