List: ML fundamentals | Curated by Nathan Hanks

Jun 23, 2023
92 stories
ML fundamentalsThe core principles of ML
In
TDS Archive
by
John Adeojo
Building AI Strategies for BusinessesThe art of crafting an AI strategy through Wardley Maps
Jun 6, 2023
9
Jun 6, 2023
9
In
TDS Archive
by
Vyacheslav Efimov
Quantile Loss & Quantile RegressionLearn how to adjust regression algorithms to predict any quantile of data
Jan 28, 2023
1
Jan 28, 2023
1
In
Towards AI
by
Bex T.
19 Most Elegant Sklearn Tricks I Found After 3 Years of UseAdvanced techniques and hidden gems for effective machine learning
Jun 16, 2023
5
Jun 16, 2023
5
In
TDS Archive
by
Baijayanta Roy
All about Feature ScalingScale data for better performance of Machine Learning Model
Apr 6, 2020
12
Apr 6, 2020
12
In
TDS Archive
by
Jeff Hale
Scale, Standardize, or Normalize with Scikit-LearnWhen to use MinMaxScaler, RobustScaler, StandardScaler, and Normalizer
Mar 4, 2019
22
Mar 4, 2019
22
Roy Ravid
Have you heard about “Tweedie” Loss?Using “Tweedie” Loss in order to improve LTV predictions
Jul 7, 2019
4
Jul 7, 2019
4
In
TDS Archive
by
Wenbo Shi
Tweedie Loss Function for Right-Skewed DataA loss function for data with massive zeros and long tail
Mar 18, 2020
5
Mar 18, 2020
5
In
TDS Archive
by
Nicolas Vandeput
Forecast KPI: RMSE, MAE, MAPE & BiasThe article below is an extract from my book Data Science for Supply Chain Forecast, available here. You can find my other articles here:
Jul 5, 2019
8
Jul 5, 2019
8
In
TDS Archive
by
Practicus AI
Understanding the 3 most common loss functions for Machine Learning RegressionA loss function in Machine Learning is a measure of how accurately your ML model is able to predict the expected outcome i.e the ground…
May 20, 2019
8
May 20, 2019
8
In
TDS Archive
by
Sarang Narkhede
Understanding AUC - ROC Curve
Jun 26, 2018
80
Jun 26, 2018
80
Maria Gusarova
Feature Selection TechniquesIn this article, we will review feature selection techniques and answer questions on why this is important and how to achieve it in…
Sep 1, 2022
2
Sep 1, 2022
2
Maria Gusarova
Logistic Regression. Detailed Overview for Fintech challengesThis article is about Logistic Regression, How does it work, the benefits and challenges of logistic regression, Solving Fintech challenges…
Sep 6, 2022
Sep 6, 2022
In
FinTechExplained
by
Farhad Malik
Classification Model Evaluation For Data ScientistsExplaining How To Evaluate Classification Model In Data Science
Jun 24, 2020
Jun 24, 2020
Maria Gusarova
Revolutionize Your Machine Learning Models with These XGBoost Hyperparameters — Boost Your Model…Learn how XGBoost works, its comparison with Decision Trees and Random Forest, the difference between boosting and bagging, hyperparameter…
Dec 23, 2022
3
Dec 23, 2022
3
In
TDS Archive
by
Vinícius Trevisan
Target-encoding Categorical VariablesOne nice alternative to One-hot encoding your categories
Mar 17, 2022
3
Mar 17, 2022
3
In
vickdata
by
Rebecca Vickery
Feature Transformation for Machine Learning, a Beginners GuideA walkthrough of my approach to feature transformation for machine learning.
Sep 7, 2018
7
Sep 7, 2018
7
In
TDS Archive
by
Aditya Lahiri
Dealing With Class Imbalanced Datasets For Classification.Skewed datasets are not uncommon. And they are tough to handle. Usual classification models and techniques often fail miserably when…
Dec 15, 2018
3
Dec 15, 2018
3
In
TDS Archive
by
Kacper Kubara
Why using a mean for missing data is a bad idea. Alternative imputation algorithms.We all know the pain when the dataset we want to use for Machine Learning contains missing data. The quick and easy workaround is to…
Jun 24, 2019
2
Jun 24, 2019
2
In
Towards AI
by
Claudia Ng
Ensemble Methods Explained in Plain English: BaggingUnderstand the intuition behind bagging with examples in Python
Mar 28, 2021
Mar 28, 2021
In
GrabNGoInfo
by
Amy @GrabNGoInfo
Bagging vs Boosting vs Stacking in Machine LearningData Science Interview Questions and Answers
Nov 24, 2022
1
Nov 24, 2022
1