/ML_Process_Course

Public repo for the 365 Data Science ML Process Course

Primary LanguageJupyter Notebook

ML Process Course

This is the public repository for the ML Process Course (https://365datascience.com/learn-machine-learning-process-a-z/). In this course, we take you through the end-to-end process of building a Machine Learning Model. We did not build this course ourselves. We stood on the shoulders of giants. We think its only fair to credit all the resources we used to build this course, as we could not have created this course without the help of the ML community.

Table of Contents

  1. Coding Workbooks for Each Course
  2. Data Science Blogs
  3. Applying ML
  4. Problem Framing
  5. Data Collection
  6. Data Preprocessing
  7. Exploratory Data Analysis
  8. Feature Engineering
  9. Cross Validation
  10. Feature Selection
  11. Imbalanced Data
  12. Modeling
  13. Model Evaluation

Coding Workbooks for Each Course

Google Colab Kaggle Workbook
5-Missing Values, 5-Outliers 5-Missing Values, 5-Outliers
6-EDA 6-EDA
7-Categoricals, 7-Continuous 7-Categoricals, 7-Continuous
8-Cross Validation 8-Cross Validation
9-Feature Selection 9-Feature Selection
10-Imbalanced Data 10-Imbalanced Data
11-Model Selection 11-Model Selection
12-Model Evaluation Classification, 12-Model Evaluation Regression 12-Model Evaluation Classification, 12-Model Evaluation Regression

Data Science Blogs

2. Applying ML

3. Problem Framing

4. Data Collection

5. Data Preprocessing

6. Exploratory Data Analysis

7. Feature Engineering

Categorical Feature Engineering

Continuous Feature Engineering

8. Cross Validation

9. Feature Selection

10. Imbalanced Data

11. Modeling

Hyperparameter Tuning

Ensembling

12. Model Evaluation

12. Model Productionization