Data-Centric Machine Learning with Python - Hands-On Guide

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Free Download Data-Centric Machine Learning with Python - Hands-On Guide
Published: 3/2025
Created by: Meta Brains,Skool of AI
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 37 Lectures ( 3h 35m ) | Size: 2 GB

Master data preprocessing, feature engineering, and ML modeling techniques with a hands-on loan prediction project.
What you'll learn
Preprocess data effectively for machine learning models.
Perform exploratory data analysis using Python libraries.
Differentiate between supervised and unsupervised learning.
Build and optimize machine learning algorithms in Python.
Create insightful data visualizations and plots.
Apply feature engineering techniques to improve models.
Evaluate model performance with appropriate metrics.
Solve real-world problems using machine learning workflows.
Requirements
Basic understanding of Python programming.
Familiarity with high school-level math concepts.
A computer with Python and necessary libraries installed.
No prior data science or machine learning knowledge needed!
Description
In a world where data is the new oil, mastering machine learning isn't just about algorithms-it's about understanding the data that fuels them.This intensive 3-4 hour course dives deep into the data-centric approach to machine learning using Python, equipping parti[beeep]nts with both theoretical knowledge and practical skills to extract meaningful insights from complex datasets. The curriculum focuses on the critical relationship between data quality and model performance, emphasizing that even the most sophisticated algorithms are only as good as the data they're trained on.Parti[beeep]nts will embark on a comprehensive learning journey spanning from foundational concepts to advanced techniques. Beginning with an introduction to machine learning paradigms and Python's powerful data science ecosystem, the course progresses through the crucial stages of data preparation-including exploratory analysis, handling missing values, feature engineering, and preprocessing. Students will gain hands-on experience with supervised learning techniques, mastering both regression and classification approaches while learning to select appropriate evaluation metrics for different problem types.The course extends beyond basic applications to cover sophisticated model selection and validation techniques, including cross-validation and hyperparameter tuning, ensuring models are robust and generalizable. Unsupervised learning methods such as clustering and anomaly detection further expand parti[beeep]nts' analytical toolkit, while specialized topics like text analysis, image classification, and recommendation systems provide insight into real-world applications.The learning experience culminates in a practical loan prediction project where parti[beeep]nts apply their newly acquired knowledge to develop a predictive model for loan approvals based on applicant information-bridging theoretical understanding with practical implementation. Through this hands-on approach, students will develop the critical thinking skills necessary to tackle complex machine learning challenges in various professional contexts, making this course ideal for aspiring data scientists, analysts, and technology professionals seeking to leverage the power of data-centric machine learning.Don't wait! Transform your career with this focused course that delivers in hours what others learn in months. With companies actively seeking data-centric ML skills, secure your spot now to gain the competitive edge that commands premium salaries. Your future in data science starts here!
Who this course is for
Beginners looking to explore machine learning concepts.
Python programmers wanting to expand their skill set.
Data analysts eager to transition into machine learning.
Students interested in practical applications of data science.
Professionals seeking to automate data-driven decision-making.
Enthusiasts curious about building predictive models.
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