Bayesian Optimization for Machine Learning

Users of Python Fundamentals, 2/e on O'Reilly: The new Lesson 5, Lists and Tuples, is live, and I am sending Lesson 06, Dictionaries and Sets, for processing right now. Should be live before the end of the week. https://lnkd.in/ePMpTP5t #Python Pearson Deitel & Associates

✅ *Must-Know Python Libraries for Data Science 🐍📊* *1️⃣ NumPy (Numerical Python)* ➤ Used for: Fast numerical computation & handling arrays ✔️ Core Features: - N-dimensional arrays (`ndarray`) - Mathematical functions (mean, std, dot, etc.) - Broadcasting for element-wise operations - Works 10x faster than native Python lists 📌 Foundation for almost every other data science library. *2️⃣ Pandas* ➤ Used for: Data cleaning, manipulation, and analysis ✔️ Core Features: - DataFrame & Series objects - Handling missing data - Merging, grouping, filtering, reshaping - Time series analysis 📌 Ideal for working with CSV, Excel, SQL, or JSON datasets. *3️⃣ Matplotlib* ➤ Used for: Basic data visualization ✔️ Core Features: - Line, bar, pie, scatter, histogram charts - Customizable axes, labels, titles - Save plots as images (PNG, PDF, SVG) 📌 Great for quick visual reports or graphs. *4️⃣ Seaborn* ➤ Used for: Advanced & beautiful visualizations ✔️ Core Features: - Heatmaps, pair plots, violin plots - Works seamlessly with Pandas - Built-in themes & color palettes 📌 Easier and prettier than Matplotlib for many plots. *5️⃣ Scikit-learn* ➤ Used for: Machine learning (ML) ✔️ Core Features: - Algorithms: Linear regression, decision trees, SVM, KNN, etc. - Model training, testing & evaluation - Preprocessing: scaling, encoding, splitting - Pipelines for cleaner code 📌 Beginner-friendly for ML tasks. *6️⃣ SciPy* ➤ Used for: Scientific computing ✔️ Core Features: - Linear algebra, integration, interpolation - Signal/image processing - Statistical distributions & optimization 📌 More advanced math than NumPy. *7️⃣ Statsmodels* ➤ Used for: Statistical analysis ✔️ Core Features: - Linear regression with statistical output - ANOVA, t-tests, ARIMA (time series) - Hypothesis testing 📌 Excellent for academic research and econometrics. *8️⃣ TensorFlow / PyTorch* ➤ Used for: Deep learning & neural networks ✔️ Core Features: - Build and train neural networks - GPU acceleration - Support for image, NLP, and tabular data - TensorBoard (in TensorFlow) for visual training insights 📌 TensorFlow is more production-ready; PyTorch is more flexible and beginner-friendly. *9️⃣ Plotly* ➤ Used for: Interactive visualizations ✔️ Core Features: - Zoomable, clickable charts - Dashboards with dropdowns, sliders - Export to HTML or use in Jupyter 📌 Best for presenting insights to non-technical users. *🔟 Jupyter Notebook* ➤ Used for: Writing, running, and documenting code ✔️ Core Features: - Markdown + Python in same notebook - Visual output (charts, tables, images) - Share notebooks easily (.ipynb) - Widely used in data science interviews and portfolios 📌 Your coding notebook + presentation tool. Data Science Resources: https://lnkd.in/g6Kgerxr Learn Python: https://lnkd.in/gsMtMnp8 💬

New article: Sample Paths for Uncertainty Quantification in Time Series Forecasting In this article, we explore the difference between marginal and joint distributions, and how they answer different questions when quantifying uncertainty in time series forecasting. Plus, we get a hands-on experiment with the latest release of #neuralforecast which now supports sample paths across all models. Enjoy the read! #timeseries #forecasting #deeplearning #machinelearning #python #artificialintelligence https://lnkd.in/ekrZPn8S

⭐️ An insightful article on uncertainty quantification in time series forecasting, featuring the latest #neuralforecast 🧠 release. Check it out 👇

🐍📰 In this tutorial, you'll learn all about the k-Nearest Neighbors (kNN) algorithm in Python, including how to implement kNN from scratch, kNN hyperparameter tuning, and improving kNN performance using bagging #python

Recommender Systems using tensorrec #machinelearning #datascience #recommendersystems #tensorrec TensorRec is a Python recommendation system that allows you to quickly develop recommendation algorithms and customize them using TensorFlow. TensorRec lets you to customize your recommendation system's representation functions, prediction function, and loss function while TensorRec handles the data manipulation, scoring, and ranking to generate recommendations. A TensorRec system consumes three pieces of data: user_features, item_features, and interactions. It uses this data to learn to make and rank recommendations. https://lnkd.in/gtZx4Nje

Building with LLMs in Python involves a lot of moving parts. You need to call model APIs, write prompts that produce reliable results, set up retrieval pipelines, and eventually build agents that can reason and use tools. We put together a learning path that walks through all of it, step by step: - Call LLM APIs from OpenAI, Ollama, and OpenRouter - Write effective prompts that return structured output - Build RAG pipelines with LlamaIndex, ChromaDB, and LangChain - Create AI agents using Pydantic AI and LangGraph - Connect agents to external tools and data via MCP It's aimed at Python developers who are comfortable with the language and want to start building real applications on top of language models. https://lnkd.in/ggdqNgNu

Recommender Systems using ikpy #machinelearning #datascience #recommendersystems #ikpy LensKit is a set of Python tools for experimenting with and studying recommender systems. It provides support for training, running, and evaluating recommender algorithms in a flexible fashion suitable for research and education. https://lnkd.in/grqEBXKK

Python is the silent backbone of AI 🐍 Everyone talks about AI models. Few talk about what actually runs them. It’s Python. Behind almost every major AI breakthrough: • Data is processed using Python 📊 • Models are trained using Python libraries 🧠 • APIs are built and deployed using Python 🔗 • Automation pipelines run on Python ⚙️ From research labs to startups, Python is everywhere 🌍 Libraries like: • TensorFlow • PyTorch • Scikit-learn • Pandas …have turned complex AI into something developers can actually build with. Why Python? Because it’s: • Simple to learn • Extremely flexible • Backed by a massive ecosystem 🌐 • Built for fast development 🚀 AI didn’t just grow because of ideas 💡 It scaled because of tools 🛠️ And Python became that tool. That’s why I’m not just learning AI. I’m learning to build with Python 💻 Because in an AI-first world, understanding the backbone matters. If you want to start, here are 5 great Python courses 📚 • Python for Everybody – University of Michigan (Coursera) • CS50’s Introduction to Programming with Python – Harvard University • Complete Python Course – CodeWithHarry • Python for Data Science & AI – IBM

In this week's Python + AI Office Hours, we spent most of the session exploring how to extract content from PDFs using Python. We tried a few approaches: 📄 MarkItDown, PyMuPDF: Two free, open-source Python packages that work well for straightforward documents. We tested both on a complex PDF and they each struggled in different ways. MarkItDown also has an OCR plugin for image descriptions, which we got working with an Azure OpenAI GPT-5.4 model. The entity-extraction repo is a great place to try out those packages: https://lnkd.in/gNWm4DUt 📄 Azure Document Intelligence: This cloud service takes things further by extracting figures, tables, and text separately. In our RAG repo, we also added an LLM description step to generate alt-text-style descriptions for each extracted figure. For the complex PDF, this combination of structured extraction + LLM descriptions gave the best output. In the attached screenshot, you can see a page from the PDF next to its extracted chunk. That code is in the RAG repo, in pdfparser.py and mediadescriber.py : https://lnkd.in/gPd8A8rv Or, just fork the repo and let the ingestion pipeline do its thing. Remember: Always set up evaluations for your data! Document extraction quality can vary widely depending on the structure and content of your documents. See the recording and questions here: https://lnkd.in/guKt8P2E