Grasp Machine Learning Algorithms and Fashions in Python with hands-on Projects in Knowledge Science. Code workbooks included.
What you’ll study
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
PCA & LDA
Content material and Collaborative based mostly filtering
Singular Worth Decomposition
Completely different algorithms used for Time Collection forecasting
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.