Machine Learning in Python Bootcamp with 5 Capstone 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

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 Series forecasting

Case studies

Requirements

To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language.

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.

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 Series forecasting.

20. Case studies

We have covered each and every topic in detail and also learned applying them to real world problems.

There are lots and lots of exercises for you to practice and also a 5 bonus capstone project "Employee Promotion Prediction", "Predicting Medical Health Expenses", "Deteing Status for Loan Applicants" and "Optimizing Crop Production".

In this Employee Promotion Prediction project, you will learn how to Implement a Predictive Model for Identifying the Right Employees deserving Promotion. Also learn how to balance Imbalanced Datasets.

In this Predicting Medical Health Expenses project, you will learn how to Implement a Regression Analysis Predictive Model for Predicting the Future Medical Expenses for People using Linear Regression, Random Forest, Gradient Boosting, etc.

In this Deteing Status for Loan Applicants project, you will learn how to Implement a Classification Analysis Predictive Model for Deteing whether a Person should be Granted Loan or Not.

In this Optimizing Crop Production project, you will learn about Precision Fag using Data Science Technologies such as Clustering Analysis and Classification Analysis. You will be able to Recommend best Crops to Farmers to Increase their Productivity.

You will make use of all the topics read in this course.

You will also have access to all the resources used in this course.

Enroll now and become a master in machine learning.

Who this course is for:

Anyone who has already started their data science journey and now wanting to master machine learning.

This course is for machine learning bners as well as intermediates.

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