Giveaway [Udemy] Machine Learning in Python Bootcamp with 5 Capstone Projects

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Machine Learning in Python Bootcamp with 5 Capstone Projects | Udemy

Description
Crazy about Data science and Machine learning?
This course is a perfect fit for you.
This course will take you step by step into the world of Machine learning.
Machine learning is the study of computer algorithms that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Machine learning is actively being used today, perhaps in many more places than one world expect.
It contains a lot of topics and this course will cover all step by step.
this course will give you theoretical as well as practical knowledge of machine learning.
This course is fun as well as exciting.
It will cover all common and important algorithms and will give you experience of working on some real world projects.
This course will cover the following topics:-
1. Theory and practical implementation of linear regression using sklearn.
2. Theory and practical implementation of logistic regression using sklearn.
3. Feature selection using RFECV.
4. Data transformation with linear and logistic regression.
5. Evaluation metrics to analyze the performance of models
6. Industry relevance of linear and logistic regression.
7. Mathematics behind KNN, SVM and Naive Bayes algorithms.
8. Implementation of KNN, SVM and Naive Bayes using sklearn.
9. Attribute selection methods- Gini Index and Entropy.
10. Mathematics behind Decision trees and random forest.
11. Boosting algorithms:- Adaboost, Gradient Boosting and XgBoost.
12. Different algorithms for clustering
13. Different methods to deal with imbalanced data.
14. Correlation filtering
15. Variance filtering
16. PCA & LDA
17. Content and Collaborative based filtering
18. Singular Value decomposition
19. Different algorithms used for Time Series forecasting.
20. Case studies
We have covered each and every topic in detail and also learned applying them to real world problems.

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