À propos de ce cours
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Dates limites flexibles

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Niveau avancé

Approx. 7 heures pour terminer

Recommandé : This course requires 7.5 to 9 hours of study....

Anglais

Sous-titres : Anglais

Compétences que vous acquerrez

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau avancé

Approx. 7 heures pour terminer

Recommandé : This course requires 7.5 to 9 hours of study....

Anglais

Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
4 heures pour terminer

Data transforms and feature engineering

6 vidéos (Total 31 min), 14 lectures, 5 quiz
6 vidéos
Introduction to Class Imbalance1 min
Class Imbalance Deep Dive9 min
Introduction to Dimensionality Reduction2 min
Dimension Reduction13 min
Case study intro / Feature Engineering1 min
14 lectures
Data Transformation: Through the eyes of our Working Example3 min
Transforms / Scikit-learn3 min
Pipelines3 min
Class imbalance: Through the eyes of our Working Example3 min
Class Imbalance5 min
Sampling techniques2 min
Models that naturally handle imbalance2 min
Data bias2 min
Dimensionality Reduction: Through the eyes of our Working Example3 min
Why is dimensionality reduction important?3 min
Dimensionality reduction and Topic models5 min
Topic modeling: Through the eyes of our Working Example3 min
Getting Started with the topic modeling case study (hands-on)2 h
Data transforms and feature engineering: Summary/Review5 min
5 exercices pour s'entraîner
Getting Started: Check for Understanding2 min
Class imbalance, data bias: Check for Understanding2 min
Dimensionality Reduction: Check for Understanding3 min
CASE STUDY - Topic modeling: Check for Understanding2 min
Data transforms and feature engineering:End of Module Quiz10 min
Semaine
2
3 heures pour terminer

Pattern recognition and data mining best practices

4 vidéos (Total 10 min), 11 lectures, 5 quiz
4 vidéos
Introduction to Outliers2 min
Outlier Detection3 min
Introduction to Unsupervised learning2 min
11 lectures
ai360: Through the eyes of our Working Example3 min
Introduction to ai360 (hands-on)15 min
Outlier detection: Through the eyes of our Working Example3 min
Outliers3 min
Unsupervised learning: Through the eyes of our Working Example3 min
An overview of unsupervised learning2 min
Clustering3 min
Clustering evaluation3 min
Clustering: Through the eyes of our Working Example3 min
Getting Started with the clustering case study (hands-on)2h 10min
Pattern recognition and data mining best practices: Summary/Review4 min
5 exercices pour s'entraîner
ai360 Tutorial: Check for Understanding2 min
Outlier detection: Check for Understanding2 min
Unsupervised learning: Check for Understanding2 min
CASE STUDY - Clustering: Check for Understanding2 min
Pattern recognition and data mining best practices: End of Module Quiz12 min

Enseignants

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Mark J Grover

Digital Content Delivery Lead
IBM Data & AI Learning
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Ray Lopez, Ph.D.

Data Science Curriculum Leader
IBM Data & Artificial Intelligence

À propos de IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

À propos du Spécialisation IBM AI Enterprise Workflow

This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow....
IBM AI Enterprise Workflow

Foire Aux Questions

  • Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.

  • Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.

  • This course assumes that you are already familiar with basic data science concepts including probability and statistics, linear algebra, machine learning, and the use of Python and Jupyter. It is assumed you have completed the first two courses of the specialization: AI Workflow: Business Priorities and Data Ingestion, AI Workflow: Data Analysis and Hypothesis Testing.

  • No. Most of the exercises may be completed with open source tools running on your personal computer. However, the exercises are designed with an enterprise focus and are intended to be run in an enterprise environment that allows for easier sharing and collaboration. The exercises in the last two modules of the course are heavily focused on deployment and testing of machine learning models and use the IBM Watson tooling found on the IBM Cloud.

  • Yes. All IBM Cloud Data and AI services are based upon open source technologies.

  • The exercises in the course may be completed by anyone using the IBM Cloud "Lite" plan, which is free for use.

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