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.
- Implementation of Correlation Filtering.
- Implementating Variance Filtering.
- Implementation of PCA & LDA.
- Implementation of Content and Collaborative based filtering.
- Implementing Singular Value Decomposition.
- Implementation of Different algorithms used for Time Series forecasting.
- Case studies.
- Hands on Real-World examples.
How to Enroll 2022 Machine Learning A to Z : 5 Machine Learning Projects course?
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