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 204 Educación2 103...

📈 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 75 899 suscriptores, ocupando la posición 2 103 en la categoría Educación y el puesto 4 204 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 899 suscriptores.

Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 731, y en las últimas 24 horas de 33, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.95%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.86% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 2 239 visualizaciones. En el primer día suele acumular 650 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 24 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.

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Suscriptores

+3324 horas

+587 días

+73130 días

2 239

Visitas de la publicación

~ 65024 horas

~ 85748 horas

2.95%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

beta

Archivo de publicaciones

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Image recognition is an example of?

Anonymous voting

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You will prefer YouTube videos in which language?

Anonymous voting

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Rules of Machine Learning.pdf4.49 KB

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Perfect resources for beginners https://www.kaggle.com/kanncaa1/data-sciencetutorial-for-beginners

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For free machine learning, data science, ethical hacking and general programming courses join @bigdataspecialist channel. He also has discord server where you can ask anything about data science/machine learning and programing in general. https://discord.gg/f4sXD37H9q

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Git Cheatsheet

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Top Machine Learning Case studies https://nuggetsnetwork.com/blog/Machine-Learning-Case-Studies.html

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🔰Data Science [All Courses] 🔰 🌀Source : Udacity 🌀Size : 54.05 GB 🔗Link: https://mega.nz/#F!qrpxSIRD!PClG5ZMHdd5FroIFTT_Z5Q 💢 Share and Support Us 💢

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https://github.com/datastacktv/data-engineer-roadmap

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https://towardsdatascience.com/my-journey-into-data-science-39e9bbbbf452

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https://towardsdatascience.com/you-should-master-data-analytics-first-before-becoming-a-data-scientist-5dbceaea9d3d

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Amazon is hiring Position: Data Science Intern 👉 Apply: https://www.amazon.jobs/en/jobs/1008217/data-scientist-intern 👍 All the best.

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Open in app Responses To respond to this story, get the free Medium app. Open in app There are currently no responses for this story. Be the first to respond. What is it to be like Data Scientist (From a guy who has been one and now hires them!) Harsh Gupta 13 hours ago·3 min read I have worked as a Data Scientist for 4+ years and now manage data science teams. I have also worked with data science teams at multiple Fortune 500 companies. Here are my best observations about what it is like to be a data scientist as of Feb 2021. Best Case You work on very exciting problems in the realm of data science/ AI as well as for your business. You are publishing, you are thinking about new solutions all the time, and you are using your creative juices to the fullest. You are working with very interesting people inside and outside of your organization. Your team has visibility to senior leadership. You also have access to subject matter experts in your company, in AI research labs, at vendors who are thou

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SQL for Data Science

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Intershala Machine learning https://drive.google.com/folderview?id=1tZFMiQ-ZD9rZ4epFSd4lVCCuwqhQt0_9

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What do you want to learn?

Anonymous voting

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Logistic regression fits a logistic model to data and makes predictions about the probability of an event (between 0 and 1). Naive Bayes uses Bayes Theorem to model the conditional relationship of each attribute to the class variable. The k-Nearest Neighbor (kNN) method makes predictions by locating similar cases to a given data instance (using a similarity function) and returning the average or majority of the most similar data instances. The kNN algorithm can be used for classification or regression. Classification and Regression Trees (CART) are constructed from a dataset by making splits that best separate the data for the classes or predictions being made. The CART algorithm can be used for classification or regression. Support Vector Machines (SVM) are a method that uses points in a transformed problem space that best separate classes into two groups. Classification for multiple classes is supported by a one-vs-all method. SVM also supports regression by modeling the function with a minimum amount of allowable error.

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Three different learning styles in machine learning algorithms: 1. Supervised Learning Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time. A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data. Example problems are classification and regression. Example algorithms include: Logistic Regression and the Back Propagation Neural Network. 2. Unsupervised Learning Input data is not labeled and does not have a known result. A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity. Example problems are clustering, dimensionality reduction and association rule learning. Example algorithms include: the Apriori algorithm and K-Means. 3. Semi-Supervised Learning Input data is a mixture of labeled and unlabelled examples. There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions. Example problems are classification and regression. Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.

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