Machine Learning Bootcamp in Python with 5 Capstone Projects

Machine Learning Bootcamp in Python with 5 Capstone Projects | Udemy
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Genre: eLearning

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

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 expects.

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 the 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

Who this course is for:
Anyone who want to start a career in Machine Learning.
Students who have at least knowledge in linear algebra, calculus, statistics, probability and who want to start their journey in Machine Learning.
Any people who want to level up their Machine Learning Knowledge.
Software developers or programmers or Tech lover who want to change their career path to machine learning.
Technologists who are curious about how Machine Learning works in the real world.
Anyone who has already started their data science journey and now want to master in machine learning.
If you have no prior coding or scripting experience, This course is completely for you. This Course also includes Python Fundamental for beginners.

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