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
How to Enroll Machine Learning in Python with 5 Machine Learning Projects course?
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