100% Off Coupon - 2022 Machine Learning A to Z : 5 Machine Learning 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.
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.

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