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.
段階的な特徴エンジニアリング：MLデータ準備
欠損データ、スケーリング、特徴抽出、選択、高度な技術と自動化された特徴エンジニアリング
Lahat ng Antas • 22 Mga lektura • Panghabambuhay na Access
Na-verify na Master Instructor
TRAILER
Kilalanin ang iyong instructor: Dr. Amar Massoud
19 ms
5.0 / 5.0
Global Rating1
Mga Aktibong Mag-aaralIngles (American)
99 Mga Subtitle na Wika1 oras 37 min
Kabuuang NilalamanAng matututuhan mo
モデルの精度を向上させるために特徴エンジニアリング技術を理解し適用します。
偏りを特定し軽減し、公平で倫理的な特徴選択を確保します。
FeatureToolsのようなライブラリを使用して自動化された特徴エンジニアリングを実装します。
再現性とコラボレーションのために特徴のバージョンを追跡し文書化します。
Nilalaman ng Kurso
10 na mga seksyon • 22 mga lektura • 01:37:25 kabuuang habaHandling 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.
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Mga kinakailangan
Pythonと機械学習の概念の基本的な理解。
PandasとScikit-learnの知識。
データセットの操作経験が推奨されます。
高度な特徴エンジニアリング経験は不要です。
Target na 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.
Tungkol sa kursong ito
機械学習モデルの潜在能力を最大限に引き出すための包括的な特徴エンジニアリングコースです。データサイエンス愛好家、機械学習実践者、開発者向けに設計されており、このコースはモデルのパフォーマンス、精度、解釈性を向上させるための必須および高度な特徴エンジニアリング技術をカバーしています。
欠損データの処理や特徴の変換からFeatureToolsのようなライブラリを使用した自動化された特徴エンジニアリングまで、強力で関連性の高い特徴を作成するためのスキルを学びます。スケーリング、正規化、ワンホットエンコーディング、特徴抽出などの主要な技術を発見してください。より深いパターンを明らかにするために多項式や相互作用特徴を適用するタイミングを理解し、時系列データに対して時間ベースの特徴を活用します。このコースでは重要な倫理的考慮事項も紹介し、特徴選択において偏りを避け、公平性を確保し、解釈性を向上させる方法を示します。
実践的な例、一貫した実世界のユースケース、各方法のPythonコードを通じて、すぐに適用できる実践的な経験を獲得します。また、ドキュメンテーションとバージョン管理のベストプラクティスも学び、特徴が整理され再現可能であることを確保します。最後に、継続的な学習と反復技術により、データが進化するにつれてモデルを関連性と効果的に保つ準備ができます。
特徴エンジニアリングスキルを磨きたいか、ワークフローを自動化したいかにかかわらず、このコースは高性能で倫理的なモデルを構築するための知識とツールを提供します。今すぐ登録して、機械学習における特徴エンジニアリングの習得に向けて一歩踏み出しましょう！
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Kilalanin ang iyong instruktor
Dr. Amar Massoud
Pangunahing Instruktor
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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