Journey of a Machine Learning and Self-Development Enthusiast.

5 prospective AI Technologies

Hi, TheMLGuy here, today I want to share in my opinion 5 of the most cool and prospecitve applications of AI in real Products and Technologies! Topics for today: 1. AlphaFold 2. AlphaZero 3. Github Copilot 4. Netflix Recommendation System 5. OpenAI 5 ALphaFold Proteins are molecules made up of

Machine Learning

What Is Reinforcement Learning? 🤖

Reinforcement Learning (RL) is an area of Machine Learning where the model is trained to make a sequence of decisions under different conditions. Let’s see an example: Let’s imagine that we have a robot vacuum that cleans the floor in the apartment. For the robot to have an

Time Management

Perfect Workspace and Best Notes with Notion

Notion is an application to take Notes. It Sounds simple, like any other application to take notes, but the interesting part is how far you can go using Notion and how big you can build using this tool. 💡 Hint: You can do almost everything here. Check the image below. But

Random

Drawing Images with Fourier Series

Before I begin explaining what the hell is Fourier Series let me show you the magic that it can do ✨. Pretty cool huh? GitHub - tokyo-s/FourierDrawings: This is one of most interesting projects that i made so far, and it is about a math concept that is called, Fourier

Machine Learning

Whiten your data with imperio ZCATransformer

Feature engineering is the process of transforming your input data in such a way that it will be more representative of the Machine Learning Algorithms. However, it is very often forgotten because of the inexistence of an easy-to-use package. That’s why we decided to create the one — imperio, the

Machine Learning

Whiten your data with imperio WhiteningTransformer

Feature engineering is the process of transforming your input data in such a way that it will be more representative of the Machine Learning Algorithms. However, it is very often forgotten because of the inexistence of an easy-to-use package. That’s why we decided to create the one — imperio, the

Machine Learning

Transform your categorical columns with imperio SmoothingTransformer

Feature engineering is the process of transforming your input data in such a way that it will be more representative of the Machine Learning Algorithms. However, it is very often forgotten because of the inexistence of an easy-to-use package. That’s why we decided to create the one — imperio, the

Machine Learning

Split your data into clusters with imperio ClusterizeTransformer

Feature engineering is the process of transforming your input data in such a way that it will be more representative of the Machine Learning Algorithms. However, it is very often forgotten because of the inexistence of an easy-to-use package. That’s why we decided to create the one — imperio, the

Machine Learning

Using Crucio SMOTENC for unbalanced datasets

Unbalanced data In simple terms, an unbalanced dataset is one in which the target variable has more observations in one specific class than the others. An example can be a dataset for transactions: usually, 99% of this set consists of simple, not fraudulent transactions, and only 1% of them are

Machine Learning

Using Crucio SMOTE and Clustered Undersampling Technique for unbalanced datasets

Unbalanced data In simple terms, an unbalanced dataset is one in which the target variable has more observations in one specific class than the others. An example can be a dataset for transactions: usually, 99% of this set consists of simple, not fraudulent transactions, and only 1% of them are

Machine Learning

Balancing datasets using Crucio Safe-Level-SMOTE

Unbalanced data In simple terms, an unbalanced dataset is where the target variable has more observations in one specific class than the others. An example can be a dataset for transactions: usually, 99% of this set consists of simple, not fraudulent transactions, and only 1% of them are fraud. What

Machine Learning

Using Crucio SMOTEENN for balancing data

This article will be about a combination of the two most powerful algorithms used for oversampling and undersampling imbalanced datasets, SMOTE and ENN. I talked about SMOTE in one of my previous articles, so you should check it out because I will now rely more on the ENN algorithm. How

Machine Learning

Using Crucio SMOTETOMEK for balancing data

This article will be about a combination of the two most powerful algorithms used for oversampling and undersampling imbalanced datasets, SMOTE and Tomek Links. I talked about SMOTE in my previous article, so you should check it out because I will now rely more on the Tomek Links algorithm. So

Machine Learning

Using Crucio SMOTE for balancing data

This will be a new series of articles related to our brand new Python library made with love by Sigmoid. Its name is just like the second unforgivable spell from the Harry Potter series, Crucio. This library was created specifically for unbalanced data sets, with a lot of different methods

Machine Learning

Using Kydavra ElasticNetSelector for feature selection

Even if we don’t leave in the world of Harry Potter, now everyone can learn the unforgivable spells to use some not easy algorithms just by typing 3 lines of code. This can be done by using the Kydavra library for python. Using Kydavra ElasticNetSelector. To install Kydavra we

Machine Learning

Using Kydavra MixerSelector for feature selection

Usually, when we try to select the best features of the dataset, we choose different spells(algorithms) to see a difference, and select the best one, so why don’t we try to mix them all up and combine the power them all in one spell. This is where the

News

Hello World

Welcome to My Blog! As you might already read from the About page, my name is Vladimir Stojoc, I am 21 years old, a student at the Technical University of Moldova, a Data Scientist at Entangled AI, and Chief Technology Officer at Sigmoid, but let's talk about everything