Machine Learning Bootcamp in Python with 5 Capstone Projects: Learn how to use Machine Learning with Python. Build 5 Complete Machine Learning Real World Projects with Python.
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 This course includes:
25.5 hours on-demand video 2 articles 207 downloadable resources Full lifetime access Access on mobile and TV Certificate of completion Requirements:
To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability, and python programming language.
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