GV

GeezerVison

The World Wide Web, restored to 800 pixels of glory.

Home|About|Gaming cabinet|Archives|How it works|Starter sites

On this site

  • Sign in
  • ActionsCI/CD and automation
  • CodespacesCloud development environments
  • All Features
  • Documentation
  • Training
  • Certifications
  • Build with Copilot
  • Customize Copilot
  • Integrations
  • Trending repos
  • GitHub Sponsors
  • Developer articles
  • Enterprises

This view

  • Original page
  • New address

GeezerVison is showing github.com — GitHub - edyoda/data-science-complete-tutorial: For extensive instructor led learning
BranchesTags
Open more actions menu

Latest commit

 

History

155 Commits
155 Commits

Folders and files

NameName
Last commit message
Last commit date
Data Wrangling
Data Wrangling
 
 
Data
Data
 
 
1. Introduction to Numpy.ipynb
1. Introduction to Numpy.ipynb
 
 
1. NumPy.ipynb
1. NumPy.ipynb
 
 
10. Feature Selection Techniques.ipynb
10. Feature Selection Techniques.ipynb
 
 
11. Nearest Neighbors.ipynb
11. Nearest Neighbors.ipynb
 
 
12. Clustering Techniques.ipynb
12. Clustering Techniques.ipynb
 
 
13. Anomaly Detection.ipynb
13. Anomaly Detection.ipynb
 
 
14. Support Vector Machines.ipynb
14. Support Vector Machines.ipynb
 
 
15. Dealing with Imbalanced Classes.ipynb
15. Dealing with Imbalanced Classes.ipynb
 
 
16. Ensemble Methods.ipynb
16. Ensemble Methods.ipynb
 
 
17. Association Rules.ipynb
17. Association Rules.ipynb
 
 
2. Pandas for Machine Learning.ipynb
2. Pandas for Machine Learning.ipynb
 
 
3. Plotting.ipynb
3. Plotting.ipynb
 
 
4. Linear Models for Classification & Regression.ipynb
4. Linear Models for Classification & Regression.ipynb
 
 
5. PreProcessing.ipynb
5. PreProcessing.ipynb
 
 
6. Decision Tree.ipynb
6. Decision Tree.ipynb
 
 
7. Naive Bayes.ipynb
7. Naive Bayes.ipynb
 
 
8. Composite Estimators using Pipelines & FeatureUnions.ipynb
8. Composite Estimators using Pipelines & FeatureUnions.ipynb
 
 
9. Model Selection & Evaluation.ipynb
9. Model Selection & Evaluation.ipynb
 
 
Altimetrik - 1.ipynb
Altimetrik - 1.ipynb
 
 
Cancer Prediction.ipynb
Cancer Prediction.ipynb
 
 
Case Study - IMDB dataset.ipynb
Case Study - IMDB dataset.ipynb
 
 
Case Study - Balancing Class.ipynb
Case Study - Balancing Class.ipynb
 
 
Case Study - Clustering, Outliers.ipynb
Case Study - Clustering, Outliers.ipynb
 
 
Case Study - Face Detection.ipynb
Case Study - Face Detection.ipynb
 
 
Case Study - Feature Selection on Store Data.ipynb
Case Study - Feature Selection on Store Data.ipynb
 
 
Case Study - Fraud Detection.ipynb
Case Study - Fraud Detection.ipynb
 
 
Case Study - HealthCare Data Analysis.ipynb
Case Study - HealthCare Data Analysis.ipynb
 
 
Case Study - House Pricing.ipynb
Case Study - House Pricing.ipynb
 
 
Case Study - Image Compression.ipynb
Case Study - Image Compression.ipynb
 
 
Case Study - Income Prediction.ipynb
Case Study - Income Prediction.ipynb
 
 
Case Study - Titanic Shipwrek prediction.ipynb
Case Study - Titanic Shipwrek prediction.ipynb
 
 
Case Study - Visa Status Prediction.ipynb
Case Study - Visa Status Prediction.ipynb
 
 
Case Study 1 - Employee Attrition Prediction.ipynb
Case Study 1 - Employee Attrition Prediction.ipynb
 
 
Case Study 2 - Review Sentiment Classification.ipynb
Case Study 2 - Review Sentiment Classification.ipynb
 
 
Case Study Churn Prediction.ipynb
Case Study Churn Prediction.ipynb
 
 
Case Study Clothe Identification.ipynb
Case Study Clothe Identification.ipynb
 
 
Case Study Handwriting Detection.ipynb
Case Study Handwriting Detection.ipynb
 
 
Case Study Insurance Amount Prediction.ipynb
Case Study Insurance Amount Prediction.ipynb
 
 
Case Study using Pipeline & Column Transformer.ipynb
Case Study using Pipeline & Column Transformer.ipynb
 
 
Column Transformer Example.ipynb
Column Transformer Example.ipynb
 
 
Comparing Regression.ipynb
Comparing Regression.ipynb
 
 
Day 1.ipynb
Day 1.ipynb
 
 
Day1-2.ipynb
Day1-2.ipynb
 
 
Grid Search.ipynb
Grid Search.ipynb
 
 
LR Example.ipynb
LR Example.ipynb
 
 
Mallicious Network Data Prediction.ipynb
Mallicious Network Data Prediction.ipynb
 
 
MlModel.py
MlModel.py
 
 
Naive Bayes, Pipelining, GridSearh, ColumnTransformer.ipynb
Naive Bayes, Pipelining, GridSearh, ColumnTransformer.ipynb
 
 
Online Learning.ipynb
Online Learning.ipynb
 
 
Project - Customer Churn Prediction.ipynb
Project - Customer Churn Prediction.ipynb
 
 
Project - Face Generation.ipynb
Project - Face Generation.ipynb
 
 
Project - Finding Similar Houses.ipynb
Project - Finding Similar Houses.ipynb
 
 
Project - Income Prediction.ipynb
Project - Income Prediction.ipynb
 
 
Project - Predicting Employee Exit.ipynb
Project - Predicting Employee Exit.ipynb
 
 
Project - Recommandation Engine.ipynb
Project - Recommandation Engine.ipynb
 
 
Project - Reducing colors in image.ipynb
Project - Reducing colors in image.ipynb
 
 
Project - Wardrobe Identification.ipynb
Project - Wardrobe Identification.ipynb
 
 
Project Neighbors & Clustering.ipynb
Project Neighbors & Clustering.ipynb
 
 
Project using Feature Selection.ipynb
Project using Feature Selection.ipynb
 
 
README.md
README.md
 
 
Sony - NumPy.ipynb
Sony - NumPy.ipynb
 
 
Sony - Pandas & Basic Visualization.ipynb
Sony - Pandas & Basic Visualization.ipynb
 
 
allstate_train.csv.zip
allstate_train.csv.zip
 
 
cic_0.01km.csv
cic_0.01km.csv
 
 
flask_ml_api.py
flask_ml_api.py
 
 
mymlproj.zip
mymlproj.zip
 
 
parkinsons.csv
parkinsons.csv
 
 

Repository files navigation

In person training - https://www.edyoda.com/program/data-scientist-program

Machine Learning Git Codebook

Lesson 1 : Introduction to Numpy (Video)
Lesson 2 : Data Wrangling using Pandas
Lesson 3 : Plotting in Python
Lesson 4 : Linear Models for Regression & Classification
Lesson 5 : Preprocessing Data
Lesson 6 : Decision Trees
Lesson 7 : Naive Bayes
Lesson 8 : Composite Estimators
Lesson 9 : Model Selection and Evaluation
Lesson 10 : Feature Selection Techniques
Lesson 11 : Nearest Neighbors
Lesson 12 : Clustering Techniques
Lesson 13 : Anomaly Detection
Lesson 14 : Support Vector Machines
Lesson 15 : Dealing with Imbalanced Classes
Lesson 16 : Ensemble Methods

Case Study of Classic ML Problems

Case 1 : Linear Regression
Case 2 : Cancer Prediction
Case 3 : Online Learning
Case 4 : Customer Churn Prediction
Case 5 : Income Prediction
Case 6 : Predicting Employee Exit
Case 7 : Face Generation
Case 8 : Finding Similar Houses

The Free courses available on EdYoda

Python - https://www.edyoda.com/course/98

Angular - https://www.edyoda.com/course/1227

Machine Learning - https://www.edyoda.com/course/1416

Dog Breed Prediction Project - https://www.edyoda.com/course/1336

AI Project - Web application for Object Identification - https://www.edyoda.com/course/1185

Numpy - https://www.edyoda.com/course/1263

Tensorflow - https://www.edyoda.com/course/99

Amazon Web Services - https://www.edyoda.com/course/1410

DevOps - https://www.edyoda.com/course/100

Android -
https://www.edyoda.com/course/101
https://www.edyoda.com/course/1173

Deep Reinforcement Learning - https://www.edyoda.com/course/1421

Knowledge Graphs, Deep Learning, Reasoning - https://www.edyoda.com/course/1420

Natural Language Processing - https://www.edyoda.com/course/1419

GAN Miniseries - https://www.edyoda.com/course/1418

Implementing Java Api's work - https://www.edyoda.com/channel/2398/

Introduction to Neural Nets - https://www.edyoda.com/channel/2500/

Videos from deep cognition studio - https://www.edyoda.com/channel/2380/

About Us

We want to democratize education and create free quality course content.

About

For extensive instructor led learning

www.edyoda.com/micro-degree/generative-ai-and-ai-agent-career-track-micro-degree

Topics

decision-treesfeature-selectionlinear-regressionmachine-learningnearest-neighborsnumpypandaspipelinescikit-learn

Resources

Readme
Activity
Custom properties

Stars

1.8k stars

Watchers

64 watching

Forks

771 forks
Report repository

Releases

Packages

Used by

Contributors

Languages
You are visitor 01402503 — GeezerVison does not store the pages you look up.
Best viewed with Netscape Navigator 4.7 or Internet Explorer 6 at 800×600. No cookies required.