Python Libraries for Data Science & AI

Python libraries used in data science, machine learning, and AI Learn Python and AI → https://lnkd.in/d8-NH2BY Recommended courses → Python for Everybody https://lnkd.in/dw3T2MpH → IBM Data Science Professional Certificate https://lnkd.in/dwkPTFGV → Machine Learning by Andrew Ng https://lnkd.in/dmPtiWK8 Data manipulation → Pandas → Polars → Modin → NumPy → Vaex → Datatable Data visualization → Matplotlib → Seaborn → Plotly → Altair → Bokeh → Folium → Plotnine → Dash → Streamlit Statistical and probabilistic modeling → SciPy → Statsmodels → PyMC → PyStan → Lifelines → Pingouin Machine learning → Scikit-learn → XGBoost → TensorFlow → PyTorch → Keras → JAX Natural language processing → NLTK → spaCy → Gensim → Transformers → TextBlob → Polyglot Distributed data processing → PySpark → Dask → Ray → Kafka → Hadoop Time series analysis → Prophet → sktime → Darts → Kats → tsfresh → AutoTS Web scraping and automation → Requests → httpx → BeautifulSoup → lxml → Scrapy → Selenium LLMs and AI applications → Transformers → Hugging Face Hub → OpenAI API → LangChain → LlamaIndex → vLLM → Text Generation Inference → Sentence Transformers These libraries form the core Python ecosystem used in modern AI and data science projects. #Python #DataScience #MachineLearning #AI #ProgrammingValley

Python libraries used in data science, machine learning, and AI Learn Python and AI → https://lnkd.in/d8-NH2BY Recommended courses → Python for Everybody https://lnkd.in/dw3T2MpH → IBM Data Science Professional Certificate https://lnkd.in/dwkPTFGV → Machine Learning by Andrew Ng https://lnkd.in/dmPtiWK8 Data manipulation → Pandas → Polars → Modin → NumPy → Vaex → Datatable Data visualization → Matplotlib → Seaborn → Plotly → Altair → Bokeh → Folium → Plotnine → Dash → Streamlit Statistical and probabilistic modeling → SciPy → Statsmodels → PyMC → PyStan → Lifelines → Pingouin Machine learning → Scikit-learn → XGBoost → TensorFlow → PyTorch → Keras → JAX Natural language processing → NLTK → spaCy → Gensim → Transformers → TextBlob → Polyglot Distributed data processing → PySpark → Dask → Ray → Kafka → Hadoop Time series analysis → Prophet → sktime → Darts → Kats → tsfresh → AutoTS Web scraping and automation → Requests → httpx → BeautifulSoup → lxml → Scrapy → Selenium LLMs and AI applications → Transformers → Hugging Face Hub → OpenAI API → LangChain → LlamaIndex → vLLM → Text Generation Inference → Sentence Transformers These libraries form the core Python ecosystem used in modern AI and data science projects. hashtag #Python #DataScience #MachineLearning #AI #ProgrammingValley

💡 Must-Know Python Libraries for Data Science If you're stepping into Data Science, these are the essential libraries you can’t ignore 👇 🔹 NumPy The backbone of numerical computing in Python. It provides fast operations on arrays and matrices, making it essential for handling large-scale data efficiently. 🔹 Pandas Your go-to library for data manipulation and analysis. It makes cleaning, transforming, and exploring structured data simple and intuitive. 🔹 Matplotlib A powerful visualization library used to create basic plots like line, bar, and scatter charts. Great for understanding trends and patterns in data. 🔹 Seaborn Built on top of Matplotlib, it helps create more advanced and visually appealing statistical plots with minimal code. 🔹 Scikit-learn A complete toolkit for machine learning. It offers easy-to-use models for regression, classification, and clustering. 🔹 TensorFlow A robust deep learning framework widely used in production. Ideal for building scalable and high-performance ML models. 🔹 PyTorch Known for its flexibility and simplicity, PyTorch is popular in research and widely used for building deep learning models. 🔹 NLTK A leading library for Natural Language Processing. It helps in working with text data, including tokenization, sentiment analysis, and more. These tools are not just libraries — they are the foundation of real-world data science projects. 💬 Which library do you use the most? Or which one are you planning to learn next? 🔖 Save this post for your Data Science journey 🚀 #DataScience #Python #MachineLearning #DeepLearning #DataAnalytics #DataScientist #NumPy #Pandas #ScikitLearn #TensorFlow #PyTorch #Seaborn #Matplotlib #NLTK

🚀 Which Python Library Should You Use for Data Projects? 🤔 When starting your journey in data science or analytics, one of the biggest challenges is not learning Python… but choosing the right library at the right time. With so many powerful tools available, it’s easy to feel confused. But the truth is — each library has its own purpose, and mastering when to use them is what separates beginners from professionals. Let’s break it down 👇 🔹 NumPy – The foundation of data science Perfect for working with arrays, matrices, and fast numerical computations. If you're doing mathematical operations or linear algebra, this is your go-to library. 🔹 Pandas – Data manipulation made easy From reading CSV/Excel files to cleaning and transforming data, Pandas is the backbone of most data workflows. 🔹 Matplotlib – Basic data visualization Helps you create customizable plots and understand your data visually. Ideal for quick analysis. 🔹 Seaborn – Advanced statistical visualization Built on top of Matplotlib, it makes your graphs more attractive and insightful (heatmaps, distributions, etc.). 🔹 SciPy – Scientific computing Used for optimization, statistics, and more advanced mathematical operations. 🔹 Polars – Faster alternative to Pandas Handles large datasets efficiently with better performance and parallel processing. 🔹 Dask – Big data processing When your dataset is too large for memory, Dask helps you scale your Pandas workflows. 🔹 Scikit-learn – Machine Learning made simple Great for regression, classification, clustering, and model evaluation. 🔹 XGBoost / LightGBM – High-performance ML models Perfect for competitions and real-world problems where accuracy matters most. 🔹 TensorFlow / PyTorch – Deep Learning frameworks Used for building neural networks, working with images, NLP, and advanced AI systems. 💡 Pro Tip: Don’t try to learn everything at once. Start with: 👉 NumPy + Pandas + Matplotlib Then move to: 👉 Scikit-learn → XGBoost Finally explore: 👉 TensorFlow / PyTorch 🔥 Final Thought: Tools don’t make you a great data scientist — knowing when and why to use them does. Keep learning, keep building, and most importantly — apply your knowledge to real-world problems. 💬 Which Python library do you use the most in your projects? Let’s discuss in the comments! #Python #DataScience #MachineLearning #AI #DataAnalytics #Programming #100DaysOfCode #LearningJourney #TechCareer

Machine Learning Graph Data using stellargraph #machinelearning #datascience #graphdata #stellargraph StellarGraph is a Python library for machine learning on graphs and networks. The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It can solve many machine learning tasks : Representation learning for nodes and edges, to be used for visualisation and various downstream machine learning tasks ; Classification and attribute inference of nodes or edges ; Classification of whole graphs ; Link prediction ; Interpretation of node classification. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. For example, a graph can contain people as nodes and friendships between them as links, with data like a person’s age and the date a friendship was established. StellarGraph supports analysis of many kinds of graphs : homogeneous (with nodes and links of one type), heterogeneous (with more than one type of nodes and/or links) knowledge graphs (extreme heterogeneous graphs with thousands of types of edges) graphs with or without data associated with nodes graphs with edge weights StellarGraph is built on TensorFlow 2 and its Keras high-level API, as well as Pandas and NumPy. It is thus user-friendly, modular and extensible. It interoperates smoothly with code that builds on these, such as the standard Keras layers and scikit-learn, so it is easy to augment the core graph machine learning algorithms provided by StellarGraph. https://lnkd.in/gh9FxmaP

Python for Everything 🐍 | One Language, Endless Possibilities Python is not just a programming language — it’s an entire ecosystem powering almost every domain in tech today. 🚀 From data to AI, from web scraping to large-scale systems, Python has a library for everything: 📊 Data Manipulation – Pandas, NumPy, Polars 📈 Data Visualization – Matplotlib, Seaborn, Plotly 📉 Statistical Analysis – SciPy, Statsmodels, PyMC3 🤖 Machine Learning – Scikit-learn, TensorFlow, PyTorch 🧠 Generative AI – OpenAI SDK, Hugging Face, LangChain 💬 NLP – NLTK, spaCy, Gensim 🗄️ Big Data & Databases – PySpark, Dask, Hadoop ⏳ Time Series – Prophet, Darts, Kats 🌐 Web Scraping – BeautifulSoup, Scrapy, Selenium 💡 The real power of Python lies in its versatility + community support. No matter your domain, Python gives you the tools to build, scale, and innovate. 🔥 If you're starting your tech journey or leveling up—Python is your best investment. #Python #Programming #DataScience #MachineLearning #ArtificialIntelligence #GenerativeAI #DeepLearning #NLP #BigData #WebScraping #Developer #Tech #Coding #LearnPython #CareerGrowth yogesh.sonkar.in@gmail.com

Such a good read! As someone who has been deep into the world of creating predictive classification models for diagnosis via Machine Learning (yes in Python), I agree with everything in this article. https://lnkd.in/dK4UVEpw

Essential Python Libraries for Data Science 📊 Here are some key Python libraries used in Data Science: • Scrapy – Data Collection & Web Scraping • pandas – Data Manipulation, Preprocessing & EDA • Matplotlib – Data Visualization • Statsmodels—Statistical & Time Series Analysis • scikit-learn – Machine Learning • TensorFlow – Deep Learning • spaCy – Natural Language Processing • Flask – Model Deployment • PySpark – Big Data & Distributed Computing • Apache Airflow – Automation & Workflow Orchestration #DataScience #Python #MachineLearning #AI #DataAnalytics #Programming #Technology #Upskilling

💻 Python Libraries Every Data Scientist and Data Analyst Must Know If you're starting in Data Science or Data Analytics, these libraries are non-negotiable: ✔ NumPy – Numerical computing ✔ Pandas – Data manipulation ✔ Matplotlib & Seaborn – Data visualization ✔ Scikit-learn – Machine learning ✔ TensorFlow & PyTorch – Deep learning(Not Mandatory for Analysts,but good later) ✔ Plotly, Statsmodels, XGBoost – Advanced analytics(Optional but Valuable) 📌 Master these tools and you’re already ahead of most beginners. Data is powerful, but the right tools make it impactful. #Python #DataScience #DataAnalytics #MachineLearning #DeepLearning #AI #Pandas #NumPy

🚨BREAKING: New Python library for PDF parsing: One of the most annoying problems in building RAG systems: Your documents are PDFs. PDFs are chaos. Tables that don't parse. Charts that become gibberish. Pages that lose their structure entirely. Most tutorials skip this problem completely. They hand you a clean CSV and pretend that's how data arrives in the real world. It's not. In the real world your data is locked inside: Annual reports Client contracts Research papers Internal documentation Scanned invoices That's where Doctra comes in. Parse PDFs to clean text, tables, and charts. Export to CSV, Excel, or Markdown. Drop it directly into your RAG pipeline. This is the unglamorous part of building production AI systems that nobody posts about. But it's exactly what separates a demo that works on toy data from a system a business actually runs. Track 2 data scientists know this. They're not just building the AI layer. They're solving the full pipeline — from messy source documents to decision-ready output. That's the skill companies are paying $150K–$200K for. Not the model. The end-to-end system. 🔗 https://lnkd.in/eMqxWvhg 🚨 What’s the next step (if you want to build an AI/DS portfolio for real)? I have a live workshop that will help (free). Inside my live workshop, you'll get: • My Generative AI + Data Science Process • Applied to a Business Problem (not "toy" data) • Actual Python code + AI Agents 👉Registration Link (500 seats): Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below