Data Science & Machine Learning

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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data

Red:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India4 219 Educación2 107...

📈 Análisis del canal de Telegram Data Science & Machine Learning

El canal Data Science & Machine Learning (@datasciencefun) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 75 837 suscriptores, ocupando la posición 2 107 en la categoría Educación y el puesto 4 219 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 837 suscriptores.

Según los últimos datos del 22 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 728, y en las últimas 24 horas de -2, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.00%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.05% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 2 278 visualizaciones. En el primer día suele acumular 794 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
Intereses temáticos: El contenido se centra en temas clave como learning, accuracy, distribution, panda, dataset.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 23 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.

75 837

Suscriptores

-224 horas

+637 días

+72830 días

2 278

Visitas de la publicación

~ 79424 horas

~ 1 00048 horas

3.00%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

beta

Archivo de publicaciones

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👉A handy notebook on handling missing values Link : 👇👇 https://www.kaggle.com/parulpandey/a-guide-to-handling-missing-values-in-python A list of NLP Tutorials Link : 👇👇 https://github.com/lyeoni/nlp-tutorial “An Implementation and Explanation of the Random Forest in Python” by Will Koehrsen 👇👇 https://link.medium.com/GCWFv81v95 “How to analyse 100s of GBs of data on your laptop with Python” by Jovan Veljanoski 👇👇 https://link.medium.com/V8xS82Cax6

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Scatter plot is used to?

Anonymous voting

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Recall how many of the true positives were recalled (found), i.e. how many of the correct hits were also found. Its formula would be

Anonymous voting

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Precision is one indicator of a machine learning model's performance – the quality of a positive prediction made by the model. Its formula would be?

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Type-2 error is?

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Type-1 Error is?

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Seeing Theory : A visual introduction to probability and statistics Link :👇👇 https://seeing-theory.brown.edu/ “The Projects You Should Do to Get a Data Science Job” by Ken Jee 👇👇 https://link.medium.com/Q2DnxSGRO6

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👉The Ultimate Guide to the Pandas Library for Data Science in Python 👇👇 https://www.freecodecamp.org/news/the-ultimate-guide-to-the-pandas-library-for-data-science-in-python/amp/ A Visual Intro to NumPy and Data Representation . Link : 👇👇 https://jalammar.github.io/visual-numpy/ Matplotlib Cheatsheet 👇👇 https://github.com/rougier/matplotlib-cheatsheet SQL Cheatsheet 👇👇 https://websitesetup.org/sql-cheat-sheet/

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Start working on any project if you are a beginner and want to grow your career as a data scientist You will learn much more as you practice and work on projects from yourself You can find dataset in this channel or go to kaggle to find any random dataset and just work on it Learning concepts is fine but most of the learnings come from projects I know that might feel boring at first time but as you move forward, it become interesting

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K-means vs DBScan ML Algorithm DBScan is more robust to noise. DBScan is better when the amount of clusters is difficult to guess. K-means has a lower complexity, i.e. it will be much faster, especially with a larger amount of points.

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What is the curse of dimensionality? Why do we care about it? Data in only one dimension is relatively tightly packed. Adding a dimension stretches the points across that dimension, pushing them further apart. Additional dimensions spread the data even further making high dimensional data extremely sparse. We care about it, because it is difficult to use machine learning in sparse spaces.

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Dimensionality reduction techniques Singular Value Decomposition (SVD) Principal Component Analysis (PCA) Linear Discriminant Analysis (LDA) T-distributed Stochastic Neighbor Embedding (t-SNE) Autoencoders Fourier and Wavelet Transforms

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Data_science Numpy cheat sheet

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Chatbot project using ML Before using this you have to install Tensorflow, keras , pickle, nltk by using pip install in command prompt

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Pandas

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🎲Dice_roll_Simulator_Gui with python in 2 minute 😊

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Fake news Detection Machine Learning Project with 92%Accuracy it contain compressed file in which "jupyter notebook file and dataset"✅