IBM Machine Learning Data Scientist v1 C1000-144 Certified Exam Dumps

C1000-144 Exam Dumps

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Certification Provider: IBM
Exam Code / Number: C1000-144
Exam Name: IBM Machine Learning Data Scientist v1
Exam Questions: 0
Corresponding Certification: IBM Certification

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IBM C1000-144 exam is an essential certification for professionals who want to become IBM Machine Learning Data Scientists. C1000-144 exam measures the candidate's knowledge and skills in various areas such as data preparation, modeling, evaluation, deployment, and monitoring of machine learning models. IBM Machine Learning Data Scientist v1 certification is valuable for professionals who want to advance their careers in this field, as it provides them with the knowledge and skills required to design, build, deploy, and monitor machine learning models using IBM software.

IBM C1000-144 exam tests the candidate's proficiency in various domains related to machine learning. C1000-144 exam is based on the IBM Watson Studio platform, which is a cloud-based platform that enables data scientists to create and deploy machine learning models. C1000-144 exam covers topics such as data preparation, data visualization, machine learning algorithms, model evaluation, and deployment. C1000-144 exam also includes questions on statistics, programming languages such as Python and R, and data science tools.

IBM C1000-144 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Evaluate Business Problem Including Ethical Implications21%- Business Problem Evaluation
  • 1. Perform AI design thinking
  • 2. Understand available data
  • 3. Understand ethical challenges in the business problem
  • 4. Assess progress on the AI Ladder
  • 5. Understand business requirements
Topic 2: Exploratory Data Analysis Including Data Preparation18%- Data Understanding and Preparation
  • 1. Handle missing values and outliers
  • 2. Visualize and summarize data
  • 3. Explore and profile data
  • 4. Prepare and clean data
  • 5. Perform feature engineering
Topic 3: IBM Watson Studio and AI Tools18%- IBM AI Platform Usage
  • 1. Collaborate within data science environments
  • 2. Work with IBM AI tools and services
  • 3. Use IBM Watson Studio
  • 4. Manage projects and assets
Topic 4: Model Deployment and Monitoring20%- Operationalization
  • 1. Implement enterprise AI workflows
  • 2. Monitor model performance
  • 3. Deploy machine learning models
  • 4. Manage model lifecycle
Topic 5: Model Selection and Development23%- Machine Learning Modeling
  • 1. Train machine learning models
  • 2. Evaluate model performance
  • 3. Interpret model results
  • 4. Optimize model parameters
  • 5. Select appropriate machine learning algorithms


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