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Machine Learning in Python Bootcamp with 5 Capstone Projects

Grasp Machine Learning Algorithms and Fashions in Python with hands-on Projects in Knowledge Science. Code workbooks included.

What you’ll study

(*5*)

  • Idea and sensible implementation of linear regression utilizing sklearn
  • Idea and sensible implementation of logistic regression utilizing sklearn
  • Function choice utilizing RFECV
  • Knowledge transformation with linear and logistic regression.
  • Analysis metrics to investigate the efficiency of fashions
  • Trade relevance of linear and logistic regression
  • Arithmetic behind KNN, SVM and Naive Bayes algorithms
  • Implementation of KNN, SVM and Naive Bayes utilizing sklearn
  • Attribute choice methods- Gini Index and Entropy
  • Arithmetic behind Resolution bushes and random forest
  • Boosting algorithms:- Adaboost, Gradient Boosting and XgBoost
  • Completely different Algorithms for Clustering
  • Completely different strategies to deal with imbalanced knowledge
  • Correlation Filtering
  • Variance Filtering
  • PCA & LDA
  • Content material and Collaborative based mostly filtering
  • Singular Worth Decomposition
  • Completely different algorithms used for Time Collection forecasting
  • Case research
  • Requirements

    • To make sense out of this course, you ought to be effectively conscious of linear algebra, calculus, statistics, chance and python programming language.

    Who this course is for:

    • Anybody who has already began their knowledge science journey and now eager to grasp machine studying.
    • This course is for machine studying newcomers in addition to intermediates.


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