Free Online Courses with Certificate | Udacity Free Courses | Eduonix | IP Cybersecurity | Coursera | Premium Certified Courses
前往频道在 Telegram
👉Udacity, Microsoft, Edx, Google and Eduonix courses for free 👉Get premium Free courses from top websites 👉We also provide discount coupon codes for premium Udacity courses to help you as much as we can For promotions: @love_data
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网络:📈 Telegram 频道 Free Online Courses with Certificate | Udacity Free Courses | Eduonix | IP Cybersecurity | Coursera | Premium Certified Courses 的分析概览
频道 Free Online Courses with Certificate | Udacity Free Courses | Eduonix | IP Cybersecurity | Coursera | Premium Certified Courses (@udacityfreecourse) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 46 593 名订阅者，在 教育 类别中位列第 3 792，并在 印度 地区排名第 7 901 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 46 593 名订阅者。
根据 05 十月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -277，过去 24 小时变化为 1，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 1.51%。内容发布后 24 小时内通常能获得 0.53% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 703 次浏览，首日通常累积 247 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 2。
- 主题关注点： 内容集中在 |--, learning, javascript, analytic, certification 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“👉Udacity, Microsoft, Edx, Google and Eduonix courses for free
👉Get premium Free courses from top websites
👉We also provide discount coupon codes for premium Udacity courses to help you as much as we can
For promotions: @love_data”
凭借高频更新（最新数据采集于 06 十月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 教育 类别中的关键影响点。
46 593
订阅者
+124 小时
-597 天
-27730 天
数据加载中...
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| 日期 | 订阅者增长 | 提及 | 频道 | |
| 06 十月 | +4 | |||
| 05 十月 | +3 | |||
| 04 十月 | +11 | |||
| 03 十月 | 0 | |||
| 02 十月 | 0 | |||
| 01 十月 | 0 |
频道帖子
✅ Programming Important Terms You Should Know 💻🚀
Programming is the backbone of tech, and knowing the right terms can boost your learning and career.
🧠 Core Programming Concepts
• Programming: Writing instructions for a computer to perform tasks.
• Algorithm: Step-by-step procedure to solve a problem.
• Flowchart: Visual representation of a program’s logic.
• Syntax: Rules that define how code must be written.
• Compilation: Converting source code into machine code.
• Interpretation: Executing code line-by-line without compiling first.
⚙️ Basic Programming Elements
• Variable: Storage location for data.
• Constant: Fixed value that cannot change.
• Data Type: Type of data (int, float, string, boolean).
• Operator: Symbol performing operations (+, -, **, /, ==).
• Expression: Combination of variables, operators, and values.
• Statement: A single line of instruction in a program.
🔄 Control Flow Concepts
• Conditional Statements: Execute code based on conditions (if, else).
• Loops: Repeat a block of code (for, while).
• Break Statement: Exit a loop early.
• Continue Statement: Skip the current loop iteration.
• Switch Case: Multi-condition decision structure.
📦 Functions & Modular Programming
• Function: Reusable block of code performing a task.
• Parameter: Input passed to a function.
• Return Value: Output returned by a function.
• Module: File containing reusable functions or classes.
• Library: Collection of pre-written code.
🧩 Object-Oriented Programming (OOP)
• Class: Blueprint for creating objects.
• Object: Instance of a class.
• Encapsulation: Bundling data and methods together.
• Inheritance: One class acquiring properties of another.
• Polymorphism: Same function behaving differently in different contexts.
• Abstraction: Hiding complex implementation details.
📊 Data Structures
• Array: Collection of elements stored sequentially.
• List: Ordered collection that can change size.
• Stack: Last In First Out (LIFO) structure.
• Queue: First In First Out (FIFO) structure.
• Hash Table / Dictionary: Key-value data storage.
• Tree: Hierarchical data structure.
• Graph: Network of connected nodes.
⚡ Advanced Programming Concepts
• Recursion: Function calling itself.
• Concurrency: Multiple tasks running simultaneously.
• Multithreading: Multiple threads within a program.
• Memory Management: Allocation and deallocation of memory.
• Garbage Collection: Automatic memory cleanup.
• Exception Handling: Handling runtime errors using try, catch, except.
🌐 Software Development Concepts
• Framework: Pre-built structure for building applications.
• API: Interface allowing different software to communicate.
• Version Control: Tracking code changes using tools like Git.
• Debugging: Finding and fixing code errors.
• Testing: Verifying that code works correctly.
Double Tap ♥️ For Detailed Explanation of Each Topic
| 2 |  𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗟𝗲𝗮𝗿𝗻 𝗔𝗜 𝗶𝗻 𝟮𝟬𝟮𝟲🚀
Explore 6 free resources covering AI fundamentals, tools, deep learning, research and real-world applications.
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📢 Share this valuable opportunity with your friends and classmates! | 626 |
| 3 | ✅ FREE Tools to Learn Coding & Practice Skills 💻🚀
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| 4 |  🎓 𝗛𝗔𝗥𝗩𝗔𝗥𝗗 𝗨𝗡𝗜𝗩𝗘𝗥𝗦𝗜𝗧𝗬 𝗙𝗥𝗘𝗘 𝗢𝗡𝗟𝗜𝗡𝗘 𝗖𝗢𝗨𝗥𝗦𝗘𝗦 😍
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📢 Share this valuable opportunity with your friends and classmates! | 584 |
| 5 | Important Topics You Should Know to Learn Python 👇
Lists, Strings, Tuples, Dictionaries, Sets – Learn the core data structures in Python.
Boolean, Arithmetic, and Comparison Operators – Understand how Python evaluates conditions.
Operations on Data Structures – Append, delete, insert, reverse, sort, and manipulate collections efficiently.
Reading and Extracting Data – Learn how to access, modify, and extract values from lists and dictionaries.
Conditions and Loops – Master if, elif, else, for, while, break, and continue statements.
Range and Enumerate – Efficiently loop through sequences with indexing.
Functions – Create functions with and without parameters, and understand *args and **kwargs.
Classes & Object-Oriented Programming – Work with init methods, global/local variables, and concepts like inheritance and encapsulation.
File Handling – Read, write, and manipulate files in Python.
Free Resources to learn Python👇👇
👉 Free Python course by Google
https://developers.google.com/edu/python
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https://www.freecodecamp.org/learn/data-analysis-with-python/#
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👉 Practice Python
http://www.pythonchallenge.com/
👉 Kaggle
https://kaggle.com/learn/intro-to-programming
https://kaggle.com/learn/python
👉 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻
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https://freecodecamp.org/learn/machine-learning-with-python/
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| 6 |  𝗟𝗲𝘃𝗲𝗹 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗚𝗮𝗺𝗲-𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀!
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https://pdlink.in/4z3vOYU
Save this post and share with your friends | 565 |
| 7 | SQL (Structured Query Language) is a standard programming language used to manage and manipulate relational databases. Here are some key concepts to understand the basics of SQL:
1. Database: A database is a structured collection of data organized in tables, which consist of rows and columns.
2. Table: A table is a collection of related data organized in rows and columns. Each row represents a record, and each column represents a specific attribute or field.
3. Query: A SQL query is a request for data or information from a database. Queries are used to retrieve, insert, update, or delete data in a database.
4. CRUD Operations: CRUD stands for Create, Read, Update, and Delete. These are the basic operations performed on data in a database using SQL:
- Create (INSERT): Adds new records to a table.
- Read (SELECT): Retrieves data from one or more tables.
- Update (UPDATE): Modifies existing records in a table.
- Delete (DELETE): Removes records from a table.
5. Data Types: SQL supports various data types to define the type of data that can be stored in each column of a table, such as integer, text, date, and decimal.
6. Constraints: Constraints are rules enforced on data columns to ensure data integrity and consistency. Common constraints include:
- Primary Key: Uniquely identifies each record in a table.
- Foreign Key: Establishes a relationship between two tables.
- Unique: Ensures that all values in a column are unique.
- Not Null: Specifies that a column cannot contain NULL values.
7. Joins: Joins are used to combine rows from two or more tables based on a related column between them. Common types of joins include INNER JOIN, LEFT JOIN (or LEFT OUTER JOIN), RIGHT JOIN (or RIGHT OUTER JOIN), and FULL JOIN (or FULL OUTER JOIN).
8. Aggregate Functions: SQL provides aggregate functions to perform calculations on sets of values. Common aggregate functions include SUM, AVG, COUNT, MIN, and MAX.
9. Group By: The GROUP BY clause is used to group rows that have the same values into summary rows. It is often used with aggregate functions to perform calculations on grouped data.
10. Order By: The ORDER BY clause is used to sort the result set of a query based on one or more columns in ascending or descending order.
Understanding these basic concepts of SQL will help you write queries to interact with databases effectively. Practice writing SQL queries and experimenting with different commands to become proficient in using SQL for database management and manipulation.
SQL Learning Series: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075 | 612 |
| 8 |  🚀 𝗚𝗼𝗼𝗴𝗹𝗲 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜! 📊
Explore these 4 Google learning programs and develop practical, career-relevant skills.
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📌 Save this post and share it with someone interested in Data Analytics or AI! | 605 |
| 9 |  🚀 𝐁𝐞𝐜𝐨𝐦𝐞 𝐚𝐧 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐢𝐧 𝟐𝟎𝟐𝟔
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⚡ AI is creating new career opportunities—start building the skills companies need in 2026! | 753 |
| 10 | Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
BEST RESOURCES TO LEARN DATA SCIENCE AND MACHINE LEARNING FOR FREE
https://developers.google.com/machine-learning/crash-course
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Join @datasciencefun for more free resources
ENJOY LEARNING 👍👍 | 758 |
| 11 |  🚀 𝗧𝗼𝗽 𝟳 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀! 📊
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💡 Ideal for students, freshers and professionals who want to build practical data skills. | 825 |
| 12 |  🎓 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀! 🚀
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🎯 Great for students, freshers and working professionals looking to expand their knowledge. | 779 |
| 13 |  𝗙𝗥𝗘𝗘 𝗔𝗜 𝗖𝗮𝗿𝗲𝗲𝗿 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 🚀
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⚡ Register now and take your first step towards a successful career in AI! | 812 |
| 14 | 🔥 A-Z Data Science Road Map
1. 📊 Math and Statistics
- Descriptive statistics
- Probability
- Distributions
- Hypothesis testing
- Correlation
- Regression basics
2. 🐍 Python Basics
- Variables
- Data types
- Loops
- Conditionals
- Functions
- Modules
3. 🐼 Core Python for Data Science
- NumPy
- Pandas
- DataFrames
- Missing values
- Merging
- GroupBy
- Visualization
4. 📈 Data Visualization
- Matplotlib
- Seaborn
- Plotly
- Histograms, boxplots, heatmaps
- Dashboards
5. 🧹 Data Wrangling
- Cleaning
- Outlier detection
- Feature engineering
- Encoding
- Scaling
6. 🔍 Exploratory Data Analysis (EDA)
- Univariate analysis
- Bivariate analysis
- Stats summary
- Correlation analysis
7. 💾 SQL for Data Science
- SELECT
- WHERE
- GROUP BY
- JOINS
- CTEs
- Window functions
8. 🤖 Machine Learning Basics
- Supervised vs unsupervised
- Train test split
- Cross validation
- Metrics
9. 🎯 Supervised Learning
- Linear regression
- Logistic regression
- Decision trees
- Random forest
- Gradient boosting
- SVM
- KNN
10. 💡 Unsupervised Learning
- K-Means
- Hierarchical clustering
- PCA
- Dimensionality reduction
11. ⭐ Model Evaluation
- Accuracy
- Precision
- Recall
- F1
- ROC AUC
- MSE, RMSE, MAE
12. 🛠️ Feature Engineering
- One hot encoding
- Binning
- Scaling
- Interaction terms
13. ⏳ Time Series
- Trends
- Seasonality
- ARIMA
- Prophet
- Forecasting steps
14. 🧠 Deep Learning Basics
- Neural networks
- Activation functions
- Loss functions
- Backprop basics
15. 🚀 Deep Learning Libraries
- TensorFlow
- Keras
- PyTorch
16. 💬 NLP
- Tokenization
- Stemming
- Lemmatization
- TF-IDF
- Word embeddings
17. 🌐 Big Data Tools
- Hadoop
- Spark
- PySpark
18. ⚙️ Data Engineering Basics
- ETL
- Pipelines
- Scheduling
- Cloud concepts
19. ☁️ Cloud Platforms
- AWS (S3, Lambda, SageMaker)
- GCP (BigQuery)
- Azure ML
20. 📦 MLOps
- Model deployment
- CI/CD
- Monitoring
- Docker
- APIs (FastAPI, Flask)
21. 📊 Dashboards
- Power BI
- Tableau
- Streamlit
22. 🏗️ Real-World Projects
- Classification
- Regression
- Time series
- NLP
- Recommendation systems
23. 🧑💻 Version Control
- Git
- GitHub
- Branching
- Pull requests
24. 🗣️ Soft Skills
- Problem framing
- Business communication
- Storytelling
25. 📝 Interview Prep
- SQL practice
- Python challenges
- ML theory
- Case studies
------------------- END -------------------
✅ Good Resources To Learn Data Science
1. 📚 Documentation
- Pandas docs: pandas.pydata.org
- NumPy docs: numpy.org
- Scikit-learn docs: scikit-learn.org
- PyTorch: pytorch.org
2. 📺 Free Learning Channels
- FreeCodeCamp: youtube.com/c/FreeCodeCamp
- Data School: youtube.com/dataschool
- Krish Naik: YouTube
- WhatsApp channel
- StatQuest: YouTube
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| 16 | Sure! Here’s the text with the asterisks replaced by **:
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| 17 |  🎓 𝐅𝐑𝐄𝐄 𝐈𝐁𝐌 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🚀
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| 18 |  🎯 GigaChat 3.5 Reasoning: 5 Key Features
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2️⃣ Autonomous Tool Usage: Independently decides when to call external APIs or revise earlier steps
3️⃣ Linear Attention: Proprietary architecture retains key context points without re-matching from scratch
4️⃣ Token Economy: Uses 37% fewer tokens than DeepSeek V4 Flash Preview on math problems
5️⃣ Proven Performance: Open-source LLM (built on GigaChat 3.5 Ultra) with massive benchmark gains:
• IFBench: 44 → 77
• Natural Plan: 64 → 80
• LiveCodeBench v6: 56 → 85
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| 19 |  𝗜𝗻𝗳𝗼𝘀𝘆𝘀 𝗠𝗼𝘀𝘁 𝗔𝘀𝗸𝗲𝗱 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 & 𝗔𝗻𝘀𝘄𝗲𝗿𝘀😍
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| 20 | Best YouTube Channels To Learn
• Cybersecurity - John Hammond
• Networking - David Bombal
• Python - Code With Harry
• UI/UX - GFXMentol
• React - Codevolution
• JavaScript - Traversy Media
• Java - Kunal Kushwaha
• DevOps - Techworld With Nana
• Blockchain - Telusko
• Al/ML- Krish Naik
• Web Development - Traversy Media
• AWS - Code With Harry
• SQL - Programming With Mosh
• DBMS -Edureka
• Ruby-The Ruby Way
• Scala - Scala Love
• SAP -Intellipaat
• C- FeecodeCamp
• R- Krish Naik | 907 |
