Numpy_Cheat_Sheet.pdf
4.8 MB
NumPy Cheat Sheet: Data Analysis in Python
This #Python cheat sheet is a quick reference for #NumPy beginners.
Learn more:
https://www.datacamp.com/cheat-sheet/numpy-cheat-sheet-data-analysis-in-python
https://news.1rj.ru/str/DataAnalyticsX
This #Python cheat sheet is a quick reference for #NumPy beginners.
Learn more:
https://www.datacamp.com/cheat-sheet/numpy-cheat-sheet-data-analysis-in-python
https://news.1rj.ru/str/DataAnalyticsX
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Forwarded from Learn Python Hub
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://news.1rj.ru/str/addlist/8_rRW2scgfRhOTc0
✅ https://news.1rj.ru/str/Codeprogrammer
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🤖 Automating Research with NotebookLM
Notebooklm-py is an unofficial library for working with Google NotebookLM, allowing you to automate research processes, generate content, and integrate AI agents. It's suitable for prototypes and personal projects, using Python or the command line.
🚀 Key features:
- Integration with AI agents and Claude Code
- Automating research with source importing
- Generating podcasts, videos, and educational materials
- Support for working via the Python API and CLI
- Use with unofficial Google APIs
📌 GitHub: https://github.com/teng-lin/notebooklm-py
https://news.1rj.ru/str/DataAnalyticsX
Notebooklm-py is an unofficial library for working with Google NotebookLM, allowing you to automate research processes, generate content, and integrate AI agents. It's suitable for prototypes and personal projects, using Python or the command line.
🚀 Key features:
- Integration with AI agents and Claude Code
- Automating research with source importing
- Generating podcasts, videos, and educational materials
- Support for working via the Python API and CLI
- Use with unofficial Google APIs
📌 GitHub: https://github.com/teng-lin/notebooklm-py
https://news.1rj.ru/str/DataAnalyticsX
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Forwarded from Machine Learning
🚀 Machine Learning Workflow: Step-by-Step Breakdown
Understanding the ML pipeline is essential to build scalable, production-grade models.
👉 Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.
👉 Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.
👉 Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.
👉 Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.
👉 Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.
👉 Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.
👉 Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.
👉 Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.
👉 Model Evaluation
Use task-specific metrics:
- Classification – MCC, Sensitivity, Specificity, Accuracy
- Regression – RMSE, R², MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.
💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.
https://news.1rj.ru/str/DataScienceM
Understanding the ML pipeline is essential to build scalable, production-grade models.
👉 Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.
👉 Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.
👉 Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.
👉 Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.
👉 Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.
👉 Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.
👉 Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.
👉 Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.
👉 Model Evaluation
Use task-specific metrics:
- Classification – MCC, Sensitivity, Specificity, Accuracy
- Regression – RMSE, R², MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.
💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.
https://news.1rj.ru/str/DataScienceM
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Forwarded from Machine Learning
Effective Pandas 2: Opinionated Patterns for Data Manipulation
This book is now available at a discounted price through our Patreon grant:
Original Price: $53
Discounted Price: $12
Limited to 15 copies
Buy: https://www.patreon.com/posts/effective-pandas-150394542
This book is now available at a discounted price through our Patreon grant:
Discounted Price: $12
Limited to 15 copies
Buy: https://www.patreon.com/posts/effective-pandas-150394542
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Forwarded from Machine Learning with Python
👨🏻💻 When I was just starting out and trying to get into the "data" field, I had no one to guide me, nor did I know what exactly I should study. To be honest, I was confused for months and felt lost.
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These 9 lectures from Stanford are a pure goldmine for anyone wanting to learn and understand LLMs in depth
Lecture 1 - Transformer: https://lnkd.in/dGnQW39t
Lecture 2 - Transformer-Based Models & Tricks: https://lnkd.in/dT_VEpVH
Lecture 3 - Tranformers & Large Language Models: https://lnkd.in/dwjjpjaP
Lecture 4 - LLM Training: https://lnkd.in/dSi_xCEN
Lecture 5 - LLM tuning: https://lnkd.in/dUK5djpB
Lecture 6 - LLM Reasoning: https://lnkd.in/dAGQTNAM
Lecture 7 - Agentic LLMs: https://lnkd.in/dWD4j7vm
Lecture 8 - LLM Evaluation: https://lnkd.in/ddxE5zvb
Lecture 9 - Recap & Current Trends: https://lnkd.in/dGsTd8jN
Start understanding #LLMs in depth from the experts. Go through each step-by-step video.
https://news.1rj.ru/str/DataAnalyticsX🔗
Lecture 1 - Transformer: https://lnkd.in/dGnQW39t
Lecture 2 - Transformer-Based Models & Tricks: https://lnkd.in/dT_VEpVH
Lecture 3 - Tranformers & Large Language Models: https://lnkd.in/dwjjpjaP
Lecture 4 - LLM Training: https://lnkd.in/dSi_xCEN
Lecture 5 - LLM tuning: https://lnkd.in/dUK5djpB
Lecture 6 - LLM Reasoning: https://lnkd.in/dAGQTNAM
Lecture 7 - Agentic LLMs: https://lnkd.in/dWD4j7vm
Lecture 8 - LLM Evaluation: https://lnkd.in/ddxE5zvb
Lecture 9 - Recap & Current Trends: https://lnkd.in/dGsTd8jN
Start understanding #LLMs in depth from the experts. Go through each step-by-step video.
https://news.1rj.ru/str/DataAnalyticsX
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Hands-On Large Language Models
Inside:
Chapter 1: Introduction to Language Models
Chapter 2: Tokens and Embeddings
Chapter 3: Understanding the Transformer LLM from Inside
Chapter 4: Text Classification
Chapter 5: Text Clustering and Topic Modeling
Chapter 6: Prompt Engineering
Chapter 7: Advanced Techniques and Tools for Text Generation
Chapter 8: Semantic Search and Retrieval-Augmented Generation (RAG)
Chapter 9: Multimodal Large Language Models
Chapter 10: Creating Text Embedding Models
Chapter 11: Fine-Tuning Representation Models for Classification
Chapter 12: Fine-Tuning Generation Models
GitHub: http://github.com/HandsOnLLM/Hands-On-Large-Language-Models
👉 https://news.1rj.ru/str/DataAnalyticsX
Inside:
Chapter 1: Introduction to Language Models
Chapter 2: Tokens and Embeddings
Chapter 3: Understanding the Transformer LLM from Inside
Chapter 4: Text Classification
Chapter 5: Text Clustering and Topic Modeling
Chapter 6: Prompt Engineering
Chapter 7: Advanced Techniques and Tools for Text Generation
Chapter 8: Semantic Search and Retrieval-Augmented Generation (RAG)
Chapter 9: Multimodal Large Language Models
Chapter 10: Creating Text Embedding Models
Chapter 11: Fine-Tuning Representation Models for Classification
Chapter 12: Fine-Tuning Generation Models
GitHub: http://github.com/HandsOnLLM/Hands-On-Large-Language-Models
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Cheat sheet NumPy: basics of ndarray arrays, creation and data types, indexing and slicing, vectorized operations, aggregation (mean, sum, std), boolean logic, sorting, working with random numbers and basic shape transformations
https://news.1rj.ru/str/DataAnalyticsX
https://news.1rj.ru/str/DataAnalyticsX
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Forwarded from Machine Learning with Python
A full-fledged educational course has been published on the university's website: 24 lectures, practical tasks, homework assignments, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
A great opportunity to study deep learning based on the structure of a top university, free of charge and without simplifications — let's learn here.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/resources/lecture-videos/
tags: #python #deeplearning
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Forwarded from Machine Learning with Python
Data Science Roadmap.pdf
15.5 MB
🏷 Comprehensive Data Science Roadmap Notes
✅ This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.
🕡 From mastering Python and SQL to cleaning data and working with cloud tools, which are prerequisites for any project.
🕑 How to extract real analysis reports and strategies from raw data using statistics and visualization tools.
🕗 You will learn everything from machine learning and advanced algorithms to precise model evaluation.
🕙 Get familiar with neural networks, generative artificial intelligence, and language models to have a voice in today's modern world.
🕧 How to build real projects and portfolios that are exactly what hiring managers and big companies are looking for.
🌐 #DataScience #DataScience #pytorch #python #Roadmap
https://news.1rj.ru/str/CodeProgrammer
✅ This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.
🕡 From mastering Python and SQL to cleaning data and working with cloud tools, which are prerequisites for any project.
🕑 How to extract real analysis reports and strategies from raw data using statistics and visualization tools.
🕗 You will learn everything from machine learning and advanced algorithms to precise model evaluation.
🕙 Get familiar with neural networks, generative artificial intelligence, and language models to have a voice in today's modern world.
🕧 How to build real projects and portfolios that are exactly what hiring managers and big companies are looking for.
🌐 #DataScience #DataScience #pytorch #python #Roadmap
https://news.1rj.ru/str/CodeProgrammer
❤3
Forwarded from Machine Learning with Python
🎯 2026 IT Certification Prep Kit – Free!
🔥Whether you're preparing for #Python, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #AI, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification – SPOTO has got you covered!
✅ What’s Inside:
・Free Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS courses: https://bit.ly/3M9h5su
・IT Certs E-book: https://bit.ly/3Mlu5ez
・IT Exams Skill Test: https://bit.ly/3NVrgRU
・Free Cloud Study Guide: https://bit.ly/4kgFVDs
・Free AI material and support tools：https://bit.ly/46qvpDX
👉 Become Part of Our IT Learning Circle! resources and support:
https://chat.whatsapp.com/FlG2rOYVySLEHLKXF3nKGB
💬 Want exam help? Chat with an admin now!
wa.link/8fy3x4
🔥Whether you're preparing for #Python, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #AI, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification – SPOTO has got you covered!
✅ What’s Inside:
・Free Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS courses: https://bit.ly/3M9h5su
・IT Certs E-book: https://bit.ly/3Mlu5ez
・IT Exams Skill Test: https://bit.ly/3NVrgRU
・Free Cloud Study Guide: https://bit.ly/4kgFVDs
・Free AI material and support tools：https://bit.ly/46qvpDX
👉 Become Part of Our IT Learning Circle! resources and support:
https://chat.whatsapp.com/FlG2rOYVySLEHLKXF3nKGB
💬 Want exam help? Chat with an admin now!
wa.link/8fy3x4
Forwarded from Machine Learning with Python
❤1
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://news.1rj.ru/str/addlist/8_rRW2scgfRhOTc0
✅ https://news.1rj.ru/str/Codeprogrammer
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Cheatsheet for Pandas to Polar
Getting started with Polars? This post shows you how to convert some familar Pandas commands to #Polars. But it also tries to go beyond that to introduce you to some of the more fundamental differences between Pandas and Polars.
https://www.rhosignal.com/posts/polars-pandas-cheatsheet/
https://news.1rj.ru/str/DataAnalyticsX 🔴
Getting started with Polars? This post shows you how to convert some familar Pandas commands to #Polars. But it also tries to go beyond that to introduce you to some of the more fundamental differences between Pandas and Polars.
https://www.rhosignal.com/posts/polars-pandas-cheatsheet/
https://news.1rj.ru/str/DataAnalyticsX 🔴
Rho Signal
Cheatsheet for Pandas to Polars
Getting started with Polars? This post shows you how to convert some familar Pandas commands to Polars. But it also tries to go beyond that to introduce you to some of the more fundamental differences between Pandas and Polars.
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#Polars Cheat Sheet with example
Learn and Run online: https://colab.research.google.com/github/FranzDiebold/polars-cheat-sheet/blob/main/polars-cheat-sheet.ipynb
https://news.1rj.ru/str/DataAnalyticsX🔴
Learn and Run online: https://colab.research.google.com/github/FranzDiebold/polars-cheat-sheet/blob/main/polars-cheat-sheet.ipynb
https://news.1rj.ru/str/DataAnalyticsX
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Google
polars-cheat-sheet.ipynb
Run, share, and edit Python notebooks
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