Preprocessing with scikit-learn – A Complete Guide

Free Download Preprocessing with scikit-learn – A Complete Guide
Published 7/2023
Created by Jitendra Singh
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 6 Lectures ( 2h 29m ) | Size: 679 MB

Data Preprocessing for Machine Learning with Python’s scikit-learn Library
What you’ll learn
Gain a deep understanding of data preprocessing using scikit-learn.
Learn essential techniques to clean, transform, and prepare data for machine learning tasks.
Engage in hands-on projects and practical examples for real-world application.
Enhance the performance of machine learning models through effective data preprocessing.
Requirements
Basic understanding of Python programming.
Familiarity with data manipulation concepts.
Some exposure to machine learning fundamentals (beneficial but not mandatory).
Willingness to explore and experiment with scikit-learn’s preprocessing capabilities.
Description
Course Overview:Dive deep into the world of data preprocessing with scikit-learn, the most popular Python library for machine learning. This comprehensive course will guide you through the essential steps of data preprocessing, ensuring your datasets are primed and ready for a variety of machine learning models.What You’ll Learn:Foundations of Data Preprocessing: Understand the significance of preprocessing and how it can dramatically impact the performance of your machine learning models.Handling Missing Data: Techniques to identify, evaluate, and impute missing data to maintain the integrity of your datasets.Feature Scaling: Master normalization and standardization methods to ensure features contribute equally to model performance.Categorical Data Encoding: Dive into techniques like one-hot encoding, ordinal encoding, and binary encoding to convert categorical data into a format suitable for machine learning.Feature Engineering: Discover how to create new features, transform existing ones, and select the most impactful features for your models.Dimensionality Reduction: Learn about PCA, t-SNE, and other techniques to reduce the number of features while retaining essential information.Pipeline Creation: Seamlessly integrate preprocessing steps using scikit-learn’s Pipeline to streamline your machine learning workflow.Who This Course Is For:Beginners who are just starting out with machine learning and data preprocessing.Intermediate data scientists looking to refine their preprocessing skills.Professionals aiming to integrate scikit-learn preprocessing techniques into their data workflows.Anyone interested in ensuring their machine learning models are built on well-prepared data.Course Features:Hands-on Projects: Apply what you’ve learned with real-world projects and datasets.Quizzes & Assignments: Test your knowledge and understanding throughout the course.Expert Instructors: Learn from industry professionals with years of experience in data science and machine learning.Lifetime Access: Revisit the course material anytime, with lifetime access to all updates and additions.Prerequisites:Basic knowledge of Python programming.Familiarity with fundamental concepts of machine learning is beneficial but not mandatory.Enroll now and master the art of data preprocessing with scikit-learn. Equip yourself with the skills to ensure that your machine learning models are built on robust, clean, and optimized data.
Who this course is for
Aspiring data scientists and machine learning enthusiasts.
Beginners seeking a strong foundation in data preprocessing.
Experienced practitioners aiming to enhance their skills with scikit-learn.
Homepage

www.udemy.com/course/preprocessing-with-scikit-learn-a-complete-guide/