𝗬𝗼𝘂𝗿 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱!😍
Want to break into Artificial Intelligence and work with cutting-edge technologies?👋
This FREE roadmap will guide you through everything you need to become an AI Engineer in 2025!🎊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4iA6aTE
Build Real-World AI Projects & stand out from the crowd!✅️
Want to break into Artificial Intelligence and work with cutting-edge technologies?👋
This FREE roadmap will guide you through everything you need to become an AI Engineer in 2025!🎊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4iA6aTE
Build Real-World AI Projects & stand out from the crowd!✅️
🏆1
𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 😍
- Capgemini
- Infosys
- KPMG
- Genpact
- JP Morgan
Qualification :- Any Graduate
𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 & 𝐔𝐩𝐥𝐨𝐚𝐝 𝐘𝐨𝐮𝐫 𝐑𝐞𝐬𝐮𝐦𝐞👇:-
https://bit.ly/3ZI20AY
Enter your experience & Complete The Registration Process
Select the company name & Apply for jobs
- Capgemini
- Infosys
- KPMG
- Genpact
- JP Morgan
Qualification :- Any Graduate
𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 & 𝐔𝐩𝐥𝐨𝐚𝐝 𝐘𝐨𝐮𝐫 𝐑𝐞𝐬𝐮𝐦𝐞👇:-
https://bit.ly/3ZI20AY
Enter your experience & Complete The Registration Process
Select the company name & Apply for jobs
𝗟𝗲𝗮𝗿𝗻 𝗔𝗜, 𝗗𝗲𝘀𝗶𝗴𝗻 & 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍
Want to break into AI, UI/UX, or project management? 🚀
These 5 beginner-friendly FREE courses will help you develop in-demand skills and boost your resume in 2025!🎊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4iV3dNf
✨ No cost, no catch—just pure learning from anywhere!
Want to break into AI, UI/UX, or project management? 🚀
These 5 beginner-friendly FREE courses will help you develop in-demand skills and boost your resume in 2025!🎊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4iV3dNf
✨ No cost, no catch—just pure learning from anywhere!
👍1
Tips for working with Microsoft
Word
✅ To quickly insert the current date, press SHIFT + ALT + D, and to insert the time, SHIFT + ALT + T.
✅ If you accidentally typed a text with the CAPS LOCK key enabled, then select the text in uppercase and press SHIFT + F3.
✅ To speed up the cursor movement through the text, hold down CTRL and use the arrows.
✅ By holding CTRL, you can select all the parts of the text you are interested in, even if they are not arranged in a row.
✅ The F4 key repeats the last command used.
✅ Place the cursor at the beginning of the text selection and click at the end of the desired text segment with the SHIFT key held down for quick selection.
✅ Press CTRL + ENTER to quickly create a new sheet.
#lifehack #windows
Word
✅ To quickly insert the current date, press SHIFT + ALT + D, and to insert the time, SHIFT + ALT + T.
✅ If you accidentally typed a text with the CAPS LOCK key enabled, then select the text in uppercase and press SHIFT + F3.
✅ To speed up the cursor movement through the text, hold down CTRL and use the arrows.
✅ By holding CTRL, you can select all the parts of the text you are interested in, even if they are not arranged in a row.
✅ The F4 key repeats the last command used.
✅ Place the cursor at the beginning of the text selection and click at the end of the desired text segment with the SHIFT key held down for quick selection.
✅ Press CTRL + ENTER to quickly create a new sheet.
#lifehack #windows
👍1
𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
- Python Programming
- Data Analytics
- Generative AI
- Machine Learning
- Data Science
- SQL
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/41VIuSA
Enroll Now & Get a course completion certificate🎓
- Python Programming
- Data Analytics
- Generative AI
- Machine Learning
- Data Science
- SQL
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/41VIuSA
Enroll Now & Get a course completion certificate🎓
Anthropic’s AI assistant Claude learns to search the web
Anthropic has announced its AI assistant Claude can now search the web, providing users with more up-to-date and relevant responses.
This integration of web search functionality means Claude can now access the latest information to expand its knowledge base beyond its initial training data.
A key feature of this update is the emphasis on transparency and fact-checking. Anthropic highlights that “When Claude incorporates information from the web into its responses, it provides direct citations so you can easily fact check sources.”
Anthropic has announced its AI assistant Claude can now search the web, providing users with more up-to-date and relevant responses.
This integration of web search functionality means Claude can now access the latest information to expand its knowledge base beyond its initial training data.
A key feature of this update is the emphasis on transparency and fact-checking. Anthropic highlights that “When Claude incorporates information from the web into its responses, it provides direct citations so you can easily fact check sources.”
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𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜? 𝗧𝗵𝗶𝘀 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 𝗶𝘀 𝗬𝗼𝘂𝗿 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗦𝗵𝗼𝗿𝘁𝗰𝘂𝘁!😍
Mastering Power BI can be overwhelming, but this cheat sheet by DataCamp makes it super easy! 🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4ld6F7Y
No more flipping through tabs & tutorials—just pin this cheat sheet and analyze data like a pro!✅️
Mastering Power BI can be overwhelming, but this cheat sheet by DataCamp makes it super easy! 🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4ld6F7Y
No more flipping through tabs & tutorials—just pin this cheat sheet and analyze data like a pro!✅️
👍1
How long are coding interviews?
The phone screen portion of the coding interview typically lasts up to one hour. The second, more technical part of the interview can take multiple hours.
Where can I practice coding?
There are many ways to practice coding and prepare for your coding interview. LeetCode provides practice opportunities in more than 14 languages and more than 1,500 sample problems. Applicants can also practice their coding skills and interview prep with HackerRank.
How do I know if my coding interview went well?
There are a variety of indicators that your coding interview went well. These may include going over the allotted time, being introduced to additional team members, and receiving a quick response to your thank you email.
The phone screen portion of the coding interview typically lasts up to one hour. The second, more technical part of the interview can take multiple hours.
Where can I practice coding?
There are many ways to practice coding and prepare for your coding interview. LeetCode provides practice opportunities in more than 14 languages and more than 1,500 sample problems. Applicants can also practice their coding skills and interview prep with HackerRank.
How do I know if my coding interview went well?
There are a variety of indicators that your coding interview went well. These may include going over the allotted time, being introduced to additional team members, and receiving a quick response to your thank you email.
👍1
𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗢𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 😍
Data Science is reshaping industries, and having the right tools and skills can set you apart in this exciting field
Know The Roadmap To Become a Successful Data Scientist In 2025
Eligibility :- Students, Graduates & Woking Professionals
𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐅𝐨𝐫 𝐅𝐑𝐄𝐄 👇:-
https://pdlink.in/4ccjV8P
(Limited Slots ..HurryUp🏃♂️ )
𝐃𝐚𝐭𝐞 & 𝐓𝐢𝐦𝐞:- 29th March, 2025, at 7 PM
Data Science is reshaping industries, and having the right tools and skills can set you apart in this exciting field
Know The Roadmap To Become a Successful Data Scientist In 2025
Eligibility :- Students, Graduates & Woking Professionals
𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐅𝐨𝐫 𝐅𝐑𝐄𝐄 👇:-
https://pdlink.in/4ccjV8P
(Limited Slots ..HurryUp🏃♂️ )
𝐃𝐚𝐭𝐞 & 𝐓𝐢𝐦𝐞:- 29th March, 2025, at 7 PM
👍1
7 level of writing Python Dictionary
Level 1: Basic Dictionary Creation
Level 2: Accessing and Modifying values
Level 3: Adding and Removing key Values Pairs
Level 4: Dictionary Methods
Level 5: Dictionary Comprehensions
Level 6: Nested Dictionary
Level 7: Advanced Dictionary Operations
I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this 👍❤️
Level 1: Basic Dictionary Creation
Level 2: Accessing and Modifying values
Level 3: Adding and Removing key Values Pairs
Level 4: Dictionary Methods
Level 5: Dictionary Comprehensions
Level 6: Nested Dictionary
Level 7: Advanced Dictionary Operations
I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this 👍❤️
👍4
Artificial intelligence can change your career by 180 degrees! 📌
Here's how you can start with AI engineering with zero experience!
The simplest definition of artificial intelligence|
Artificial intelligence (AI) is a part of computer science that creates smart systems to solve problems usually needing human intelligence.
AI includes tasks like recognizing objects and patterns, understanding voices, making predictions, and more.
Step 1: Master the prerequisites
Basics of programming
Probability and statistics essentials
Data structures
Data analysis essentials
Step 2: Get into machine learning and deep learning
Basics of data science, an intersection field
Feature engineering and machine learning
Neural networks and deep learning
Scikit-learn for machine learning along with Numpy, Pandas and matplotlib
TensorFlow, Keras and PyTorch for deep learning
Step 3: Exploring Generative Adversarial Networks (GANs)
Learn GAN fundamentals: Understand the theory behind GANs, including how the generator and discriminator work together to produce realistic data.
Hands-on projects: Build and train simple GANs using PyTorch or TensorFlow to generate images, enhance resolution, or perform style transfer.
Step 4: Get into Transformers architecture
Grasp the basics: Study the Transformer architecture's key concepts, including attention mechanisms, positional encodings, and the encoder-decoder structure.
Implementations: Use libraries like Hugging Face’s Transformers to experiment with different Transformer models, such as GPT and BERT, on NLP tasks.
Step 5: Working with Pre-trained Large Language Models
Utilize existing models: Learn how to leverage pre-trained models from libraries like Hugging Face to perform tasks like text generation, translation, and sentiment analysis.
Fine-tuning techniques: Explore strategies for fine-tuning these models on domain-specific datasets to improve performance and relevance.
Step 6: Introduction to LangChain
Understand LangChain: Familiarize yourself with LangChain, a framework designed to build applications that combine language models with external knowledge and capabilities.
Build applications: Use LangChain to develop applications that interactively use language models to process and generate information based on user queries or tasks.
Step 7: Leveraging Vector Databases
Basics of vector databases: Understand what vector databases are and why they are crucial for managing high-dimensional data typically used in AI models.
Tools and technologies: Learn to use vector databases like Milvus, Pinecone, or Weaviate, which are optimized for fast similarity search and efficient handling of vector embeddings.
Practical application: Integrate vector databases into your projects for enhanced search functionalities
Step 8: Exploration of Retrieval-Augmented Generation (RAG)
Learn the RAG approach: Understand how RAG models combine the power of retrieval (extracting information from a large database) with generative models to enhance the quality and relevance of the outputs.
Practical applications: Study case studies or research papers that showcase the use of RAG in real-world applications.
Step 9: Deployment of AI Projects
Deployment tools: Learn to use tools like Docker for containerization, Kubernetes for orchestration, and cloud services (AWS, Azure, Google Cloud) for deploying models.
Monitoring and maintenance: Understand the importance of monitoring AI systems post-deployment and how to use tools like Prometheus, Grafana, and Elastic Stack for performance tracking and logging.
Step 10: Keep building
Implement Projects and Gain Practical Experience
Work on diverse projects: Apply your knowledge to solve problems across different domains using AI, such as natural language processing, computer vision, and speech recognition.
Contribute to open-source: Participate in AI projects and contribute to open-source communities to gain experience and collaborate with others.
Hope this helps you ☺️
Here's how you can start with AI engineering with zero experience!
The simplest definition of artificial intelligence|
Artificial intelligence (AI) is a part of computer science that creates smart systems to solve problems usually needing human intelligence.
AI includes tasks like recognizing objects and patterns, understanding voices, making predictions, and more.
Step 1: Master the prerequisites
Basics of programming
Probability and statistics essentials
Data structures
Data analysis essentials
Step 2: Get into machine learning and deep learning
Basics of data science, an intersection field
Feature engineering and machine learning
Neural networks and deep learning
Scikit-learn for machine learning along with Numpy, Pandas and matplotlib
TensorFlow, Keras and PyTorch for deep learning
Step 3: Exploring Generative Adversarial Networks (GANs)
Learn GAN fundamentals: Understand the theory behind GANs, including how the generator and discriminator work together to produce realistic data.
Hands-on projects: Build and train simple GANs using PyTorch or TensorFlow to generate images, enhance resolution, or perform style transfer.
Step 4: Get into Transformers architecture
Grasp the basics: Study the Transformer architecture's key concepts, including attention mechanisms, positional encodings, and the encoder-decoder structure.
Implementations: Use libraries like Hugging Face’s Transformers to experiment with different Transformer models, such as GPT and BERT, on NLP tasks.
Step 5: Working with Pre-trained Large Language Models
Utilize existing models: Learn how to leverage pre-trained models from libraries like Hugging Face to perform tasks like text generation, translation, and sentiment analysis.
Fine-tuning techniques: Explore strategies for fine-tuning these models on domain-specific datasets to improve performance and relevance.
Step 6: Introduction to LangChain
Understand LangChain: Familiarize yourself with LangChain, a framework designed to build applications that combine language models with external knowledge and capabilities.
Build applications: Use LangChain to develop applications that interactively use language models to process and generate information based on user queries or tasks.
Step 7: Leveraging Vector Databases
Basics of vector databases: Understand what vector databases are and why they are crucial for managing high-dimensional data typically used in AI models.
Tools and technologies: Learn to use vector databases like Milvus, Pinecone, or Weaviate, which are optimized for fast similarity search and efficient handling of vector embeddings.
Practical application: Integrate vector databases into your projects for enhanced search functionalities
Step 8: Exploration of Retrieval-Augmented Generation (RAG)
Learn the RAG approach: Understand how RAG models combine the power of retrieval (extracting information from a large database) with generative models to enhance the quality and relevance of the outputs.
Practical applications: Study case studies or research papers that showcase the use of RAG in real-world applications.
Step 9: Deployment of AI Projects
Deployment tools: Learn to use tools like Docker for containerization, Kubernetes for orchestration, and cloud services (AWS, Azure, Google Cloud) for deploying models.
Monitoring and maintenance: Understand the importance of monitoring AI systems post-deployment and how to use tools like Prometheus, Grafana, and Elastic Stack for performance tracking and logging.
Step 10: Keep building
Implement Projects and Gain Practical Experience
Work on diverse projects: Apply your knowledge to solve problems across different domains using AI, such as natural language processing, computer vision, and speech recognition.
Contribute to open-source: Participate in AI projects and contribute to open-source communities to gain experience and collaborate with others.
Hope this helps you ☺️
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