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Explore sklearn

 

Explore sklearn

This course is intended to give the student an overview of Python's machine learning library, sklearn. The course is broken down into seven ...

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What you'll learn

  • Students will learn about sklearn, Python's machine learning library
  • Students will learn about and go over the code of supervised learning classification and regression problems
  • Students will learn about and go over the code of semi-supervised classification and regression problems
  • Students will learn about and go over the code of unsupervised regression problems
  • Students will learn about and go over the code of principal component analysis
  • Students will learn about and go over the code of feature selection techniques

About The Course Explore sklearn

This course is intended to give the student an overview of Python's machine learning library, sklearn. The course is broken down into seven sections, being:-

1. Introduction

2. Supervised learning

3. Semi-supervised learning

4. Unsupervised learning

5. Dimensionality reduction

6. Feature selection

7. Other topics

The student will receive extensive guidance on how to use sklearn. Sklearn's search engine will be used to research sklearn's many functions, which include:-

1. Preprocessing functions

2. Classification models

3. Regression models

4. Semi-supervision models

5. Clustering models

6. Dimensionality reduction functions

7. Feature selection functions

8. Metrics functions

In addition to learning about the numerous and varied types of functions in sklearn, The student will go over the code of twelve Jupyter Notebooks. The subject matter of these notebooks are:-

1. Supervised classification problems

2. Supervised regression problems

3. Semi-supervised classification problems

4. Semi-supervised regression problems

5. Unsupervised classification problems

6. Dimensionality reduction

7. Feature selection by selecting the best features

8. Feature selection by selecting a percentage of the best features

9. Logistic regression versus decision tree

10. The machine learning life cycle

The student will, using sklearn and other coding, cover the entire machine learning life cycle from the beginning to the end. This will cover:-

1. Creating a Jupyter Notebook in Google Colab

2. Importing Python libraries into the Jupyter Notebook

3. Loading the dataset from either sklearn, openml, or Github

4. Cleaning the data by taking care of any null values

5. Encoding the data to covert object features to numeric features

6. Using visualisation techniques to analyse the data

7. Removing any outliers from regression models where necessary

8. Removing any feastures that have a high correlation where necessary

9. Reducing the dimensionality of a dataset where necessary

10. Reducing the features of a dataset where necessary

11. Assigning dependent and independent variables

12. Splitting the dataset into training and validation sets where necessary

13. Selecting the most appropriate model

14. making predictions on the model

15. Analysing the accuracy of the model by using metric functions

Online Course CoupoNED based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.