PageIndex Revolutionizes RAG with 98.7% Accuracy on FinanceBench

𝗡𝘂𝗺𝗣𝘆 𝗔𝗿𝗿𝗮𝘆𝘀 𝗩𝘀 𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝘀𝘁𝘀 You use NumPy arrays often. You might wonder why you need them. Python lists hold numbers. Python lists support indexing. Speed is the main reason. Testing 5 million numbers shows a huge gap. A Python list takes 0.83 seconds. A NumPy array takes 0.0089 seconds. NumPy is 94 times faster. This gap grows with more data. Memory is the secret. Python lists store references to objects. These objects are scattered. To multiply a list, Python visits each object one by one. NumPy arrays store raw numbers in one block. All elements have the same type. NumPy uses C code to process these in parallel. Packing wins. Fixed types provide speed. - int8 uses 1 byte per number. - int64 uses 8 bytes per number. Using int8 saves 8 times more memory. This helps you fit large datasets into RAM. Deep learning models use float32 to save GPU memory. Useful NumPy tools: - linspace: Creates evenly spaced numbers. - Fancy indexing: Picks specific rows without loops. - Boolean masking: Filters data in one line. - Broadcasting: Adds arrays of different shapes. Essential functions: - sum, mean, and std: Fast statistics. - argsort: Finds the rank of items. - vstack and hstack: Combines data matrices. Now you know NumPy. Next is Pandas. Pandas handles labels and messy real world data. Source: https://lnkd.in/gVMVwUyC Optional learning community: https://t.me/GyaanSetuAi

AI Beyond the Hype | Part 8: Vector Databases “What is Python used for?” “Is python dangerous?” Same word. Completely different meaning. 👉 In one case → Python = programming language 🧑💻 👉 In another → python = reptile 🐍 We can’t store every possible variation or phrasing. Traditional search fails here because it works on exact match, not meaning. This is where semantic search (search based on meaning) comes in — and that’s where vector databases play a key role. ## 🧠 What is a Vector Database? A vector DB stores data as embeddings (numbers) instead of plain text, so it can search based on meaning. ## 🔢 How data is generated and stored Text → tokens → embeddings Example: “Python is used for backend development” → [0.12, -0.45, 0.78, …] “Python is a dangerous reptile” → [-0.33, 0.91, -0.12, …] These numbers capture meaning, not just words. ## 🔍 How search happens User query → embedding Example: “Python coding” → vector “Is python poisonous” → vector Then system finds vectors that are closest in meaning (not exact match). This is semantic search. ## ⚡ How search is optimized Searching millions of vectors directly is slow. So vector DBs use indexing (ANN – Approximate Nearest Neighbors) and sometimes hashing/partitioning to find nearest vectors quickly. ## 🧩 How prompt-based retrieval works 1. Query → embedding 2. Retrieve relevant chunks 3. Add to prompt 4. LLM generates answer → This is how RAG works internally. ## 🚨 Reality check Vector DB doesn’t understand meaning. It just finds patterns that are mathematically close. ## ⚠️ Challenges Similar ≠ correct Bad embeddings → bad retrieval Needs tuning (top-k, thresholds) Scaling & latency trade-offs ## 💡 Takeaway 👉 “Vector DB doesn’t search words — it searches meaning.” Funny how things work — what felt pointless in school is now the backbone of AI systems

When datasets become very large, typical Python ML libraries struggle because they operate on a single machine. Spark ML solves this by providing a distributed machine learning API. It replaced most of the older MLlib functionality, which was built on the RDD-based API. What is a SparkML Pipeline? Conceptually, the idea is similar to scikit-learn's Pipeline object. The key difference is that Spark ML runs in a distributed manner across a cluster, while scikit-learn is designed primarily for single-machine execution. A Pipeline in Spark ML chains together a sequence of stages, where each stage is either a Transformer or an Estimator, creating a single reproducible workflow. This is how we take raw distributed data and produce trained model predictions without ever leaving Spark's execution engine. The classic pattern for tabular data looks like this: StringIndexer converts categorical string columns into numeric indices. OneHotEncoder then converts those indices into sparse binary vectors. VectorAssembler combines all our features: encoded categoricals and raw numericals into a single feature vector column. Finally, our estimator (say, LogisticRegression or RandomForestClassifier) trains on that vector. Why Pipeline over doing it step by step? Pipelines prevent data leakage by fitting transformers only on training data and applying them consistently to test/validation splits. They also make the entire workflow serializable. We can save and reload a PipelineModel for batch or streaming inference later. For large-scale transformations, we typically use Spark ML's native transformers, not pandas' get_dummies or scikit-learn's ColumnTransformer. Those don't distribute. SparkML does.  #DatabricksMLJourney #Day2

Sometimes you want to practice a method or create a teaching example, but it is difficult to find a dataset that truly fits your needs. Real data is often messy, restricted, or simply not aligned with what you want to demonstrate. That’s where drawing your own data becomes very useful. Instead of searching for the "perfect" dataset, you can create one that matches your exact requirements. A great tool for this is the drawdata library in Python. It allows you to visually sketch data points and convert them into structured datasets within seconds. The image below illustrates a typical workflow: You generate data in Python using drawdata and then apply a method to it, for example k-means clustering. What makes this even more interesting is the environment used here. The Positron IDE is a modern IDE by Posit, the company behind RStudio, and is designed for multi-language workflows. You can work with Python and R in the same environment, side by side. In this example, the data is created in Python and then directly analyzed in R without switching tools. This kind of setup can make your workflow more efficient, especially if you regularly move between languages. I’ve just published a new module in the Statistics Globe Hub on how to draw synthetic datasets using the drawdata Python library and analyze them afterward in R using k-means clustering. It includes a full video walkthrough, practical examples, and detailed exercises. Not part of the Statistics Globe Hub yet? The Hub is a continuous learning program with new modules released every week on topics such as statistics, data science, AI, R, and Python. More information about the Statistics Globe Hub: https://lnkd.in/e5YB7k4d #datascience #python #rstats #machinelearning #kmeans #statisticsglobehub

Sometimes you want to practice a method or create a teaching example, but it is difficult to find a dataset that truly fits your needs. Real data is often messy, restricted, or simply not aligned with what you want to demonstrate. That’s where drawing your own data becomes very useful. Instead of searching for the "perfect" dataset, you can create one that matches your exact requirements. A great tool for this is the drawdata library in Python. It allows you to visually sketch data points and convert them into structured datasets within seconds. The image below illustrates a typical workflow: You generate data in Python using drawdata and then apply a method to it, for example k-means clustering. What makes this even more interesting is the environment used here. The Positron IDE is a modern IDE by Posit, the company behind RStudio, and is designed for multi-language workflows. You can work with Python and R in the same environment, side by side. In this example, the data is created in Python and then directly analyzed in R without switching tools. This kind of setup can make your workflow more efficient, especially if you regularly move between languages. I’ve just published a new module in the Statistics Globe Hub on how to draw synthetic datasets using the drawdata Python library and analyze them afterward in R using k-means clustering. It includes a full video walkthrough, practical examples, and detailed exercises. Not part of the Statistics Globe Hub yet? The Hub is a continuous learning program with new modules released every week on topics such as statistics, data science, AI, R, and Python. More information about the Statistics Globe Hub: https://lnkd.in/exBRgHh2 #datascience #python #rstats #machinelearning #kmeans #statisticsglobehub

Shipped: Python SDK for tag-graph agent memory. For a year I've been chasing one problem — how do you give an LLM agent memory that's bounded, predictable, and doesn't blow your token bill? Vector DBs → fuzzy, impossible to budget. Raw history → 5-turn context overflow. Summarize-and-re-inject → silently drops facts the agent needs three turns later. So we built MME — a bounded tag-graph memory engine. Every memory carries tags, retrieval starts from the current scope, propagates to neighbors with bounded fanout, ranks by graph proximity. Deterministic, token-budgeted, sub-50ms at 100k items. Today the Python SDK is live: → pip install railtech-mme → Native LangChain + LangGraph tool integrations → Online learning via feedback loops → Open source Wrote up the full design rationale, tradeoffs vs. vector search, and the SDK surface area here: https://lnkd.in/eNR5n_iq Honest beat — this is launch day. If you're building LLM agents in Python and "my agent doesn't remember things well" feels familiar, I'd love to hear what's clunky about the API. #AI #Python #LangChain #LLM #AgentMemory #BuildInPublic #OpenSource

Hyperparameter Optimization Machine Learning using smac #machinelearning #datascience #hyperparameteroptimization #smac SMAC is a tool for algorithm configuration to optimize the parameters of arbitrary algorithms, including hyperparameter optimization of Machine Learning algorithms. The main core consists of Bayesian Optimization in combination with an aggressive racing mechanism to efficiently decide which of two configurations performs better. SMAC3 is written in Python3 and continuously tested with Python 3.8, 3.9, and 3.10. Its Random Forest is written in C++. In the following, SMAC is representatively mentioned for SMAC3. https://lnkd.in/gMXPgSrP

Workflow Experiment Tracking using pycaret #machinelearning #datascience #workflowexperimenttracking #pycaret PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that exponentially speeds up the experiment cycle and makes you more productive. Compared with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with a few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks, such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and a few more. The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Features PyCaret is an open-source, low-code machine learning library in Python that aims to reduce the hypothesis to insight cycle time in an ML experiment. It enables data scientists to perform end-to-end experiments quickly and efficiently. In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to perform complex machine learning tasks with only a few lines of code. PyCaret is simple and easy to use. PyCaret for Citizen Data Scientists The design and simplicity of PyCaret is inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are ‘power users’ who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise. Seasoned data scientists are often difficult to find and expensive to hire but citizen data scientists can be an effective way to mitigate this gap and address data science challenges in the business setting. PyCaret deployment capabilities PyCaret is a deployment ready library in Python which means all the steps performed in an ML experiment can be reproduced using a pipeline that is reproducible and guaranteed for production. A pipeline can be saved in a binary file format that is transferable across environments. PyCaret and its Machine Learning capabilities are seamlessly integrated with environments supporting Python such as Microsoft Power BI, Tableau, Alteryx, and KNIME to name a few. This gives immense power to users of these BI platforms who can now integrate PyCaret into their existing workflows and add a layer of Machine Learning with ease. Ideal for : Experienced Data Scientists who want to increase productivity. Citizen Data Scientists who prefer a low code machine learning solution. Data Science Professionals who want to build rapid prototypes. Data Science and Machine Learning students and enthusiasts. https://lnkd.in/g2b_5wTd

Standard classification models tell you if a customer will leave, but Survival Analysis tells you <<when>>. I just published a new deep dive into Survival Analysis using Python and the lifelines library. Using telco churn data, I explore: ✅ The Kaplan-Meier Estimator: Visualizing the "survival" journey of a subscriber. ✅ Cox Proportional Hazards: Identifying exactly which behaviors (like high charges or complaints) accelerate the risk of churn. ✅ Censoring: How to handle customers who haven't churned yet without biasing your data. Treating churn like a timeline. Check out the full article and breakdown at Towards Data Science: https://lnkd.in/evH9Fk2R #DataScience #MachineLearning #SurvivalAnalysis #Python #ChurnPrediction #Analytics

🔍 Exploratory Data Analysis (EDA) with Python Before building any model, you need to understand your data. That's exactly what EDA is about. EDA is the process of investigating datasets to discover patterns, spot anomalies, test hypotheses, and check assumptions — using visual and statistical methods. Here's how I approach it with Python: 1. Load & Inspect the Data python import pandas as pd df = pd.read_csv("data.csv") df.head() df.info() df.describe() → Understand shape, dtypes, null values, and basic statistics right away. 2. Handle Missing Values python df.isnull().sum() df.fillna(df.median(), inplace=True) → Never ignore nulls — they skew your results silently. 3. Univariate Analysis python import seaborn as sns sns.histplot(df['age'], kde=True) → Understand the distribution of each feature individually. 4. Bivariate & Multivariate Analysis python sns.heatmap(df.corr(), annot=True, cmap='coolwarm') sns.pairplot(df, hue='target') → Find correlations and relationships between features. 5. Detect Outliers python sns.boxplot(x=df['salary']) → Outliers can destroy model performance if ignored. 6. Feature Distribution by Class python sns.violinplot(x='target', y='feature', data=df) → See how features behave across different classes. 💡 EDA is not optional — it's the foundation of every reliable ML pipeline. The better you understand your data, the better your model will be. What's your go-to EDA library? Drop it in the comments 👇 #DataScience #Python #EDA #MachineLearning #Pandas #Seaborn #Analytics #DataAnalysis #AI