Advertising on the Telegram channel «Best Courses with Certificate»

Call for papers on AI to AI Journey* conference journal has started! Prize for the best scientific paper - 1 million roubles! Selected papers will be published in the scientific journal Doklady Mathematics. 📖 The journal: •  Indexed in the largest bibliographic databases of scientific citations •  Accessible to an international audience and published in the world’s digital libraries Submit your article by August 20 and get the opportunity not only to publish your research the scientific journal, but also to present it at the AI Journey conference. Prize for the best article - 1 million roubles! More detailed information can be found in the Selection Rules -> AI Journey *AI Journey - a major online conference in the field of AI technologies

Useful websites to practice and enhance your data analytics skills 👇👇 1. Python http://learnpython.org 2. SQL https://www.sql-practice.com/ 3. Excel https://excel-practice-online.com/ 4. Power BI https://www.workout-wednesday.com/power-bi-challenges/ 5. Quiz and Interview Questions https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Haven't shared lot of resources to avoid too much distraction Just focus on the basics, practice learnings and work on building projects  to improve your skills. Thats the best way to learn in my opinion 😄 Join @free4unow_backup for more free courses ENJOY LEARNING 👍👍

Free Courses/ Certifications for a full-stack career in 2025: 1. Web design https://freecodecamp.org/learn/2022/responsive-web-design/ 2. JavaScript https://freecodecamp.org/learn/javascript-algorithms-and-data-structures/ https://bit.ly/3PI8JWB 3. Front-end libraries https://freecodecamp.org/learn/front-end-development-libraries/ 5. Back-end and APIs https://freecodecamp.org/learn/back-end-development-and-apis/ 6. Databases https://freecodecamp.org/learn/relational-database/ 7. Full Stack Web Development Bootcamp https://t.me/webdevcoursefree/461 Join @free4unow_backup for more free resources ENJOY LEARNING 👍👍

Learn for free ✅ HTML — https://www.w3schools.com/html CSS — http://CSS-tricks.com JavaScript — http://LearnJavaScript.online DSA --- https://t.me/dsabooks/21 Git, GitHub -- http://LearnGitBranching.js.org React — http://React-tutorial.app API — http://RapidAPI.com/comics SQL — http://SQLbolt.com Python -- https://t.me/pythondevelopersindia/76 Data Science -- https://t.me/datalemur PHP --- https://bit.ly/3QkY3wW ML -- https://developers.google.com/machine-learning/crash-course ChatGPT -- https://t.me/aisigma AI -- http://microsoft.github.io/AI-For-Beginners ENJOY LEARNING 👍👍

Complete Python Programming Roadmap for Beginners Stage 1: Python Fundamentals (Week 1–2) Goals: - Understand basic syntax and structure. - Get comfortable with writing and running Python code. Topics: - Variables, Data Types (int, float, str, bool) - Input/Output - Operators - If-Else Conditions - Loops (for, while) - Basic Functions Practice Platforms: - W3Schools Python - Replit - Python Exercises Stage 2: Data Structures in Python (Week 3–4) Goals: - Learn to store and manipulate data efficiently. Topics: - Lists, Tuples - Sets, Dictionaries - String manipulation - List comprehensions Mini Projects: - Word counter - Contact book using dictionary Practice: - HackerRank - LeetCode Easy Python Problems Stage 3: Functions, Error Handling & File Handling (Week 5–6) Goals: - Write reusable code. - Learn to debug and handle exceptions. Topics: - Creating & calling functions - *args and **kwargs - Try-Except blocks - Reading/writing files (.txt, .csv) - Working with with open(...) Mini Projects: - Quiz app - File-based To-Do list Stage 4: Modules, Libraries & OOP Basics (Week 7–8) Goals: - Understand Object-Oriented Programming and use Python libraries. Topics: - Importing and using libraries - Creating your own modules - Classes & Objects - init, methods, inheritance Mini Projects: - Calculator using OOP - Basic Library System Practice: - Real Python OOP Guide Stage 5: First Real Projects (Week 9–10) Goals: - Apply your knowledge to build end-to-end mini projects. Ideas: - Weather App using API - Simple Expense Tracker - Rock-Paper-Scissors Game - Number Guessing Game with levels Bonus Tips: - Keep a GitHub Repo to track progress. - Ask questions on forums like Stack Overflow or Reddit’s r/learnpython. - Use ChatGPT to get code explanations or help debugging. - Schedule 1 hour daily for coding + 30 mins for review. Best Resource to learn Python Python Interview Questions with Answers Freecodecamp Python Course with FREE Certificate Python for Data Analysis and Visualization Python course for beginners by Microsoft Python course by Google Python Coding Challenge Machine Learning with Python Please give us credits while sharing: -> https://t.me/free4unow_backup ENJOY LEARNING 👍👍

With its advanced technology and customer-centric approach, Global Cloud Mining offers a sustainable way to mine cryptocurrencies in 2025. Whether users are experienced investors or beginners, the platform ensures profitability and ease of use. Become a new member and receive a $18 credit to spend on the platform. This bonus is a welcome bonus and a recognition of your earning potential. Join cloud mining, free airdrop, start your journey to crypto wealth!Limited time airdrop, free mining machine to help you mine easily, enjoy the benefits every day! **High-yield contract plans to choose from** Looking for higher returns? Join a more profitable contract plan to achieve long-term performance growth. These flexible plans help you steadily increase your income while optimizing your financial planning.Official website: https://35global.com

Ad 👇👇

Complete Data Analytics Mastery: From Basics to Advanced 🚀 Begin your Data Analytics journey by mastering the fundamentals: - Understanding Data Types and Formats - Basics of Exploratory Data Analysis (EDA) - Introduction to Data Cleaning Techniques - Statistical Foundations for Data Analytics - Data Visualization Essentials Grasp these essentials in just a week to build a solid foundation in data analytics. Once you're comfortable, dive into intermediate topics: - Advanced Data Visualization (using tools like Tableau) - Hypothesis Testing and A/B Testing - Regression Analysis - Time Series Analysis for Analytics - SQL for Data Analytics Take another week to solidify these skills and enhance your ability to draw meaningful insights from data. Ready for the advanced level? Explore cutting-edge concepts: - Machine Learning for Data Analytics - Predictive Analytics - Big Data Analytics (Hadoop, Spark) - Advanced Statistical Methods (Multivariate Analysis) - Data Ethics and Privacy in Analytics These advanced concepts can be mastered in a couple of weeks with focused study and practice. Remember, mastery comes with hands-on experience: - Work on a simple data analytics project - Tackle an intermediate-level analysis task - Challenge yourself with an advanced analytics project involving real-world data sets Consistent practice and application of analytics techniques are the keys to becoming a data analytics pro. Best platforms to learn: - Intro to Data Analysis - Udacity's Data Analyst Nanodegree - Intro to Data Visualisation - SQL courses with Certificate - Freecodecamp Python Course - 365DataScience - Data Analyst Interview Questions - Free SQL Resources Share your progress and insights with others in the data analytics community. Enjoy the fascinating journey into the realm of data analytics! 👩‍💻👨‍💻 Join @free4unow_backup for more free resources. Like this post if it helps 😄❤️ ENJOY LEARNING 👍👍

When people thank me saying my telegram channel helped them a lot in learning new things, first question I ask them is which channel 😂 I have created multiple telegram channels but this one is my favourite.

Data Science Detailed Roadmap | | | |-- Fundamentals | |-- Introduction to Data Science | | |-- What is Data Science? | | |-- Roles: Analyst vs Scientist vs Engineer | | |-- Data Science Workflow | |-- Math and Statistics | | |-- Descriptive & Inferential Statistics | | |-- Probability Theory | | |-- Linear Algebra & Calculus Basics | |-- Programming for Data Science | |-- Python | | |-- Variables, Loops, Functions | | |-- NumPy, Pandas, Matplotlib, Seaborn | |-- R Programming (Optional but Useful) | | |-- Data Manipulation with dplyr, tidyr | | |-- Visualization with ggplot2 | |-- SQL | | |-- SELECT, WHERE, GROUP BY, JOINS | | |-- Subqueries and Window Functions | |-- Data Wrangling & Preprocessing | |-- Cleaning and Handling Missing Data | |-- Data Transformation & Encoding | |-- Feature Engineering | |-- Working with APIs and Web Scraping | |-- Data Visualization | |-- Exploratory Data Analysis (EDA) | |-- Visualization Tools | | |-- Python: Seaborn, Plotly | | |-- BI Tools: Power BI, Tableau | |-- Machine Learning | |-- Supervised Learning | | |-- Linear Regression | | |-- Classification (Logistic Regression, Decision Trees, SVM) | |-- Unsupervised Learning | | |-- Clustering (K-Means, DBSCAN) | | |-- Dimensionality Reduction (PCA, t-SNE) | |-- Model Evaluation | | |-- Cross-validation, Confusion Matrix | | |-- ROC-AUC, Precision, Recall, F1 Score | |-- Deep Learning & Neural Networks | |-- Introduction to Neural Networks | |-- Frameworks: TensorFlow, Keras, PyTorch | |-- CNNs for Image Data | |-- RNNs & LSTMs for Time Series / Text | |-- Projects & Real-World Applications | |-- End-to-End ML Projects | |-- Kaggle Competitions | |-- Case Studies (Retail, Finance, Healthcare) | |-- Big Data & Cloud Tools | |-- Introduction to Big Data | | |-- Hadoop, Spark | |-- Cloud Platforms | | |-- AWS, GCP, Azure (S3, EC2, BigQuery, SageMaker) | |-- Data Engineering Basics | |-- ETL Pipelines | |-- Workflow Automation with Airflow | |-- Data Warehousing (Snowflake, Redshift) | |-- Natural Language Processing (NLP) | |-- Text Preprocessing | |-- Bag of Words, TF-IDF | |-- NLP Libraries (NLTK, spaCy) | |-- Transformers (BERT, GPT) | |-- Time Series Analysis | |-- Trends, Seasonality, Forecasting | |-- ARIMA, Prophet | |-- LSTM for Time Series | |-- Model Deployment | |-- Building Web Apps (Streamlit, Flask) | |-- Model Serialization (Pickle, joblib) | |-- Deploy to Cloud (Heroku, AWS, GCP) | |-- Soft Skills & Career Prep | |-- Resume Projects and Portfolio | |-- Git and GitHub for Version Control | |-- Interview Preparation | |-- Communication & Storytelling with Data | |-- Bonus Topics | |-- Reinforcement Learning Basics | |-- Ethics in AI & Data Privacy | |-- MLOps and CI/CD for Data Science | |-- Community & Growth | |-- Kaggle, GitHub, LinkedIn | |-- Contributing to Open Source | |-- Blogging / Sharing Your Learnings Free Resources to learn Data Science Python Free Course Machine Learning Crash Course Data Science Course Google Cloud Generative AI Path Machine Learning with Python Free Course Data Science Free Resources Deep Learning Nanodegree Program with Real-world Projects AI, Machine Learning and Deep Learning Python Free Resources Data Science Interview Process Useful WhatsApp Channels to learn Data Science & Artificial Intelligence: Data Science & Machine Learning: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D Artificial intelligence: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y Data Science Projects: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z AI Agents: https://whatsapp.com/channel/0029Vb5vWhu0AgW92o23LY0I Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U Deeplearning AI: https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t Machine Learning: https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O Join @free4unow_backup for more free courses ENJOY LEARNING👍👍