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Free Download Udemy - Mastering Polars - Fast Data Processing & Big Data Analysis Published: 3/2025 Created by: Olga Piliaieva MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Intermediate | Genre: eLearning | Language: English | Duration: 10 Lectures ( 1h 15m ) | Size: 552 MB Master Polars for Fast Data Manipulation: Work with Large Datasets, Lazy Execution, Performance Optimization, and More What you'll learn Master Data Manipulation - Learn to filter, group, and transform data efficiently using Polars' powerful functions. Optimize Performance - Use lazy evaluation and parallel execution to handle large datasets faster. Compare Polars and Pandas - Understand key differences to choose the best tool for your data tasks. Process Large Files in Chunks - Load, process, and aggregate large datasets efficiently without running out of memory. Requirements A computer with Python installed. Jupyter Notebook for running and testing code. Polars and Pandas libraries must be installed. Basic familiarity with Python is helpful but not required - beginners are welcome! Description Ready to speed up your data analysis? In this course, you'll explore Polars, a fast and powerful library designed to efficiently handle large datasets. If you've faced performance challenges with Pandas or want to speed up your work with big data, this course is perfect for you. I'll walk you through everything - from the fundamentals of loading and manipulating data to advanced techniques like lazy evaluation and chunk processing. You'll also learn how to compare Polars with Pandas in real-world scenarios, and discover why Polars could be the game-changing tool you've been waiting for. By the end of this course, you'll be able to process millions of rows in a fraction of the time it takes with traditional methods. Join me and learn how to optimize your workflow and elevate your data analysis skills.The files used in this course are attached in the resources section of Lecture 2You will learn:Introduction to Polars: Why It's Faster and How It Differs from PandasPolars Installation, DataFrame Loading, and Efficient Column AccessData Manipulation in Polars: Arithmetic Operations, Column Management, FilteringMastering Polars DataFrames: Slicing, Stats, and Data ExplorationPolars DataFrame Methods: Flags, Schema, Column Operations, and ConversionAdvanced Data Manipulation: Grouping, Aggregation, Sorting, and TransformationAdvanced Polars Operations: write_csv, Pivot Tables, and Join StrategiesEager vs Lazy Execution in Polars: Speed Comparison with PandasData Visualization in Polars. Benefits, Limitations, and ComparisonThis course will equip you with the skills to efficiently process and analyze large-scale datasets, leveraging Polars' powerful features for data manipulation and performance optimization.In this course, you'll master the key techniques and tools in Polars to efficiently handle large datasets and optimize your data workflows. You will gain:Understand the basics of Polars and its features.Efficient Data Loading: Learn how to load large datasets into Polars efficiently.Data Manipulation: Apply various data transformation techniques, including filtering, sorting, and aggregation.Lazy Evaluation: Understand how lazy evaluation in Polars speeds up computations and reduces memory usage.Chunk Processing: Learn how to process large datasets in chunks for better memory management.Comparison with Pandas: Compare the performance and features of Polars with the traditional Pandas library.Measuring Memory Usage: Explore techniques for measuring memory consumption and performance when working with big data.Performance Optimization: Master techniques for maximizing performance when working with huge datasets. Who this course is for Beginner Python developer curious about data science Anyone Looking for Faster Pandas Alternatives Engineers & Business Analysts - If you work with data in any field, Polars can help streamline your workflows Students & Researchers - Whether you're working on academic projects or research, you'll benefit from learning faster data processing techniques Homepage: https://www.udemy.com/course/mastering-polars-fast-data-processing-big-data-analysis/ [b]AusFile[/b] https://ausfile.com/byvx2t22k5tw/gasqd.Mastering.Polars.Fast.Data.Processing..Big.Data.Analysis.rar.html Rapidgator https://rg.to/file/b7b22d1d63c783f99c02a2cab3fb70cf/gasqd.Mastering.Polars.Fast.Data.Processing..Big.Data.Analysis.rar.html Fikper Free Download https://fikper.com/ccHkPsyhpz/gasqd.Mastering.Polars.Fast.Data.Processing..Big.Data.Analysis.rar.html No Password - Links are Interchangeable

Free Download Machine Learning With Polars Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.08 GB | Duration: 2h 24m Master the Essentials of Modern Machine Learning What you'll learn Explore the fundamentals of an end-to-end machine learning application. Carry out basic data cleaning and pre-processing in Python with Polars. Build a pipeline to train machine learning models. Implement regression, ensemble, and gradient-boosted models Deploy a machine learning model using MLFlow. Requirements Very basic Python programming knowledge. Familiarity with running code in Jupyter notebooks. Description Machine learning (ML) and AI are the key drivers of innovation today. Understanding how these models work can help you apply ML techniques effectively.In this course, expert instructor Joram Mutenge shows you how to master machine learning essentials by leveraging Python and the high-performance Polars library for advanced data manipulation.You will build an end-to-end machine learning application to predict laptop prices. Building this ML application will help you gain hands-on experience in data exploration, data processing, model creation, model evaluation, model tuning, and model deployment with MLFlow.Learn from a Data Science PractionerJoram has a master's degree in Data Science from the University of Illinois Urbana-Champaign, and currently works in data at a manufacturing company building demand forecasting models. He has years of experience building and deploying machine learning models. In this course, he shares the lessons he has learned along the way.Making the most of this courseThe modules in this course build on top of each other. Learn by following the order in which these modules are presented. This will help you understand the material better. To further cement the understanding, type out the code and run it on your computer instead of passively watching. Finally, apply the knowledge learned to your own dataset. Overview Section 1: Introduction Lecture 1 A brief Introduction to Machine Learning Section 2: Reading the Data Lecture 2 Loading data Section 3: Exploratory Data Analysis (EDA) Lecture 3 Descriptive statistics and plots Section 4: Cleaning and Processing Lecture 4 Cleaning columns: Ram, Weight Lecture 5 Cleaning column: Memory Lecture 6 Cleaning column: Memory (part II) Lecture 7 Cleaning column: Screen Resolution Lecture 8 Cleaning column: CPU Lecture 9 Cleaning column: GPU Lecture 10 Cleaning column: Operating System Lecture 11 Creating column: Clock Speed Lecture 12 Selecting columns to use Section 5: Data Transformation Lecture 13 Standardizing numeric values Lecture 14 One-Hot-Encoding categorical columns Lecture 15 Data partitioning Section 6: Model Building Lecture 16 Model building: Dummy Regressor Lecture 17 Model building: Linear Regression Lecture 18 Model building: Decision Tree Lecture 19 Model building: Catboost Lecture 20 Model building: Random Forest Section 7: Model Evaluation Lecture 21 Model Evaluation: R-squared Lecture 22 Model Evaluation: MSE Lecture 23 Model Evaluation: MAE Lecture 24 Model Evaluation: Residual plot Section 8: Hyperparameter Tuning Lecture 25 Hyperparameter tuning: Regression Lecture 26 Hyperparameter tuning: Decision Tree Lecture 27 Hyperparameter tuning: Catboost Lecture 28 Hyperparameter tuning: GridSearchCV Section 9: Model Deployment Lecture 29 End-to-End Notebook Lecture 30 Model deployment: MLFlow Professionals with tabular data in spreadsheets or databases seeking to make predictions from it.,Students interested in learning the fundamentals of applied machine learning.,Students and professionals seeking to learn the implementation of regression, ensemble, and gradient-boosted models.,Data professionals interested in learning how to deploy a model into production. Homepage https://www.udemy.com/course/machine-learning-with-polars/ Rapidgator https://rg.to/file/0bcbfa848404939a9ecb129ad297e286/lglrv.Machine.Learning.With.Polars.part2.rar.html https://rg.to/file/4697359241d6bf35d1c537e54705bab1/lglrv.Machine.Learning.With.Polars.part1.rar.html Fikper Free Download https://fikper.com/8TZqq8FEOA/lglrv.Machine.Learning.With.Polars.part1.rar.html https://fikper.com/8ynjZ1EVim/lglrv.Machine.Learning.With.Polars.part2.rar.html No Password - Links are Interchangeable