Essential Python Libraries for Data Analytics ๐๐
Python Free Resources: https://t.iss.one/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://t.iss.one/sqlspecialist
Hope it helps :)
Python Free Resources: https://t.iss.one/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://t.iss.one/sqlspecialist
Hope it helps :)
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Things you should do in your 20s: https://t.iss.one/trueminds/526
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Useful Cheatsheets for Free ๐๐
Data Science
SQL
Java Programming
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Pandas in 5 minutes
Python
GIT and Machine Learning
Javascript
HTML
Supervised Learning
Cybersecurity
Generative AI
VS Code
Machine Learning
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Data Science
SQL
Java Programming
PHP
Pandas in 5 minutes
Python
GIT and Machine Learning
Javascript
HTML
Supervised Learning
Cybersecurity
Generative AI
VS Code
Machine Learning
Join @free4unow_backup for more free resourses
ENJOY LEARNING ๐๐
๐5
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]
โค7๐3
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, itโs 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://t.iss.one/datasciencefun
Like if you need similar content ๐๐
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, itโs 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://t.iss.one/datasciencefun
Like if you need similar content ๐๐
๐8โค2
Basics of Machine Learning ๐๐
Free Resources to learn Machine Learning: https://t.iss.one/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 ๐๐
Free Resources to learn Machine Learning: https://t.iss.one/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 ๐๐
๐3โค1
๐ Build Your Career In Data Analytics! ๐
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๐ 2000+ Students Placed
๐ฐ 7.4 LPA Average Package
๐ 41 LPA Highest Package
๐ค 500+ Hiring Partners
Registration link: https://tracking.acciojob.com/g/PUfdDxgHR
Limited Seats, Register Now! โจ
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Hey guys,
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ENJOY LEARNING ๐๐
Here are some best Telegram Channels for free education in 2025
๐๐
Free Courses with Certificate
Web Development Free Resources
Data Science & Machine Learning
Programming Free Books
Python Free Courses
Ethical Hacking & Cyber Security
English Speaking & Communication
Stock Marketing & Investment Banking
Coding Projects
Jobs & Internship Opportunities
Crack your coding Interviews
Udemy Free Courses with Certificate
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Python for Data Engineering role ๐
โ List Comprehensions and Dict Comprehensions
โณ Optimize iteration with one-liners
โณ Fast filtering and transformations
โณ O(n) time complexity
โ Lambda Functions
โณ Anonymous functions for concise operations
โณ Used in map(), filter(), and sort()
โณ Key for functional programming
โ Functional Programming (map, filter, reduce)
โณ Apply transformations efficiently
โณ Reduce dataset size dynamically
โณ Avoid unnecessary loops
โ Iterators and Generators
โณ Efficient memory handling with yield
โณ Streaming large datasets
โณ Lazy evaluation for performance
โ Error Handling with Try-Except
โณ Graceful failure handling
โณ Preventing crashes in pipelines
โณ Custom exception classes
โ Regex for Data Cleaning
โณ Extract structured data from unstructured text
โณ Pattern matching for text processing
โณ Optimized with re.compile()
โ File Handling (CSV, JSON, Parquet)
โณ Read and write structured data efficiently
โณ pandas.read_csv(), json.load(), pyarrow
โณ Handling large files in chunks
โ Handling Missing Data
โณ .fillna(), .dropna(), .interpolate()
โณ Imputing missing values
โณ Reducing nulls for better analytics
โ Pandas Operations
โณ DataFrame filtering and aggregations
โณ .groupby(), .pivot_table(), .merge()
โณ Handling large structured datasets
โ SQL Queries in Python
โณ Using sqlalchemy and pandas.read_sql()
โณ Writing optimized queries
โณ Connecting to databases
โซ Working with APIs
โณ Fetching data with requests and httpx
โณ Handling rate limits and retries
โณ Parsing JSON/XML responses
โฌ Cloud Data Handling (AWS S3, Google Cloud, Azure)
โณ Upload/download data from cloud storage
โณ boto3, gcsfs, azure-storage
โณ Handling large-scale data ingestion
๐๐ก๐ ๐๐๐ฌ๐ญ ๐ฐ๐๐ฒ ๐ญ๐จ ๐ฅ๐๐๐ซ๐ง ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ข๐ฌ ๐ง๐จ๐ญ ๐ฃ๐ฎ๐ฌ๐ญ ๐๐ฒ ๐ฌ๐ญ๐ฎ๐๐ฒ๐ข๐ง๐ , ๐๐ฎ๐ญ ๐๐ฒ ๐ข๐ฆ๐ฉ๐ฅ๐๐ฆ๐๐ง๐ญ๐ข๐ง๐ ๐ข๐ญ
Join for more data engineering resources: https://t.iss.one/sql_engineer
โ List Comprehensions and Dict Comprehensions
โณ Optimize iteration with one-liners
โณ Fast filtering and transformations
โณ O(n) time complexity
โ Lambda Functions
โณ Anonymous functions for concise operations
โณ Used in map(), filter(), and sort()
โณ Key for functional programming
โ Functional Programming (map, filter, reduce)
โณ Apply transformations efficiently
โณ Reduce dataset size dynamically
โณ Avoid unnecessary loops
โ Iterators and Generators
โณ Efficient memory handling with yield
โณ Streaming large datasets
โณ Lazy evaluation for performance
โ Error Handling with Try-Except
โณ Graceful failure handling
โณ Preventing crashes in pipelines
โณ Custom exception classes
โ Regex for Data Cleaning
โณ Extract structured data from unstructured text
โณ Pattern matching for text processing
โณ Optimized with re.compile()
โ File Handling (CSV, JSON, Parquet)
โณ Read and write structured data efficiently
โณ pandas.read_csv(), json.load(), pyarrow
โณ Handling large files in chunks
โ Handling Missing Data
โณ .fillna(), .dropna(), .interpolate()
โณ Imputing missing values
โณ Reducing nulls for better analytics
โ Pandas Operations
โณ DataFrame filtering and aggregations
โณ .groupby(), .pivot_table(), .merge()
โณ Handling large structured datasets
โ SQL Queries in Python
โณ Using sqlalchemy and pandas.read_sql()
โณ Writing optimized queries
โณ Connecting to databases
โซ Working with APIs
โณ Fetching data with requests and httpx
โณ Handling rate limits and retries
โณ Parsing JSON/XML responses
โฌ Cloud Data Handling (AWS S3, Google Cloud, Azure)
โณ Upload/download data from cloud storage
โณ boto3, gcsfs, azure-storage
โณ Handling large-scale data ingestion
๐๐ก๐ ๐๐๐ฌ๐ญ ๐ฐ๐๐ฒ ๐ญ๐จ ๐ฅ๐๐๐ซ๐ง ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ข๐ฌ ๐ง๐จ๐ญ ๐ฃ๐ฎ๐ฌ๐ญ ๐๐ฒ ๐ฌ๐ญ๐ฎ๐๐ฒ๐ข๐ง๐ , ๐๐ฎ๐ญ ๐๐ฒ ๐ข๐ฆ๐ฉ๐ฅ๐๐ฆ๐๐ง๐ญ๐ข๐ง๐ ๐ข๐ญ
Join for more data engineering resources: https://t.iss.one/sql_engineer
โค2๐1