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#Pca For Data Preprocessing Reel by @codevisium - PCA reduces high-dimensional data into a smaller set of features while preserving the most important information. It works by finding eigenvectors of — PCA reduces high-dimensional data into a smaller set of features while preserving the most important information. It works by finding eigenvectors of the covariance matrix that capture maximum variance in the data. #machinelearning #datascience #ai #pca #python

#Pca For Data Preprocessing Reel by @aibutsimple - Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a dataset into a new coordinate system where the axes (prin — Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a dataset into a new coordinate system where the axes (principal components) capture the most variance (which has the most amount of detail/information). The computation behind PCA involves calculating the covariance matrix of the data, followed by an eigenvalue decomposition. The eigenvalues represent the amount of variance captured by each principal component, while the corresponding eigenvectors define the directions of these components. Sorting the eigenvalues in descending order allows for selecting the most significant components, reducing dimensionality while keeping the most critical information. C: deepia Join our AI community for more posts like this @aibutsimple 🤖 #deeplearning #machinelearning #datascience #python #programming #dataanalytics #coding #datascientist #data #neuralnetworks #computerscience #computervision #ml #robotics

#Pca For Data Preprocessing Reel by @insightforge.ai - Principal Component Analysis (PCA) is a dimensionality reduction method that reprojects data into a new coordinate system, where each axis - called a — Principal Component Analysis (PCA) is a dimensionality reduction method that reprojects data into a new coordinate system, where each axis - called a principal component - captures the maximum possible variance, preserving the most important information in the dataset. To compute PCA, we first calculate the covariance matrix of the data, which measures how features vary together. Then, we perform an eigenvalue decomposition on this matrix. Each eigenvalue indicates how much variance a particular principal component explains, while the corresponding eigenvector defines the direction of that component in the new space. By sorting the eigenvalues in descending order and keeping only the top components, we can reduce the dataset’s dimensionality while retaining the majority of its meaningful variance and structure. C: Deepia #machinelearning #deeplearning #datascience #AI #dataanalytics #computerscience #python #programming #data #datascientist #neuralnetworks #computervision #statistics #robotics #ML

#Pca For Data Preprocessing Reel by @getintoai (verified account) - Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a dataset into a new coordinate system where the axes (prin — Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a dataset into a new coordinate system where the axes (principal components) capture the most variance (which has the most amount of detail/information). The computation behind PCA involves calculating the covariance matrix of the data, followed by an eigenvalue decomposition. The eigenvalues represent the amount of variance captured by each principal component, while the corresponding eigenvectors define the directions of these components. Sorting the eigenvalues in descending order allows for selecting the most significant components, reducing dimensionality while keeping the most critical information. C: deepia #deeplearning #machinelearning #datascience #python #programming #dataanalytics #coding #datascientist #data #neuralnetworks #computerscience #computervision #ml

#Pca For Data Preprocessing Reel by @datascience.swat - Principal Component Analysis, or PCA, is a method used to simplify complex data by transforming it into a smaller number of new variables called princ — Principal Component Analysis, or PCA, is a method used to simplify complex data by transforming it into a smaller number of new variables called principal components. These components are arranged in a way that they are independent from each other and capture the most meaningful structure in the data. The idea is to retain the directions that hold the most variation while removing less important details. By focusing only on the top components, large and complicated datasets can be reduced into a simpler form without losing the key patterns that matter most. Follow @datascience.swat for more daily videos like this Credits; Deepia Shared under fair use for commentary and inspiration. No copyright infringement intended. If you are the copyright holder and would prefer this removed, please DM me. I will take it down respectfully. ©️ All rights remain with the original creator (s)

#Pca For Data Preprocessing Reel by @infusewithai - Principal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance a — Principal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance as possible in a dataset. It transforms the original correlated variables into a new set of uncorrelated variables called principal components. The process begins by centering the data (which is subtracting the mean), then computing the covariance matrix to capture the relationships between variables. Eigenvectors and their corresponding eigenvalues are then calculated from the covariance matrix. The eigenvectors represent the directions/principal components of the highest variance, while the eigenvalues quantify the amount of variance in each direction. By selecting the top “k” eigenvectors with the highest eigenvalues, PCA projects the data into a lower dimensional space, simplifying analysis and visualization while retaining the most important information. C: deepia

#Pca For Data Preprocessing Reel by @shailjamishra__ - COMMENT " DATA and I will SEND the entire Data Science PreparationGuide along with "Topics to prepare " in your DM

#microsoft #datascientist #projec — COMMENT “ DATA and I will SEND the entire Data Science PreparationGuide along with “Topics to prepare “ in your DM #microsoft #datascientist #projects #preparationmaterials #guides

#Pca For Data Preprocessing Reel by @chhavi_maheshwari_ - Handling 1 Million RPS isn't about code - it's about smart architecture.

1️⃣ Traffic Distribution (Load Balancers)
➡️ Spreads incoming requests acros — Handling 1 Million RPS isn’t about code — it’s about smart architecture. 1️⃣ Traffic Distribution (Load Balancers) ➡️ Spreads incoming requests across many servers so nothing overloads. Example: 1M requests split across 200 servers = ~5K requests per server. ⸻ 2️⃣ Scale Out, Not Up (Horizontal Scaling) ➡️ Add more machines instead of making one server bigger. Example: Flash sale traffic? Instantly launch 50 new API instances. ⸻ 3️⃣ Fast Reads with Cache ➡️ Use Redis/Memcached to avoid hitting the database every time. Example: Cached user data = millions of DB calls saved daily. ⸻ 4️⃣ Edge Delivery with CDN ➡️ Static content loads from servers closest to the user. Example: Users in Delhi fetch images from a Delhi CDN node. ⸻ 5️⃣ Background Work with Queues ➡️ Heavy tasks run asynchronously so APIs respond instantly. Example: Payment succeeds now, email receipt sent in background. ⸻ 6️⃣ Split the Database (Sharding) ➡️ Divide data across multiple databases to handle scale. Example: Usernames A–M on one shard, N–Z on another. ⸻ 7️⃣ Rate Limiting ➡️ Prevent abuse and traffic spikes from taking the system down. Example: Limit clients to 100 requests/sec to block bots from killing the API. ⸻ 8️⃣ Lightweights Payloads ➡️ Smaller payloads = faster responses + less bandwidth. Example: Send only required fields instead of massive JSON blobs. Please follow for more such videos🙏 #systemdesign #softwaredevelopers #programming #tech #interview [API Design] [System Architecture] [API Scaling] [1 Million RPS] [Distributed Systems] [Load Balancing] [Database Sharding] [High Availability]

#Pca For Data Preprocessing Reel by @khushigrewall (verified account) - More information doesn't always mean better understanding.
Sometimes, you need to simplify.

In machine learning, PCA reduces dimensions by keeping wh — More information doesn’t always mean better understanding. Sometimes, you need to simplify. In machine learning, PCA reduces dimensions by keeping what matters most. Generalization is about learning patterns that work on new data. Different goals. Different concepts. Same aim: better models. If you’re learning ML or AI, save this. #machinelearning #datascience #artificialintelligence #PCA #principalcomponentanalysis generalization mlconcepts aieducation aireels techreels datasciencereels learnml buildinpublic techcreators reelsindia indiantech futureofai viralreels explorepage

#Pca For Data Preprocessing Reel by @omnifab.ph - Voltera V-One: Precision PCB prototyping & assembly. Streamline your design-to-prototype workflow

#pcbdevelopment #volteracvone #desktoppcbprinter #o — Voltera V-One: Precision PCB prototyping & assembly. Streamline your design-to-prototype workflow #pcbdevelopment #volteracvone #desktoppcbprinter #omnifabph #voltera

#Pca For Data Preprocessing Reel by @mathswithmuza - Principal Component Analysis is a dimensionality-reduction technique that transforms a high-dimensional dataset into a smaller set of new variables ca — Principal Component Analysis is a dimensionality-reduction technique that transforms a high-dimensional dataset into a smaller set of new variables called principal components. These components are constructed to capture as much of the original variation in the data as possible while remaining uncorrelated with one another. PCA works by identifying directions in the data where the spread is largest, meaning those directions explain the most meaningful structure. Instead of trying to interpret dozens of correlated features, PCA allows you to rotate the coordinate system and focus on just a few axes that summarize most of the information. Once the principal components are found, the data can be projected onto them, making it easier to visualize patterns, clusters, and trends that may be hidden in the original space. This is especially useful in fields like image processing, genetics, or marketing analytics where datasets can have hundreds or thousands of variables. PCA also helps reduce noise by filtering out directions with very little variance, which often correspond to measurement error rather than true structure. Overall, PCA simplifies complex datasets without losing their essential relationships, helping analysts uncover clearer insights and build more efficient models. Like this video and follow @mathswithmuza for more! #math #maths #mathematics #learn #learning #study #studying #coding #ai #chatgpt #foryou #fyp #reels #education #stem #physics #statistics #new #animation #manim #school #university #highschool #college

#Pca For Data Preprocessing Reel by @the.datascience.gal (verified account) - 5 Algorithms you must know as a data scientist 👩‍💻 🧑‍💻
1. Dimensionality Reduction
- PCA, t-SNE, LDA

2. Regression models
- Linesr regression, Ke — 5 Algorithms you must know as a data scientist 👩‍💻 🧑‍💻 1. Dimensionality Reduction - PCA, t-SNE, LDA 2. Regression models - Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression 3. Classification models - Binary classification- Logistic regression, SVM - Multiclass classification- One versus one, one versus many - Multilabel classification 4. Clustering models - K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models 5. Decision tree based models - CART model, ensemble models(XGBoost, LightGBM, CatBoost) #ai #datascientist #interviews

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