Machine Learning & Artificial Intelligence | Data Science Free Courses
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Сеть:📈 Аналитический обзор Telegram-канала Machine Learning & Artificial Intelligence | Data Science Free Courses
Канал Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 68 686 подписчиков, занимая 2 393 место в категории Образование и 415 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 68 686 подписчиков.
Согласно последним данным от 28 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 232, а за последние 24 часа — -1, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.78%. В первые 24 часа после публикации контент обычно набирает 0.85% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 597 просмотров. В течение первых суток публикация набирает 581 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 5.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как sellerflash, waybienad, pricing, buybox, buyer.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
Благодаря высокой частоте обновлений (последние данные получены 29 октября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
68 686
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𝗙𝗿𝗲𝗲 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝗯𝘆 𝗔𝗰𝗰𝗲𝗻𝘁𝘂𝗿𝗲: 𝗚𝗮𝗶𝗻 𝗦𝗸𝗶𝗹𝗹𝘀 & 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀!😍
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If you want to learn Data Science, Data Analytics, Project Management, Artificial Intelligence, etc.
How to start a data science career
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Unlock the world of AI/ML with Google’s completely free course series!
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An important collection of the 15 best machine learning cheat sheets.
1- Supervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf
2- Unsupervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf
3- Deep Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf
4- Machine Learning Tips and Tricks
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf
5- Probabilities and Statistics
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf
6- Comprehensive Stanford Master Cheat Sheet
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf
7- Linear Algebra and Calculus
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf
8- Data Science Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf
9- Keras Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf
10- Deep Learning with Keras Cheat Sheet
https://github.com/rstudio/cheatsheets/raw/master/keras.pdf
11- Visual Guide to Neural Network Infrastructures
http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png
12- Skicit-Learn Python Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf
13- Scikit-learn Cheat Sheet: Choosing the Right Estimator
https://scikit-learn.org/stable/tutorial/machine_learning_map/
14- Tensorflow Cheat Sheet
https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf
15- Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
ENJOY LEARNING 👍👍
Free Resources only for Indian Users
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https://chat.whatsapp.com/LnN7UuGbaEyHOfDFbuz0SA
Free Programming and Data Analytics Resources 👇👇
✅ Data science and Data Analytics Free Courses by Google
https://developers.google.com/edu/python/introduction
https://grow.google/intl/en_in/data-analytics-course/?tab=get-started-in-the-field
https://cloud.google.com/data-science?hl=en
https://developers.google.com/machine-learning/crash-course
https://t.me/datasciencefun/1371
🔍 Free Data Analytics Courses by Microsoft
1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/
2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/
3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
🤖 Free AI Courses by Microsoft
1. Fundamentals of AI by Microsoft
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
2. Introduction to AI with python by Harvard.
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python
📚 Useful Resources for the Programmers
Data Analyst Roadmap
https://t.me/sqlspecialist/94
Free C course from Microsoft
https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019
Interactive React Native Resources
https://fullstackopen.com/en/part10
Python for Data Science and ML
https://t.me/datasciencefree/68
Ethical Hacking Bootcamp
https://t.me/ethicalhackingtoday/3
Unity Documentation
https://docs.unity3d.com/Manual/index.html
Advanced Javascript concepts
https://t.me/Programming_experts/72
Oops in Java
https://nptel.ac.in/courses/106105224
Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction
Python Data Structure and Algorithms
https://t.me/programming_guide/76
Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em
Data Structures Interview Preparation
https://t.me/crackingthecodinginterview/309?single
🍻 Free Programming Courses by Microsoft
❯ JavaScript
http://learn.microsoft.com/training/paths/web-development-101/
❯ TypeScript
http://learn.microsoft.com/training/paths/build-javascript-applications-typescript/
❯ C#
http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
Join @free4unow_backup for more free resources.
ENJOY LEARNING 👍👍
𝐅𝐑𝐄𝐄 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐈𝐧𝐭𝐞𝐫𝐧𝐬𝐡𝐢𝐩𝐬 😍
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📝 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 (Avg salary for a fresher: 6-8 LPA)
1. Excel
2. SQL (80% of the interview will be on expertise in SQL)
3. Python (Basic to intermediate knowledge required)
4. Data visualization tool (Most common: Tableau/PowerBI)
5. Statistics (Basic to intermediate)
📝 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 (Avg salary for a fresher: 10-15 LPA)
1. Excel, SQL, Python, Tableau/PowerBI, Statistics (All data analyst skills)
2. Mathematics (Linear algebra, Calculus)
3. Machine learning (Scikit-learn: Supervised, Unsupervised, Recommender systems, Timeseries modelling)
4. Deep learning (TensorFlow, PyTorch)
5. NLP (NLTK, spacy, gensim)
📝 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 (Avg salary for a fresher: 9-12 LPA)
1. Big data tools (Hadoop, Spark, Hive)
2. Python, Java or Scala
3. Data pipeline automation
4. SQL & NoSQL databases
5. ETL tools & Data warehousing (Apache Nifi, Talend, Airflow)
6. Cloud computing (AWS, Azure, GCP)
📝 𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 (Avg salary for a fresher: 10-12 LPA)
1. Cloud platforms (AWS, Azure, GCP)
2. Machine learning
3. DevOps & CI/CD
4. Version control
5. Code optimization & Tuning
📝 𝐌𝐋𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 (Avg salary for a fresher: 8-10 LPA)
1. CI/CD for ML Pipelines
2. Docker, Kubernetes & Container orchestration
3. Monitoring & Logging (Prometheus, Grafana, ELK stack: Elasticsearch, Logstash, Kibana)
4. Model versioning & Governance (MLflow, DVC)
5. Infrastructure as code (IaC): Teraform, CloudFormation, Ansible
6. API development & Integration
7. Automated testing for data validation, model performance & pipeline integrity
I have curated best 80+ top-notch Data Analytics Resources 👇👇
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𝐀𝐦𝐚𝐳𝐨𝐧 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍
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Machine Learning (17.4%)
Models: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Naive Bayes, Neural Networks (including Deep Learning)
Techniques: Training/testing data splitting, cross-validation, feature scaling, model evaluation metrics (accuracy, precision, recall, F1-score)
Data Manipulation (13.9%)
Techniques: Data cleaning (handling missing values, outliers), data wrangling (sorting, filtering, aggregating), data transformation (scaling, normalization), merging datasets
Programming Skills (11.7%)
Languages: Python (widely used in data science for its libraries like pandas, NumPy, scikit-learn), R (another popular choice for statistical computing), SQL (for querying relational databases)
Statistics and Probability (11.7%)
Concepts: Descriptive statistics (mean, median, standard deviation), hypothesis testing, probability distributions (normal, binomial, Poisson), statistical inference
Big Data Technologies (9.3%)
Tools: Apache Spark, Hadoop, Kafka (for handling large and complex datasets)
Data Visualization (9.3%)
Techniques: Creating charts and graphs (scatter plots, bar charts, heatmaps), storytelling with data, choosing the right visualizations for the data
Model Deployment (9.3%)
Techniques: Cloud platforms (AWS SageMaker, Google Cloud AI Platform, Microsoft Azure Machine Learning), containerization (Docker), model monitoring
𝐒𝐐𝐋 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍
🚀 Here are some top resources offering free courses to help you learn SQL from scratch or level up your skills.
Whether you're preparing for interviews, aiming for a job in data analytics, or improving your database knowledge, these courses have got you covered!
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Data Analyst Roadmap:
- Tier 1: Excel & SQL
- Tier 2: Data Cleaning & Exploratory Data Analysis (EDA)
- Tier 3: Data Visualization & Business Intelligence (BI) Tools
- Tier 4: Statistical Analysis & Machine Learning Basics
Then build projects that include:
- Data Collection
- Data Cleaning
- Data Analysis
- Data Visualization
And if you want to make your portfolio stand out more:
- Solve real business problems
- Provide clear, impactful insights
- Create a presentation
- Record a video presentation
- Target specific industries
- Reach out to companies
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope this helps you 😊
Join before I delete the post
Free Resources only for Indian Users
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https://chat.whatsapp.com/KDNWxWYJgC50nC18TsJSBh
