🖥 Free Courses on Large Language Models
▪ChatGPT Prompt Engineering for Developers
▪LangChain for LLM Application Development
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▪Introduction to Large Language Models with Google Cloud
▪LLM University
▪Full Stack LLM Bootcamp
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▪ChatGPT Prompt Engineering for Developers
▪LangChain for LLM Application Development
▪Building Systems with the ChatGPT API
▪Google Cloud Generative AI Learning Path
▪Introduction to Large Language Models with Google Cloud
▪LLM University
▪Full Stack LLM Bootcamp
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👍1
Creating Virtual Environment for Python
» Download Python
» Steps to create '
1. Navigate to the folder where you want to make your project
Example:
2. Open terminal (local terminal, command prompt, or vs code terminal) in that folder
3. Now, use these commands
4. Your virtual environment is created in that folder, now activate this virtual environment using this command.
Command for 'Command Prompt':
Command for 'Powershell':
Command for Git Bash or WSL:
If Powershell gives you error like
5. Congratulations🎊 Your virtual environment activated now make your project
Happy Coding 👨💻
» Download Python
First you need python installed in your local machine to create virtual environment.
Download Python from Here
» Steps to create '
.env' folder (virtual environment for python)1. Navigate to the folder where you want to make your project
Example:
cd D:/code/
2. Open terminal (local terminal, command prompt, or vs code terminal) in that folder
3. Now, use these commands
python --version # Type this and hit enter to verify the python version
# Now use these commands
python -m venv .env
4. Your virtual environment is created in that folder, now activate this virtual environment using this command.
Command for 'Command Prompt':
.\env\Scripts\activate
Command for 'Powershell':
.\env\Scripts\Activate.ps1
Command for Git Bash or WSL:
source \.env\bin\activate
If Powershell gives you error like
File cannot be loaded because running noscripts is disabled then use this command!Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
5. Congratulations🎊 Your virtual environment activated now make your project
Happy Coding 👨💻
❤4👍2
Essential Python Libraries for Data Analytics 😄👇
Python Free Resources: https://news.1rj.ru/str/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Python Free Resources: https://news.1rj.ru/str/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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Things you should do in your 20s: https://news.1rj.ru/str/trueminds/526
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Useful Cheatsheets for Free 😄👇
Data Science
SQL
Java Programming
PHP
Pandas in 5 minutes
Python
GIT and Machine Learning
Javanoscript
HTML
Supervised Learning
Cybersecurity
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VS Code
Machine Learning
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Data Science
SQL
Java Programming
PHP
Pandas in 5 minutes
Python
GIT and Machine Learning
Javanoscript
HTML
Supervised Learning
Cybersecurity
Generative AI
VS Code
Machine Learning
Join @free4unow_backup for more free resourses
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👍5
10 ChatGPT Prompts To Transform Your Life
1. Use the 80/20 principle to learn faster
Prompt: "I want to learn about [insert topic].
Identify and share the most important 20% of learnings from this topic to help me understand 80%."
2. Improve your writing
Prompt: [Paste your writing] "Proofread my writing above. Fix grammar and spelling mistakes. And make suggestions that will improve the clarity of my writing."
3. Turn ChatGPT into your intern
Prompt: "I am creating a report about [insert topic].
Research and create an in-depth report with a step-by-step guide that will help readers understand how to [insert outcome]."
4. Learn any new skill
Prompt: "I want to learn [insert desired skill].
Create a 30-day learning plan to help a beginner like me learn and improve this skill."
5. Strengthen your learning
Prompt: "I am learning about [insert topic].
Ask me a series of questions that will test my knowledge. Identify knowledge gaps in my answers and give me better answers to fill those gaps."
6. Train ChatGPT to generate prompts
Prompt: "You are an Al designed to help [insert profession]. Generate a list of the 10 best prompts for yourself. The prompts should be about [insert topic]."
7. Mastering a hobby
Prompt: "Create structured learning paths for [Hobby]. Break it down into daily skill-building exercises. Design a system for validating progress.
Include a relationship between enjoyment and effort. Create opportunities for skill demonstration."
8. Learn any complex topic in seconds
Prompt: "Explain [insert topic] in simple and easy terms that even a 8 year old kid can understand."
9. Generate new ideas
Prompt: "I want to [insert task or goal]. Generate [insert desired outcome] for [insert task or goal]."
10. Summarize long documents
Prompt: "Summarize the text below and give me a list of bullet points with key insights and the most important facts." [Paste your text]
1. Use the 80/20 principle to learn faster
Prompt: "I want to learn about [insert topic].
Identify and share the most important 20% of learnings from this topic to help me understand 80%."
2. Improve your writing
Prompt: [Paste your writing] "Proofread my writing above. Fix grammar and spelling mistakes. And make suggestions that will improve the clarity of my writing."
3. Turn ChatGPT into your intern
Prompt: "I am creating a report about [insert topic].
Research and create an in-depth report with a step-by-step guide that will help readers understand how to [insert outcome]."
4. Learn any new skill
Prompt: "I want to learn [insert desired skill].
Create a 30-day learning plan to help a beginner like me learn and improve this skill."
5. Strengthen your learning
Prompt: "I am learning about [insert topic].
Ask me a series of questions that will test my knowledge. Identify knowledge gaps in my answers and give me better answers to fill those gaps."
6. Train ChatGPT to generate prompts
Prompt: "You are an Al designed to help [insert profession]. Generate a list of the 10 best prompts for yourself. The prompts should be about [insert topic]."
7. Mastering a hobby
Prompt: "Create structured learning paths for [Hobby]. Break it down into daily skill-building exercises. Design a system for validating progress.
Include a relationship between enjoyment and effort. Create opportunities for skill demonstration."
8. Learn any complex topic in seconds
Prompt: "Explain [insert topic] in simple and easy terms that even a 8 year old kid can understand."
9. Generate new ideas
Prompt: "I want to [insert task or goal]. Generate [insert desired outcome] for [insert task or goal]."
10. Summarize long documents
Prompt: "Summarize the text below and give me a list of bullet points with key insights and the most important facts." [Paste your text]
❤8👍3
Are you looking to become a machine learning engineer?
I created a free and comprehensive roadmap. Let's go through this post and explore what you need to know to become an expert machine learning engineer:
Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.
Here are the probability units you will need to focus on:
Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra
Python:
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking
Machine Learning Prerequisites:
Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data
Machine Learning Fundamentals
Using scikit-learn library in combination with other Python libraries for:
Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)
Solving two types of problems:
Regression
Classification
Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.
In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.
Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models
Machine Learning Project Deployment
Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:
Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
I created a free and comprehensive roadmap. Let's go through this post and explore what you need to know to become an expert machine learning engineer:
Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.
Here are the probability units you will need to focus on:
Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra
Python:
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking
Machine Learning Prerequisites:
Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data
Machine Learning Fundamentals
Using scikit-learn library in combination with other Python libraries for:
Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)
Solving two types of problems:
Regression
Classification
Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.
In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.
Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models
Machine Learning Project Deployment
Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:
Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
👍8❤3
Basics of Machine Learning 👇👇
Free Resources to learn Machine Learning: https://news.1rj.ru/str/free4unow_backup/587
Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:
1. Supervised Learning: The algorithm is trained on a labeled dataset, learning to map input to output. For example, it can predict housing prices based on features like size and location.
2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.
3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.
Key concepts include:
- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.
- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.
- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.
- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.
In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.
Join @datasciencefun for more
ENJOY LEARNING 👍👍
Free Resources to learn Machine Learning: https://news.1rj.ru/str/free4unow_backup/587
Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:
1. Supervised Learning: The algorithm is trained on a labeled dataset, learning to map input to output. For example, it can predict housing prices based on features like size and location.
2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.
3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.
Key concepts include:
- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.
- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.
- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.
- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.
In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.
Join @datasciencefun for more
ENJOY LEARNING 👍👍
👍3❤1
🎓 Build Your Career In Data Analytics! 📊
🌟 2000+ Students Placed
💰 7.4 LPA Average Package
🚀 41 LPA Highest Package
🤝 500+ Hiring Partners
Registration link: https://tracking.acciojob.com/g/PUfdDxgHR
Limited Seats, Register Now! ✨
🌟 2000+ Students Placed
💰 7.4 LPA Average Package
🚀 41 LPA Highest Package
🤝 500+ Hiring Partners
Registration link: https://tracking.acciojob.com/g/PUfdDxgHR
Limited Seats, Register Now! ✨
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Here are some best Telegram Channels for free education in 2025
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Free Courses with Certificate
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Data Science & Machine Learning
Programming Free Books
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Python for Data Engineering role 👇
➊ List Comprehensions and Dict Comprehensions
↳ Optimize iteration with one-liners
↳ Fast filtering and transformations
↳ O(n) time complexity
➋ Lambda Functions
↳ Anonymous functions for concise operations
↳ Used in map(), filter(), and sort()
↳ Key for functional programming
➌ Functional Programming (map, filter, reduce)
↳ Apply transformations efficiently
↳ Reduce dataset size dynamically
↳ Avoid unnecessary loops
➍ Iterators and Generators
↳ Efficient memory handling with yield
↳ Streaming large datasets
↳ Lazy evaluation for performance
➎ Error Handling with Try-Except
↳ Graceful failure handling
↳ Preventing crashes in pipelines
↳ Custom exception classes
➏ Regex for Data Cleaning
↳ Extract structured data from unstructured text
↳ Pattern matching for text processing
↳ Optimized with re.compile()
➐ File Handling (CSV, JSON, Parquet)
↳ Read and write structured data efficiently
↳ pandas.read_csv(), json.load(), pyarrow
↳ Handling large files in chunks
➑ Handling Missing Data
↳ .fillna(), .dropna(), .interpolate()
↳ Imputing missing values
↳ Reducing nulls for better analytics
➒ Pandas Operations
↳ DataFrame filtering and aggregations
↳ .groupby(), .pivot_table(), .merge()
↳ Handling large structured datasets
➓ SQL Queries in Python
↳ Using sqlalchemy and pandas.read_sql()
↳ Writing optimized queries
↳ Connecting to databases
⓫ Working with APIs
↳ Fetching data with requests and httpx
↳ Handling rate limits and retries
↳ Parsing JSON/XML responses
⓬ Cloud Data Handling (AWS S3, Google Cloud, Azure)
↳ Upload/download data from cloud storage
↳ boto3, gcsfs, azure-storage
↳ Handling large-scale data ingestion
𝐓𝐡𝐞 𝐛𝐞𝐬𝐭 𝐰𝐚𝐲 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 𝐢𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐛𝐲 𝐬𝐭𝐮𝐝𝐲𝐢𝐧𝐠, 𝐛𝐮𝐭 𝐛𝐲 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐢𝐭
Join for more data engineering resources: https://news.1rj.ru/str/sql_engineer
➊ List Comprehensions and Dict Comprehensions
↳ Optimize iteration with one-liners
↳ Fast filtering and transformations
↳ O(n) time complexity
➋ Lambda Functions
↳ Anonymous functions for concise operations
↳ Used in map(), filter(), and sort()
↳ Key for functional programming
➌ Functional Programming (map, filter, reduce)
↳ Apply transformations efficiently
↳ Reduce dataset size dynamically
↳ Avoid unnecessary loops
➍ Iterators and Generators
↳ Efficient memory handling with yield
↳ Streaming large datasets
↳ Lazy evaluation for performance
➎ Error Handling with Try-Except
↳ Graceful failure handling
↳ Preventing crashes in pipelines
↳ Custom exception classes
➏ Regex for Data Cleaning
↳ Extract structured data from unstructured text
↳ Pattern matching for text processing
↳ Optimized with re.compile()
➐ File Handling (CSV, JSON, Parquet)
↳ Read and write structured data efficiently
↳ pandas.read_csv(), json.load(), pyarrow
↳ Handling large files in chunks
➑ Handling Missing Data
↳ .fillna(), .dropna(), .interpolate()
↳ Imputing missing values
↳ Reducing nulls for better analytics
➒ Pandas Operations
↳ DataFrame filtering and aggregations
↳ .groupby(), .pivot_table(), .merge()
↳ Handling large structured datasets
➓ SQL Queries in Python
↳ Using sqlalchemy and pandas.read_sql()
↳ Writing optimized queries
↳ Connecting to databases
⓫ Working with APIs
↳ Fetching data with requests and httpx
↳ Handling rate limits and retries
↳ Parsing JSON/XML responses
⓬ Cloud Data Handling (AWS S3, Google Cloud, Azure)
↳ Upload/download data from cloud storage
↳ boto3, gcsfs, azure-storage
↳ Handling large-scale data ingestion
𝐓𝐡𝐞 𝐛𝐞𝐬𝐭 𝐰𝐚𝐲 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 𝐢𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐛𝐲 𝐬𝐭𝐮𝐝𝐲𝐢𝐧𝐠, 𝐛𝐮𝐭 𝐛𝐲 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐢𝐭
Join for more data engineering resources: https://news.1rj.ru/str/sql_engineer
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