๐ฆ๐๐ฟ๐๐ด๐ด๐น๐ถ๐ป๐ด ๐๐ถ๐๐ต ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐? ๐ง๐ต๐ถ๐ ๐๐ต๐ฒ๐ฎ๐ ๐ฆ๐ต๐ฒ๐ฒ๐ ๐ถ๐ ๐ฌ๐ผ๐๐ฟ ๐จ๐น๐๐ถ๐บ๐ฎ๐๐ฒ ๐ฆ๐ต๐ผ๐ฟ๐๐ฐ๐๐!๐
Mastering Power BI can be overwhelming, but this cheat sheet by DataCamp makes it super easy! ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4ld6F7Y
No more flipping through tabs & tutorialsโjust pin this cheat sheet and analyze data like a pro!โ ๏ธ
Mastering Power BI can be overwhelming, but this cheat sheet by DataCamp makes it super easy! ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4ld6F7Y
No more flipping through tabs & tutorialsโjust pin this cheat sheet and analyze data like a pro!โ ๏ธ
๐1
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.
๐1
๐๐ฅ๐๐ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐ฐ๐น๐ฎ๐๐ ๐ข๐ป ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐
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
๐๐๐ ๐ข๐ฌ๐ญ๐๐ซ ๐ ๐จ๐ซ ๐ ๐๐๐ ๐:-
https://pdlink.in/4ccjV8P
(Limited Slots ..HurryUp๐โโ๏ธ )
๐๐๐ญ๐ & ๐๐ข๐ฆ๐:- 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
๐๐๐ ๐ข๐ฌ๐ญ๐๐ซ ๐ ๐จ๐ซ ๐ ๐๐๐ ๐:-
https://pdlink.in/4ccjV8P
(Limited Slots ..HurryUp๐โโ๏ธ )
๐๐๐ญ๐ & ๐๐ข๐ฆ๐:- 29th March, 2025, at 7 PM
๐1
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 ๐๐
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
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 ๐๐
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
๐4
Artificial intelligence can change your career by 180 degrees! ๐
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 Faceโs 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 โบ๏ธ
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 Faceโs 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 โบ๏ธ
๐3
๐ง How to Use ChatGPT for SEO ?
ChatGPT is a powerful tool to streamline and enhance your SEO efforts, but itโs only as good as the strategy behind it.
ChatGPT is a powerful tool to streamline and enhance your SEO efforts, but itโs only as good as the strategy behind it.
๐2๐ฅฐ1
๐ญ๐ฌ๐ฌ% ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
Master Python, Machine Learning, SQL, and Data Visualization with hands-on tutorials & real-world datasets? ๐ฏ
This 100% FREE resource from Kaggle will help you build job-ready skillsโno fluff, no fees, just pure learning!
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3XYAnDy
Perfect for Beginners โ ๏ธ
Master Python, Machine Learning, SQL, and Data Visualization with hands-on tutorials & real-world datasets? ๐ฏ
This 100% FREE resource from Kaggle will help you build job-ready skillsโno fluff, no fees, just pure learning!
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3XYAnDy
Perfect for Beginners โ ๏ธ
๐1
Best programming language suited for different fields:
1. Web development - Javascript, Python, PHP
2. AI & ML- Python, R
3. Competitive Programming - C++, Java
4. Mobile App development - Swift, Kotlin
5. Game Development - C#, C++
6. DSA- Java, Python
7. Data Analysis - Python, R
8. Data Science and ML - Python, R
9. Blockchain Dev- C++, Solidity
10. HFTs- C++, Java
11. Systems Programming - Rust, C
12. Embedded Systems - C, Assembly
13. Cybersecurity - Python, C
14. Financial Technology (Fintech) - Java, Scala
15. Internet of Things (IOT) - C, Python
16. Cloud Computing - Go, Java
17. DevOps & Automation - Python, Ruby
18. Scientific Computing - Fortran, Julia
1. Web development - Javascript, Python, PHP
2. AI & ML- Python, R
3. Competitive Programming - C++, Java
4. Mobile App development - Swift, Kotlin
5. Game Development - C#, C++
6. DSA- Java, Python
7. Data Analysis - Python, R
8. Data Science and ML - Python, R
9. Blockchain Dev- C++, Solidity
10. HFTs- C++, Java
11. Systems Programming - Rust, C
12. Embedded Systems - C, Assembly
13. Cybersecurity - Python, C
14. Financial Technology (Fintech) - Java, Scala
15. Internet of Things (IOT) - C, Python
16. Cloud Computing - Go, Java
17. DevOps & Automation - Python, Ruby
18. Scientific Computing - Fortran, Julia
๐5
๐๐ฟ๐ฒ๐ฎ๐ธ ๐๐ป๐๐ผ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ โ ๐ก๐ผ ๐๐
๐ฐ๐๐๐ฒ๐!๐
Want to learn Data Analytics, Python, Power BI, and Machine Learning without spending a single rupee?
Hereโs your golden ticket! ๐๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3DMG9S5
๐ Bookmark & Share This With Someone Who Needs It!
Want to learn Data Analytics, Python, Power BI, and Machine Learning without spending a single rupee?
Hereโs your golden ticket! ๐๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3DMG9S5
๐ Bookmark & Share This With Someone Who Needs It!
โค1๐1
๐ฐ ๐๐ฅ๐๐ ๐ฆ๐ค๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐
- Introduction to SQL (Simplilearn)
- Intro to SQL (Kaggle)
- Introduction to Database & SQL Querying
- SQL for Beginners โ Microsoft SQL Server
Start Learning Today โ 4 Free SQL Courses
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/42nUsWr
Enroll For FREE & Get Certified ๐
- Introduction to SQL (Simplilearn)
- Intro to SQL (Kaggle)
- Introduction to Database & SQL Querying
- SQL for Beginners โ Microsoft SQL Server
Start Learning Today โ 4 Free SQL Courses
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/42nUsWr
Enroll For FREE & Get Certified ๐
๐1