PANDAS — CHEAT SHEET
1. DATA LOADING
2. DATA ANALYSIS
3. DATA SELECTION
4. DATA CLEANING
5. ANALYTICS
6. DATA MERGING
⭐ TOP 10 METHODS
read_csv() head() info() loc[] iloc[] query() groupby() merge() fillna() sort_values()
1. DATA LOADING
Method | What it does
----------------+--------------------
pd.read_csv() | Reads CSV file
pd.read_excel() | Reads Excel file
pd.read_sql() | Reads data from SQL
pd.read_json() | Reads JSON file
2. DATA ANALYSIS
Method | What it does
--------------+---------------------------
df.head() | Shows first rows
df.info() | Table information
df.describe() | Statistics by columns
df.shape | Table size (rows, columns)
df.columns | List of column names
3. DATA SELECTION
Method | What it does
-----------+----------------------------------
df.loc[] | Selection by row and column names
df.iloc[] | Selection by indices
df.query() | Filtering by condition
4. DATA CLEANING
Method | What it does
---------------------+--------------------------------
df.isnull() | Check for missing values (NULL)
df.dropna() | Remove rows with missing values
df.fillna() | Fill missing values
df.drop_duplicates() | Remove duplicates
df.astype() | Change data type
5. ANALYTICS
Method | What it does
------------------+----------------------------
df.groupby() | Data grouping
df.agg() | Aggregation in groups
df.value_counts() | Count of unique values
df.mean() | Mean value
df.median() | Median
df.corr() | Correlation between columns
6. DATA MERGING
Method | What it does
------------+---------------------
pd.merge() | SQL JOIN by column
pd.join() | JOIN by index
pd.concat() | Glue tables together
⭐ TOP 10 METHODS
read_csv() head() info() loc[] iloc[] query() groupby() merge() fillna() sort_values()
❤11💩1
500 AI/ML/Computer Vision/NLP projects with code 🚀
This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP 🧠
All examples come with code, so you can not just read them, but immediately analyze and run them ⚙️
➡️ Link to GitHub:
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This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP 🧠
All examples come with code, so you can not just read them, but immediately analyze and run them ⚙️
➡️ Link to GitHub:
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There are hundreds of AI channels on YouTube. Here's why we made another one.
Most AI content does one of two things: it stays so surface-level it teaches you nothing, or it goes so deep you need a PhD to follow along.
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→ AI vs ML vs Deep Learning vs GenAI ... But Done Right!
The terms everyone uses. The distinctions are almost never explained clearly. We fix that: youtu.be/72yyLA2wRWc
→ How to Break into AI Engineering in 2026
A senior applied scientist shares what actually matters: youtu.be/42vE7Ij4kdU
If AI has ever felt overwhelming or noisy, this channel is for you. If the content resonates with you, please don’t forget to like and subscribe.
Most AI content does one of two things: it stays so surface-level it teaches you nothing, or it goes so deep you need a PhD to follow along.
We built Guidely for everyone in between.
→ We start with absolute beginners in mind
→ Then take you deeper, until the details actually click
→ Every guide is reviewed by experienced AI engineers
→ We don't make more content. We make better content.
Whether you build, design, or market products, our goal is simple: leave you thinking "I've never seen it broken down this well."
Two good places to start 👇
→ AI vs ML vs Deep Learning vs GenAI ... But Done Right!
The terms everyone uses. The distinctions are almost never explained clearly. We fix that: youtu.be/72yyLA2wRWc
→ How to Break into AI Engineering in 2026
A senior applied scientist shares what actually matters: youtu.be/42vE7Ij4kdU
If AI has ever felt overwhelming or noisy, this channel is for you. If the content resonates with you, please don’t forget to like and subscribe.
YouTube
AI vs ML vs Deep Learning vs GenAI ... But Done Right!
Our blog comparing AI, Machine Learning, Deep Learning, and Generative AI has gained a lot of traction. If you prefer reading, you can check it out here:
https://guidely.tech/blog/ai-vs-machine-learning-vs-deep-learning-vs-genai
But we know not everyone…
https://guidely.tech/blog/ai-vs-machine-learning-vs-deep-learning-vs-genai
But we know not everyone…
❤6
Forwarded from Machine Learning with Python
A guide to Loop Engineering has been released — a new approach to working with AI agents
The repository loop-engineering has been published, offering a paradigm shift: instead of manually prompting AI agents, the developer designs a cycle that does this automatically. 🔄🤖
The author notes that most people still use Claude Code, Codex, Cursor, and Grok as a regular chat: prompt → wait → copy → correct → prompt again. Loop Engineering proposes to stop being a "nanny" for the agent and instead build a system where agents work, check, correct, and escalate on their own. 🛠️⚙️
The repository includes ready-made cycles for daily triage, PR, CI, dependencies, changelog, and issues. It includes CLI for creating cycles, evaluating tokens, auditing the repository, and safely running agents via GitHub Actions. 📋✅
"Prompt engineering was about how to write better prompts. Loop engineering is about creating a system where agents continue to work without your supervision at every step," the description says. 🚀🧠
The repository is available on GitHub.
Repository: https://github.com/cobusgreyling/loop-engineering
#LoopEngineering #AI #Agents #GitHub #DevOps #Automation
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The repository loop-engineering has been published, offering a paradigm shift: instead of manually prompting AI agents, the developer designs a cycle that does this automatically. 🔄🤖
The author notes that most people still use Claude Code, Codex, Cursor, and Grok as a regular chat: prompt → wait → copy → correct → prompt again. Loop Engineering proposes to stop being a "nanny" for the agent and instead build a system where agents work, check, correct, and escalate on their own. 🛠️⚙️
The repository includes ready-made cycles for daily triage, PR, CI, dependencies, changelog, and issues. It includes CLI for creating cycles, evaluating tokens, auditing the repository, and safely running agents via GitHub Actions. 📋✅
"Prompt engineering was about how to write better prompts. Loop engineering is about creating a system where agents continue to work without your supervision at every step," the description says. 🚀🧠
The repository is available on GitHub.
Repository: https://github.com/cobusgreyling/loop-engineering
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❤5
A Chinese developer has released an open-source replacement for NumPy that performs calculations on GPUs. It's called CuPy 🚀. In many cases, it's enough to replace a single line:
The same code can run on CUDA up to 100 times faster ⚡️.
What it can do:
→ Compatible with existing NumPy and SciPy code 🛠️.
→ No need to rewrite the program or learn new syntax 📝.
→ Supports not only CUDA but also AMD ROCm 💻.
The project is completely open-source 📂:
🔗 https://github.com/cupy/cupy
#Python #GPU #NumPy #CuPy #AI #DeepLearning
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import cupy as cp
The same code can run on CUDA up to 100 times faster ⚡️.
What it can do:
→ Compatible with existing NumPy and SciPy code 🛠️.
→ No need to rewrite the program or learn new syntax 📝.
→ Supports not only CUDA but also AMD ROCm 💻.
The project is completely open-source 📂:
🔗 https://github.com/cupy/cupy
#Python #GPU #NumPy #CuPy #AI #DeepLearning
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Don't learn ML by randomly jumping through tutorials. 🚫📚
DS-ML Bootcamp is a public repository for a Data Science and machine learning course for beginners who want a structured path from zero to practical projects. 🚀📊
It helps transition from installation and concepts to practical ML work, organizing lessons, assignments, code examples, datasets, and solutions around the main machine learning workflow. 🛠️🧠
Key features:
- End-to-end workflow - covers data collection, preprocessing, train/test split, model selection, training, evaluation, and deployment 🔄📈
- Lesson-based structure - starts with tools/setup, Data Science, ML, data fundamentals, and regression 📚🧮
- Practical materials - assignments give learners structured tasks, not just reading notes ✍️✅
- Code + datasets - Python examples and raw CSV datasets included for exercises 🐍📂
- Set up for repetition - the README says you can clone the repository and use Jupyter or VS Code while going through lessons 💻🔁
Free public repository on GitHub. 🆓
https://github.com/goobolabs/ds-ml-bootcamp
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DS-ML Bootcamp is a public repository for a Data Science and machine learning course for beginners who want a structured path from zero to practical projects. 🚀📊
It helps transition from installation and concepts to practical ML work, organizing lessons, assignments, code examples, datasets, and solutions around the main machine learning workflow. 🛠️🧠
Key features:
- End-to-end workflow - covers data collection, preprocessing, train/test split, model selection, training, evaluation, and deployment 🔄📈
- Lesson-based structure - starts with tools/setup, Data Science, ML, data fundamentals, and regression 📚🧮
- Practical materials - assignments give learners structured tasks, not just reading notes ✍️✅
- Code + datasets - Python examples and raw CSV datasets included for exercises 🐍📂
- Set up for repetition - the README says you can clone the repository and use Jupyter or VS Code while going through lessons 💻🔁
Free public repository on GitHub. 🆓
https://github.com/goobolabs/ds-ml-bootcamp
#MachineLearning #DataScience #Coding #Python #AI #Learning
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GitHub
GitHub - goobolabs/ds-ml-bootcamp: Data Science and Machine Learning Bootcamp. (Jun - 2026)
Data Science and Machine Learning Bootcamp. (Jun - 2026) - goobolabs/ds-ml-bootcamp
❤6
The math.perm() method
The math.perm() method in Python returns the number of ways to select k elements from n elements, with and without repetition. 🧮
Syntax:
Where:
n: The number of elements from which k elements are selected.
k: The number of elements that are selected.
In the first example, the method returns the number of ways to select 3 elements from 5 elements. The result is 60 ways. 📊
In the second example, the method returns the number of ways to select 5 elements from 10 elements. The result is 252 ways. 🚀
#Python #Math #Coding #Programming #DataScience #Tech
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The math.perm() method in Python returns the number of ways to select k elements from n elements, with and without repetition. 🧮
Syntax:
math.perm(n, k)
Where:
n: The number of elements from which k elements are selected.
k: The number of elements that are selected.
In the first example, the method returns the number of ways to select 3 elements from 5 elements. The result is 60 ways. 📊
In the second example, the method returns the number of ways to select 5 elements from 10 elements. The result is 252 ways. 🚀
#Python #Math #Coding #Programming #DataScience #Tech
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Machine Learning
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❤6
Cheat sheet for Scikit-learn: 📚 Scikit-learn is a Python library for machine learning.
📥 Loading Data - downloading and preparing data.
🧼 Preprocessing - standardization, normalization, and feature processing.
🏗️ Create Your Model - creating models for classification, regression, and clustering.
🎯 Model Fitting - training the model on data.
🔮 Prediction - obtaining forecasts.
📊 Evaluate Performance - assessing the quality of the model using various metrics.
🔄 Cross-Validation - checking the model on different samples.
⚙️ Tune Your Model - optimizing parameters using Grid Search and Randomized Search.
#ScikitLearn #MachineLearning #Python #DataScience #AI #MLOps
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📥 Loading Data - downloading and preparing data.
🧼 Preprocessing - standardization, normalization, and feature processing.
🏗️ Create Your Model - creating models for classification, regression, and clustering.
🎯 Model Fitting - training the model on data.
🔮 Prediction - obtaining forecasts.
📊 Evaluate Performance - assessing the quality of the model using various metrics.
🔄 Cross-Validation - checking the model on different samples.
⚙️ Tune Your Model - optimizing parameters using Grid Search and Randomized Search.
#ScikitLearn #MachineLearning #Python #DataScience #AI #MLOps
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Towards Data Science
How Far Can Classical NLP Go? From Bag-of-Words to Stacking on Spooky Author Identification | Towards Data Science
An end-to-end classical NLP experiment on Kaggle’s Spooky Author Identification task: from Vowpal Wabbit and TF-IDF/NB-SVM baselines to a tuned stacked ensemble, with a compact representation survey of Bag-of-Words, BM25, Word2Vec, and FastText for context.
🚀 Looking for a portfolio-ready NLP project?
I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset.
You’ll see how far classical NLP can go with:
📝 Bag-of-Words and TF-IDF
🔤 Character n-grams
📊 Model comparison
🧩 Ensemble stacking
It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:
🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/
I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset.
You’ll see how far classical NLP can go with:
📝 Bag-of-Words and TF-IDF
🔤 Character n-grams
📊 Model comparison
🧩 Ensemble stacking
It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:
🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/
❤4👍4🔥1🤩1
🔖 The Legendary MIT Textbook on Mathematics for Computer Science
Mathematics for Computer Science is one of the best free textbooks for developers, ML engineers, and data scientists.
It contains over 1000 pages covering discrete mathematics, logic, graphs, probability, combinatorics, recurrence relations, and other fundamental topics.
⛓️ Link to the textbook:
https://people.csail.mit.edu/meyer/mcs.pdf
#ComputerScience #Mathematics #MachineLearning #DataScience #MIT #OpenSource
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Mathematics for Computer Science is one of the best free textbooks for developers, ML engineers, and data scientists.
It contains over 1000 pages covering discrete mathematics, logic, graphs, probability, combinatorics, recurrence relations, and other fundamental topics.
⛓️ Link to the textbook:
https://people.csail.mit.edu/meyer/mcs.pdf
#ComputerScience #Mathematics #MachineLearning #DataScience #MIT #OpenSource
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❤5
Combining Plots in Matplotlib 📊
In Matplotlib, you can easily combine multiple plots in a single window using the subplot() function. Simply create the necessary plots, specify their layout, add titles, and you'll get a clear visualization for easy data comparison.
#Matplotlib #DataVisualization #Python #DataScience #Coding #Plotting
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In Matplotlib, you can easily combine multiple plots in a single window using the subplot() function. Simply create the necessary plots, specify their layout, add titles, and you'll get a clear visualization for easy data comparison.
#Matplotlib #DataVisualization #Python #DataScience #Coding #Plotting
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