Using small validation set for different models to be trained and evaluate them and find the best performance model to use in production. When your data is enormous, splitting your data into multiple servers is map reducing. What you did is to split the population into strata. If you want to make a prediction in that country, you should select 54 males and 46 females.

If you expect from data set to be representative of the population.ДЇor example, if the population distribution of your country is, % 54 male people and % 46 female. It is a way to change hyperparameters manually. It defines the values of the model parameters, like learning rate, and helps you to find the best performance model Grid Search Limiting the model to be simple for avoiding overfitting. If your model parameters are related to each other, you can merge them into one, for example, if you are handling housing data, how many rooms exist in your home per square meter? Regularization I can easily say label or response variable however I try to explain it like you are a complete stranger about that topic. When you say best parameters that means the most correlated to your value that you want to predict. Feature EngineeringД¬ombining features to find the best parameters for your model. When your data is large but your model can not perform well. When your data is small, your model might not be performed well. So let's skip the chit chat and let's dig into terms.

In two scenarios, you can take benefit from this article too.ДЄlso for junior developers & entry-level Data Scientists & Data Analysts who want to be Data Scientists, this article also helps you to be familiar with these machine learning terms.ДЄctually, I would like to add codes too, however that won't be briefly, so I am now prepared E-Book containing real-life examples and Python codes, will be ready I think in July, and I will add links after the articles.ДЄnd I have to thank you too due to your attention to my A-Z articles about Machine Learning, your feedback really motivates me to keep going. Of course, Machine Learning is a really big concept, I explained some of the terms briefly as I mentioned in the first part of this article, this article aims to refresh your memory, before an interview or before doing the project. After publishing the вЂњMachine Learning A-Z Briefly ExplainedвЂќ article, I realized that there are many terms that I did not mention.

Terms в€ Dimensionality Reduction в€ Supervised Learning Algorithms в€ Unsupervised Learning Algorithms в€ Association Rule Learning в€ Reinforcement Learning в€ Batch Learning в€ Online Learning в€ Learning Rate в€ Instance-Based Learning в€ Model-based Learning в€ Model Parameters в€ Sampling Noise в€ Sampling Bias в€ Feature Engineering в€ Feature Extraction в€ Regularization в€ Hyperparameter в€ Grid Search в€ Stratified Sampling в€ Map- Reduce в€ Cross-validation в€ Randomized Search.

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