Master feature engineering for machine learning with a practical, hands-on approach, applying techniques from basic feature creation to PCA, imputation, encoding, regularization, and cross-validation using Python code.
Feature Engineering Step by Step: ML Data Preparation
Data yang Hilang, Skalasi, Ekstraksi Fitur, Seleksi, Teknik Lanjutan & Penataan Fitur Otomatis
All Levels • 22 Lectures • Full Lifetime Access
Verified Master Instructor
TRAILER
Meet your instructor: Dr. Amar Massoud
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Active StudentsEnglish (US)
99 Languages Subtitles1h 37m
Total ContentWhat you will learn
Memahami dan menerapkan teknik penataan fitur untuk meningkatkan akurasi model.
Mengidentifikasi dan mengurangi bias, memastikan seleksi fitur yang adil dan etis.
Mengimplementasikan penataan fitur otomatis menggunakan perpustakaan seperti FeatureTools.
Mengakses dan mendokumentasikan versi fitur untuk reproduksi dan kolaborasi.
Course Content
10 sections • 22 lectures • 01:37:25 total lengthHandling Missing Data
09:03
Learn strategies to handle missing data, from mean, median, and mode imputation to modeling-based approaches like KNN and regression. Apply these to numerical and categorical features, including frequent category imputation.
Feature Transformation
01:57
Explore feature transformation techniques for machine learning, including scaling, normalization, log and power transformations, binning, and one hot encoding for categorical data.
Scaling and Normalization
05:40
Learn to scale and normalize features using standardization to zero mean and unit variance or min-max normalization to 0–1, improving model training on housing data.
Log Transformation, Power Transformation
05:16
Binning Numerical Variables
03:36
One-Hot Encoding vs. Label Encoding
04:07
Compare one-hot encoding and label encoding for transforming categorical data into numerical features, weighing dimensionality against potential ordinal assumptions in different datasets.
Weekly Study Plan
Plan Your Learning Journey
AI generates a personalized schedule based on your availability5 hrs/week
Requirements
Pemahaman dasar tentang Python dan konsep pembelajaran mesin.
Familiar dengan Pandas dan Scikit-learn.
Pengalaman dasar bekerja dengan dataset disarankan.
Tidak diperlukan pengalaman penataan fitur lanjutan.
Target Audience
Data science enthusiasts looking to deepen their skills in feature engineering.
Beginner and intermediate data scientists aiming to improve model performance.
Machine learning practitioners who want practical, hands-on experience.
Developers interested in ethical AI and responsible data practices.
About this course
Buka potensi penuh model mesin Anda dengan kursus komprehensif kami tentang Penataan Fitur. Dirancang untuk penggemar ilmu data, praktisi mesin belajar, dan pengembang, kursus ini mencakup teknik penataan fitur esensial dan lanjutan yang akan meningkatkan kinerja, akurasi, dan interpretabilitas model Anda.
Dari menangani data yang hilang dan transformasi fitur hingga penataan fitur otomatis dengan perpustakaan seperti FeatureTools, Anda akan mempelajari keterampilan untuk membuat fitur yang kuat dan relevan. Temukan teknik kunci seperti skalasi, normalisasi, pengkodean satu-panas, dan ekstraksi fitur. Pahami kapan menerapkan fitur polinomial dan interaksi untuk mengungkap pola yang lebih dalam, serta memanfaatkan fitur berbasis waktu untuk data seri waktu. Kursus ini juga memperkenalkan pertimbangan etis penting, menunjukkan kepada Anda cara menghindari bias, memastikan keadilan, dan meningkatkan interpretabilitas dalam fitur Anda.
Melalui contoh praktis, kasus penggunaan konsisten di dunia nyata, dan kode Python untuk setiap metode, Anda akan memperoleh pengalaman praktis yang dapat Anda terapkan segera. Anda juga akan mempelajari praktik terbaik untuk dokumentasi dan kontrol versi, memastikan fitur Anda terorganisir dan dapat direproduksi. Terakhir, dengan teknik pembelajaran berkesinambungan dan iterasi, Anda dilengkapi untuk menjaga model Anda relevan dan efektif seiring evolusi data.
Baik Anda ingin memperhalus keterampilan penataan fitur atau mengotomatiskan alur kerja Anda, kursus ini menyediakan pengetahuan dan alat untuk membangun model berkinerja tinggi dan etis. Bergabunglah hari ini dan mengambil langkah menuju penguasaan penataan fitur dalam pembelajaran mesin!
Career Opportunities & Job Roles
Build practical, job-ready skills and qualify for high-impact roles worldwide
Job-Ready Curriculum
Target Roles You Can Apply For
AI Solutions Engineer
Data Science Consultant
Machine Learning Practitioner
AI Workflow & Automation Specialist
Estimated Annual Salary
$105,000 – $165,000 / yr
Global Industry Demand
Fastest Growing Worldwide (#1)
In-Demand Skills You Master* Estimates based on global tech hiring benchmarks and industry compensation reports.
Model Fine-Tuning & Evaluation
Data Pipelines & Vector Analysis
Applied Generative AI Architecture
Predictive Analytics & Deployment
Meet your instructor
Dr. Amar Massoud
Main Instructor
PhD in computer science and IT manager with 36 years technical experience in various fields including IT Security, IT Governance, IT Service Management , Software Development, Project Management, Business Analysis and Software Architecture. I hold 80+ IT certifications such as :
ITIL 4 Master, ITIL 3 Expert
ISO 27001 Auditor, ComptIA Security+, GSEC, CEH, ECSA, CISM, CISSP, CISA
PGMP, MSP
PMP, PMI-ACP, Prince2 Practitioner, Praxis, Scrum Master
COBIT 2019 Implementor, COBIT 5 Assessor/Implementer
TOGAF certified
Lean Specialist, VSM Specialist
PMI RMP, ISO 31000 Risk Manager, ISO 22301 Lead Auditor
PMI-PBA, CBAP
Lean Six Sigma Black Belt, ISO 9001 Implementer
Azure Administrator, Azure DevOps Expert, AWS Practitioner
And many more....
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Yes! Upon completing 100% of the course lectures and assignments, you can instantly download your verified digital Certificate of Completion directly from your Lucebra profile.
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