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Know The Roadmap To Become a Successful Data Scientist In 2025
Eligibility :- Students, Graduates & Woking Professionals
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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
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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.
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Master Python, Machine Learning, SQL, and Data Visualization with hands-on tutorials & real-world datasets? ๐ฏ
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Best programming language suited for different fields:
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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
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18. Scientific Computing - Fortran, Julia
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โญ๏ธ G-Mail keyboard shortcuts โญ๏ธ
#pc_feature #OldPost
Here is the complete list of Gmail keyboard shortcuts:
Compose and Chat
<Shift> + <Esc> : Focus main window
<Esc> : Focus latest chat or compose
<Ctrl> + . : Advance to next chat or compose
<Ctrl> + , : Advance to previous chat or compose
<Ctrl> + <Enter> : Send
<Ctrl> + <Shift> + c : Add cc recipients
<Ctrl> + <Shift> + b : Add bcc recipients
<Ctrl> + <Shift> + f : Access custom from
<Ctrl> + k : Insert a link
<Ctrl> + ; : Go to previous misspelled word
<Ctrl> + ' : Go to next misspelled word
<Ctrl> + m : Open spelling suggestions
Formatting
<Ctrl> + <Shift> + 5 : Previous font
<Ctrl> + <Shift> + 6 : Next font
<Ctrl> + <Shift> + - : Decrease text size
<Ctrl> + <Shift> + + : Increase text size
<Ctrl> + b : Bold
<Ctrl> + i : Italics
<Ctrl> + u : Underline
<Ctrl> + <Shift> + 7 : Numbered list
<Ctrl> + <Shift> + 8 : Bulleted list
<Ctrl> + <Shift> + 9 : Quote
<Ctrl> + [ : Indent less
<Ctrl> + ] : Indent more
<Ctrl> + <Shift> + l : Align left
<Ctrl> + <Shift> + e : Align center
<Ctrl> + <Shift> + r : Align right
<Ctrl> + <Shift> + , : Set right-to-left
<Ctrl> + <Shift> + . : Set left-to-right
<Ctrl> + \ : Remove formatting
Jumping
g then i : Go to Inbox
g then s : Go to Starred conversations
g then t : Go to Sent messages
g then d : Go to Drafts
g then a : Go to All mail
g then c : Go to Contacts
g then k : Go to Tasks
g then l : Go to Label
Threadlist selection
* then a : Select all conversations
* then n : Deselect all conversations
* then r : Select read conversations
* then u : Select unread conversations
* then s : Select starred conversations
* then t : Select unstarred conversations
Navigation
u : Back to threadlist
k / j : Newer/older conversation
o or <Enter> : Open conversation; collapse/expand conversation
p / n : Read previous/next message
` : Go to next inbox section
~ : Go to previous inbox section
Application
c : Compose
d : Compose in a tab (new compose only)
/ : Search mail
q : Search chat contacts
. : Open "more actions" menu
v : Open "move to" menu
l : Open "label as" menu
? : Open keyboard shortcut help
Actions
, : Move focus to toolbar
x : Select conversation
s : Rotate superstar
y : Remove label
e : Archive
m : Mute conversation
! : Report as spam
# : Delete
r : Reply
<Shift> + r : Reply in a new window
a : Reply all
<Shift> + a : Reply all in a new window
f : Forward
<Shift> + f : Forward in a new window
<Shift> + n : Update conversation
] / [ : Remove conversation from current view and go previous/next
} / { : Archive conversation and go previous/next
z : Undo last action
<Shift> + i : Mark as read
<Shift> + u : Mark as unread
_ : Mark unread from the selected message
+ or = : Mark as important
- : Mark as not important
<Shift> + t : Add conversation to Tasks
#pc_feature #OldPost
Here is the complete list of Gmail keyboard shortcuts:
Compose and Chat
<Shift> + <Esc> : Focus main window
<Esc> : Focus latest chat or compose
<Ctrl> + . : Advance to next chat or compose
<Ctrl> + , : Advance to previous chat or compose
<Ctrl> + <Enter> : Send
<Ctrl> + <Shift> + c : Add cc recipients
<Ctrl> + <Shift> + b : Add bcc recipients
<Ctrl> + <Shift> + f : Access custom from
<Ctrl> + k : Insert a link
<Ctrl> + ; : Go to previous misspelled word
<Ctrl> + ' : Go to next misspelled word
<Ctrl> + m : Open spelling suggestions
Formatting
<Ctrl> + <Shift> + 5 : Previous font
<Ctrl> + <Shift> + 6 : Next font
<Ctrl> + <Shift> + - : Decrease text size
<Ctrl> + <Shift> + + : Increase text size
<Ctrl> + b : Bold
<Ctrl> + i : Italics
<Ctrl> + u : Underline
<Ctrl> + <Shift> + 7 : Numbered list
<Ctrl> + <Shift> + 8 : Bulleted list
<Ctrl> + <Shift> + 9 : Quote
<Ctrl> + [ : Indent less
<Ctrl> + ] : Indent more
<Ctrl> + <Shift> + l : Align left
<Ctrl> + <Shift> + e : Align center
<Ctrl> + <Shift> + r : Align right
<Ctrl> + <Shift> + , : Set right-to-left
<Ctrl> + <Shift> + . : Set left-to-right
<Ctrl> + \ : Remove formatting
Jumping
g then i : Go to Inbox
g then s : Go to Starred conversations
g then t : Go to Sent messages
g then d : Go to Drafts
g then a : Go to All mail
g then c : Go to Contacts
g then k : Go to Tasks
g then l : Go to Label
Threadlist selection
* then a : Select all conversations
* then n : Deselect all conversations
* then r : Select read conversations
* then u : Select unread conversations
* then s : Select starred conversations
* then t : Select unstarred conversations
Navigation
u : Back to threadlist
k / j : Newer/older conversation
o or <Enter> : Open conversation; collapse/expand conversation
p / n : Read previous/next message
` : Go to next inbox section
~ : Go to previous inbox section
Application
c : Compose
d : Compose in a tab (new compose only)
/ : Search mail
q : Search chat contacts
. : Open "more actions" menu
v : Open "move to" menu
l : Open "label as" menu
? : Open keyboard shortcut help
Actions
, : Move focus to toolbar
x : Select conversation
s : Rotate superstar
y : Remove label
e : Archive
m : Mute conversation
! : Report as spam
# : Delete
r : Reply
<Shift> + r : Reply in a new window
a : Reply all
<Shift> + a : Reply all in a new window
f : Forward
<Shift> + f : Forward in a new window
<Shift> + n : Update conversation
] / [ : Remove conversation from current view and go previous/next
} / { : Archive conversation and go previous/next
z : Undo last action
<Shift> + i : Mark as read
<Shift> + u : Mark as unread
_ : Mark unread from the selected message
+ or = : Mark as important
- : Mark as not important
<Shift> + t : Add conversation to Tasks
๐๐ถ๐๐ฐ๐ผ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐
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๐น Introduction to Cybersecurity
๐น Networking Essentials
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Enroll For FREE & Get Certified ๐
Upgrade Your Tech Skills in 2025โFor FREE!
๐น Introduction to Cybersecurity
๐น Networking Essentials
๐น Introduction to Modern AI
๐น Discovering Entrepreneurship
๐น Python for Beginners
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/4chn8Us
Enroll For FREE & Get Certified ๐
๐1