Forwarded from Artificial Intelligence
𝟰 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝘂𝗹𝗲𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀😍
Generative AI is no longer just a buzzword—it’s a career-maker🧑💻📌
Recruiters are actively looking for candidates with prompt engineering skills, hands-on AI experience, and the ability to use tools like GitHub Copilot and Azure OpenAI effectively.🖥
𝐋𝐢𝐧𝐤👇:-
http://pdlink.in/4fKT5pL
If you’re looking to stand out in interviews, land AI-powered roles, or future-proof your career, this is your chance
Generative AI is no longer just a buzzword—it’s a career-maker🧑💻📌
Recruiters are actively looking for candidates with prompt engineering skills, hands-on AI experience, and the ability to use tools like GitHub Copilot and Azure OpenAI effectively.🖥
𝐋𝐢𝐧𝐤👇:-
http://pdlink.in/4fKT5pL
If you’re looking to stand out in interviews, land AI-powered roles, or future-proof your career, this is your chance
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👩🏫🧑🏫 PROGRAMMING LANGUAGES YOU SHOULD LEARN TO BECOME.
⚔️[ Web Developer]
⚔️[ Game Developer]
⚔️[ Data Analysis]
⚔️[ Desktop Developer]
⚔️[ Embedded System Program]
⚔️[Mobile Apps Development]
⚔️[ Web Developer]
PHP, C#, JS, JAVA, Python, Ruby⚔️[ Game Developer]
Java, C++, Python, JS, Ruby, C, C#⚔️[ Data Analysis]
R, Matlab, Java, Python⚔️[ Desktop Developer]
Java, C#, C++, Python⚔️[ Embedded System Program]
C, Python, C++ ⚔️[Mobile Apps Development]
Kotlin, Dart, Objective-C, Java, Python, JS, Swift, C#❤1
🚀🔥 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 — 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺
Master the most in-demand AI skill in today’s job market: building autonomous AI systems.
In Ready Tensor’s free, project-first program, you’ll create three portfolio-ready projects using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, and vector databases — and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.
👉 Apply now: https://go.readytensor.ai/cert-514-agentic-ai-certification
Master the most in-demand AI skill in today’s job market: building autonomous AI systems.
In Ready Tensor’s free, project-first program, you’ll create three portfolio-ready projects using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, and vector databases — and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.
👉 Apply now: https://go.readytensor.ai/cert-514-agentic-ai-certification
www.readytensor.ai
Agentic AI Developer Certification Program by Ready Tensor
Learn to build chatbots, AI assistants, and multi-agent systems with Ready Tensor's free, self-paced, and beginner-friendly Agentic AI Developer Certification. View the full program guide and how to get certified.
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Data science is a multidisciplinary field that combines techniques from statistics, computer science, and domain-specific knowledge to extract insights and knowledge from data. Here are some essential concepts in data science:
1. Data Collection: The process of gathering data from various sources, such as databases, files, sensors, and APIs.
2. Data Cleaning: The process of identifying and correcting errors, missing values, and inconsistencies in the data.
3. Data Exploration: The process of summarizing and visualizing the data to understand its characteristics and relationships.
4. Data Preprocessing: The process of transforming and preparing the data for analysis, including feature selection, normalization, and encoding.
5. Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
6. Statistical Analysis: The use of statistical methods to analyze and interpret data, including hypothesis testing, regression analysis, and clustering.
7. Data Visualization: The graphical representation of data to communicate insights and findings effectively.
8. Model Evaluation: The process of assessing the performance of a predictive model using metrics such as accuracy, precision, recall, and F1 score.
9. Feature Engineering: The process of creating new features or transforming existing features to improve the performance of machine learning models.
10. Big Data: The term used to describe large and complex datasets that require specialized tools and techniques for analysis.
These concepts are foundational to the practice of data science and are essential for extracting valuable insights from data.
Join for more: https://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
1. Data Collection: The process of gathering data from various sources, such as databases, files, sensors, and APIs.
2. Data Cleaning: The process of identifying and correcting errors, missing values, and inconsistencies in the data.
3. Data Exploration: The process of summarizing and visualizing the data to understand its characteristics and relationships.
4. Data Preprocessing: The process of transforming and preparing the data for analysis, including feature selection, normalization, and encoding.
5. Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
6. Statistical Analysis: The use of statistical methods to analyze and interpret data, including hypothesis testing, regression analysis, and clustering.
7. Data Visualization: The graphical representation of data to communicate insights and findings effectively.
8. Model Evaluation: The process of assessing the performance of a predictive model using metrics such as accuracy, precision, recall, and F1 score.
9. Feature Engineering: The process of creating new features or transforming existing features to improve the performance of machine learning models.
10. Big Data: The term used to describe large and complex datasets that require specialized tools and techniques for analysis.
These concepts are foundational to the practice of data science and are essential for extracting valuable insights from data.
Join for more: https://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
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🚀🔥 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 — 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺
Master the most in-demand AI skill in today’s job market: building autonomous AI systems.
In Ready Tensor’s free, project-first program, you’ll create three portfolio-ready projects using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, and vector databases — and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.
👉 Apply now: https://go.readytensor.ai/cert-514-agentic-ai-certification
Master the most in-demand AI skill in today’s job market: building autonomous AI systems.
In Ready Tensor’s free, project-first program, you’ll create three portfolio-ready projects using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, and vector databases — and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.
👉 Apply now: https://go.readytensor.ai/cert-514-agentic-ai-certification
❤3
⌨️ An Amazing Cheatsheet for Tailwind CSS to master Tailwind in Minutes
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If I were to start my Machine Learning career from scratch (as an engineer), I'd focus here (no specific order):
1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving
And building as much as possible.
1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving
And building as much as possible.
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Future-Proof Skills for Data Analysts in 2025 & Beyond
1️⃣ AI-Powered Analytics 🤖 Leverage AI and AutoML tools like ChatGPT, DataRobot, and H2O.ai to automate insights and decision-making.
2️⃣ Generative AI for Data Analysis 🧠 Use AI for generating SQL queries, writing Python noscripts, and automating data storytelling.
3️⃣ Real-Time Data Processing ⚡ Learn streaming technologies like Apache Kafka and Apache Flink for real-time analytics.
4️⃣ DataOps & MLOps 🔄 Understand how to deploy and maintain machine learning models and analytical workflows in production environments.
5️⃣ Knowledge of Graph Databases 📊 Work with Neo4j and Amazon Neptune to analyze relationships in complex datasets.
6️⃣ Advanced Data Privacy & Ethics 🔐 Stay updated on GDPR, CCPA, and AI ethics to ensure responsible data handling.
7️⃣ No-Code & Low-Code Analytics 🛠️ Use platforms like Alteryx, Knime, and Google AutoML for rapid prototyping and automation.
8️⃣ API & Web Scraping Skills 🌍 Extract real-time data using APIs and web scraping tools like BeautifulSoup and Selenium.
9️⃣ Cross-Disciplinary Collaboration 🤝 Work with product managers, engineers, and business leaders to drive data-driven strategies.
🔟 Continuous Learning & Adaptability 🚀 Stay ahead by learning new technologies, attending conferences, and networking with industry experts.
Like for detailed explanation ❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
1️⃣ AI-Powered Analytics 🤖 Leverage AI and AutoML tools like ChatGPT, DataRobot, and H2O.ai to automate insights and decision-making.
2️⃣ Generative AI for Data Analysis 🧠 Use AI for generating SQL queries, writing Python noscripts, and automating data storytelling.
3️⃣ Real-Time Data Processing ⚡ Learn streaming technologies like Apache Kafka and Apache Flink for real-time analytics.
4️⃣ DataOps & MLOps 🔄 Understand how to deploy and maintain machine learning models and analytical workflows in production environments.
5️⃣ Knowledge of Graph Databases 📊 Work with Neo4j and Amazon Neptune to analyze relationships in complex datasets.
6️⃣ Advanced Data Privacy & Ethics 🔐 Stay updated on GDPR, CCPA, and AI ethics to ensure responsible data handling.
7️⃣ No-Code & Low-Code Analytics 🛠️ Use platforms like Alteryx, Knime, and Google AutoML for rapid prototyping and automation.
8️⃣ API & Web Scraping Skills 🌍 Extract real-time data using APIs and web scraping tools like BeautifulSoup and Selenium.
9️⃣ Cross-Disciplinary Collaboration 🤝 Work with product managers, engineers, and business leaders to drive data-driven strategies.
🔟 Continuous Learning & Adaptability 🚀 Stay ahead by learning new technologies, attending conferences, and networking with industry experts.
Like for detailed explanation ❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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Top 10 Websites for Data Science
1. Flowing Data (http://flowingdata.com)
2. Analytics Vidhya (http://www.analyticsvidhya.com)
3. R-Bloggers (http://www.r-bloggers.com)
4. Edwin Chen (http://blog.echen.me)
5. Hunch (http://hunch.net)
6. KDNuggets (http://www.kdnuggets.com)
7. Data Science Central (http://www.datasciencecentral.com)
8. Kaggle Competitions (https://www.kaggle.com/competitions)
9. Simply Statistics (http://simplystatistics.org)
10. FastML (http://fastml.com)
1. Flowing Data (http://flowingdata.com)
2. Analytics Vidhya (http://www.analyticsvidhya.com)
3. R-Bloggers (http://www.r-bloggers.com)
4. Edwin Chen (http://blog.echen.me)
5. Hunch (http://hunch.net)
6. KDNuggets (http://www.kdnuggets.com)
7. Data Science Central (http://www.datasciencecentral.com)
8. Kaggle Competitions (https://www.kaggle.com/competitions)
9. Simply Statistics (http://simplystatistics.org)
10. FastML (http://fastml.com)
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