Pumalin @PumalinML

AdaBoost - Explained ⚡️A new series of three articles about AdaBoost:⚡️ 1. General Explanation 2. Detailed Example for Classification 3. Detailed Example for Regression #AdaBoost #MachineLearning #DataScience #trees datamapu.com/posts/classica…

⚡️Do you want to learn about Random Forests?⚡️ check the article below 👇 #MachineLearning #DataScience #RandomForest #DecisionTree Random Forests - Explained datamapu.com/posts/classica…

In Ensemble Learning multiple Machine Learning models are combined into one single prediction to improve the predictive skill. ⚡️Check the new blog post for an illustration of the main ensemble methods⚡️ #MachineLearning #DataScience #Ensemble datamapu.com/posts/ml_conce…

New blog post: Bias-Variance Tradeoff 🎯 For a Machine Learning model to be generalizable to new unseen data with high predictive skill, it is important that bias and variance are balanced. #MachineLearning #DataScience #Python #learning #Bias #Variance datamapu.com/posts/ml_conce…

#2023 #NewYear2024 #goodbye2023hello2024 #math #maths

#Python #Plotting #3d #MerryChristmas

#MachineLearning #DataScience #LogisticRegression #Classification #Python

datamapu @datamapu

5 months ago

💡New blogpost about logistic regression. 💡 Explaining the connection to Linear Regression, binary and multinomial Logistic Regression. Check the full article here 👇 datamapu.com/posts/classica…

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So happy to continue building a Data Science and Machine Learning blog. Starting with the basics: Linear Regresion 📈 Linear Regression is a powerful and interpretable ML model, that's why it is very popular in academia and business. datamapu.com/posts/2023/202…

A regression predicts a continuous variable (e.g. speed), while in classification, the data is classified into discrete classes (e.g. cats and dogs). We distinguish between binary and multi-class classification. #MachineLearning #DataScience #regression #classification

Dictionaries are used to store data in key-value pairs. They make a ordered storage of data possible. This is how you can merge two dictionaries in Python 👇👇👇 #python #programming #Coding #computerscience #datascience #machinelearning

Do you want to know what Deep Learning is, how it is distinguished between AI and ML, and it's main principles? ⚙️🧠 Find an introduction here 👇 datamapu.com/posts/2023/202… #deeplearning #MachineLearning #ArtificialIntelligence

#PythonCodeSnippet If you want to check if an object is of a specific type, you can use isinstance(). isinstance(object, class) returns True, if 'object' is of type or a subclass of 'class'. In this example, we check if 'items' is of type 'list'. #pythonprogramming #code

Supervised and Unsupervised Learning are two different types of ML. The main difference is that supervised learning needs labes, while unsupervised learning only uses the data itself to find patterns. #MachineLearning #Supervised #Unsupervised 👇 datamapu.com/posts/2023/202…

Mean Absolute Error (MAE) and Mean Squared Error (MSE) are two typical metrics for regresion problems. The main difference is that MSE emphazises large errors as illustrated in the below image. #Regression #mse #mae #MachineLearning

Depending on which problem you want to solve, different metrics should be considered. Find an overview about metrics for classification problems and when to use them here📊📈👇 datamapu.com/posts/2023/202… #DataScience #MachineLearning #classification #metrics

After discussing metrics for classification, now an article summarizing the most common metrics for regression problems. 📈👇 #DataScience #Regression #MachineLearning #Metrics datamapu.com/posts/2023/202…

Recall is a metric to measure the proportion of True Positives to all actual positive values. Here it is calculated using sklearn, Python and by hand. datamapu.com/posts/2023/202…

Accuracy is probably the most known metric for classification. But there are much more and depening on your problem other metrics might be more suitable. Find an overview about the most common metrics for classification problems and when to apply them 👇 datamapu.com/posts/2023/202…

So happy to share that the first post is online! 🎉This article is talking about all the different parts within a Data Science project and that this is much more than modelling.📊 Find all the steps in the blogpost about the Data Science Lifecycle. 👇 datamapu.com/posts/2023/202…

Are you tired of learning data science theory without any practice? Our study group is your chance to get hands-on experience and practice every day! Why joining? Find details in 🧵