Online Course – Certified Professional Specialization in IBM AI Enterprise Workflow by IBM

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קורס אונליין - התמחות מקצועית מוסמכת ב-IBM AI Enterprise Workflow של IBM

Professional Certificate

Advanced

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • A clear connection between business priorities and technical applications.
  • Connecting machine learning to special use cases of artificial intelligence such as visual recognition and natural language processing.
  • Connecting Python with IBM cloud technologies.
  • Use enterprise-grade tools on IBM Cloud to create, launch, and test machine learning models.
  • Data preparation and model building using Jupyter notebooks and Python libraries.
  • Working with IBM Watson tools in the IBM cloud.

What you will learn in the course

Courses for which the course is suitable

  • Data Science Specialist
  • Data Analyst
  • Artificial Intelligence Developer
  • Machine Learning Engineer
  • Artificial Intelligence Expert
  • Artificial Intelligence Solutions Developer
  • Data Project Manager
  • Data Technology Consultant
  • Software developer with a specialization in data
  • Natural Language Processing Expert
  • Visual Recognition Expert

Internship – Course Series Number 6

This 6-lesson course series is designed to prepare you for the IBM AI Enterprise Workflow V1 Data Scientist Certification Exam. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, from business priorities to production.

The learning is designed to upgrade the skills of professional data specialists by:

  • A clear connection between business priorities and technical applications.
  • Connecting machine learning to special use cases of artificial intelligence such as visual recognition and natural language processing.
  • Connecting Python with IBM cloud technologies.

Videos, readings, and case studies in these courses are designed to guide you in your work as a data expert at a fictional streaming media company.

Focus of specialization

During this internship, the focus will be on the data science process in large, modern organizations. You will be guided through the process of using enterprise-grade toolkits in IBM Cloud, tools that you will use to create, launch, and test machine learning models.

Your favorite open source tools, like Jupyter notebooks and Python libraries, will be used extensively for data preparation and model building. The models will be achieved in the IBM cloud using IBM Watson tools that work seamlessly with open source tools.

After successfully completing this specialization, you will be ready to take the official IBM certification exam for IBM AI Enterprise Workflow.

Details of the courses that make up the specialization

AI Workflow: Business Priorities and Data Ingestion

Course 1

Duration: 7 hours

Rating: 4.3 (159 ratings)

What will you learn?

The first course in the IBM Business AI Workflow Certification introduces you to the specialization and prerequisites. The courses are designed for practical data scientists with an understanding of probability, statistics, linear algebra, and Python tools.

At the end of the course you will be able to:

  • Know the benefits of performing data science through a structured process.
  • Describe how the stages of design thinking fit into the business AI workflow.
  • Discuss several strategies for ranking business opportunities.
  • Explain where data science and data engineering overlap in the AI ​​workflow.
  • Explain the purpose of testing data entry.
  • Describe the use case of missing matrices.
  • Know the initial steps towards automating data entry pipelines.

Who should take this course?

The course is aimed at existing data scientists with expertise in building machine learning models.

What skills should you have?

  • Basic understanding of linear algebra.
  • Understanding of sampling, probability theory, and probability distributions.
  • Knowledge of descriptive statistics concepts and inference.
  • General understanding of machine learning techniques.
  • A practical understanding of Python and the packages used in data science.
  • Introducing IBM Watson Studio.
  • Introduction to the design thinking process.

Skills you will acquire

  • Artificial Intelligence (AI)
  • Data Science
  • Python programming
  • Information Engineering
  • Machine learning

The AI ​​workflow: data analysis and hypothesis testing

Course 2

Duration: 10 hours

Rating: 4.2 (110 ratings)

What will you learn?

In this course, you will begin your work for a hypothetical media company by performing exploratory data analysis (EDA). You will learn best practices for data visualization, handling missing data, and hypothesis testing.

At the end of the course you will be able to:

  • List best practices regarding EDA and data visualization.
  • Create a simple dashboard in Watson Studio.
  • Describe strategies for dealing with missing data.
  • Explain the difference between imprinting and multiple imprinting.
  • Use common distributions to answer probability questions.
  • Explain the role of exploratory testing in EDA.
  • Implement different methods for dealing with multiple tests.

Who should take this course?

The course is aimed at existing data scientists with expertise in building machine learning models.

What skills should you have?

  • Basic understanding of linear algebra.
  • Understanding of sampling, probability theory, and probability distributions.
  • Knowledge of descriptive statistics concepts and inference.
  • General understanding of machine learning techniques.
  • A practical understanding of Python and the packages used in data science.
  • Introducing IBM Watson Studio.
  • Introduction to the design thinking process.

Skills you will acquire

  • Artificial Intelligence (AI)
  • Data Science
  • Python programming
  • Information Engineering
  • Machine learning

The AI ​​workflow: feature engineering and bias detection

Course 3

Duration: 12 hours

Rating: 4.4 (68 ratings)

What will you learn?

This course introduces the next step in the workflow for our hypothetical media company. You will learn best practices for feature engineering, handling category inequality, and detecting bias in data.

At the end of the course you will be able to:

  • Use tools to address issues of inequality between categories.
  • Explain the ethical considerations regarding biases in data.
  • Use the Fairness 360 open source libraries to detect bias in models.
  • Perform dimensionality reduction techniques in the EDA phase.
  • Describe topic modeling techniques in natural language processing.
  • Implement best practices for handling data anomalies.
  • Implement anomaly detection algorithms.
  • Implement unsupervised learning techniques.
  • Implement basic clustering algorithms.

Who should take this course?

The course is aimed at existing data scientists with expertise in building machine learning models.

What skills should you have?

  • Basic understanding of linear algebra.
  • Understanding of sampling, probability theory, and probability distributions.
  • Knowledge of descriptive statistics concepts and inference.
  • General understanding of machine learning techniques.
  • A practical understanding of Python and the packages used in data science.
  • Introducing IBM Watson Studio.
  • Introduction to the design thinking process.

Skills you will acquire

  • Artificial Intelligence (AI)
  • Data Science
  • Python programming
  • Information Engineering
  • Machine learning

The AI ​​workflow: machine learning, visual recognition, and natural language processing

Course 4

Duration: 13 hours

Rating: 4.4 (78 ratings)

What will you learn?

The fourth course deals with the next stage of the workflow, defining models and associated data pipelines for a hypothetical media company.

At the end of the course you will be able to:

  • Discuss regression metrics, classification, and multiple classification metrics.
  • Explain the use of linear and logistic regression.
  • Describe strategies for searching networks and performing a cross-test.
  • Apply evaluation metrics to select models.
  • Explain the use of tree-based algorithms.
  • Explain the use of neural networks.
  • Create a neural network model in Tensorflow.
  • Create and test an instance of Watson Visual Recognition.
  • Create and test a Watson NLU instance.

Who should take this course?

The course is aimed at existing data scientists with expertise in building machine learning models.

What skills should you have?

  • Basic understanding of linear algebra.
  • Understanding of sampling, probability theory, and probability distributions.
  • Knowledge of descriptive statistics concepts and inference.
  • General understanding of machine learning techniques.
  • A practical understanding of Python and the packages used in data science.
  • Introducing IBM Watson Studio.
  • Introduction to the design thinking process.

Skills you will acquire

  • Artificial Intelligence (AI)
  • Data Science
  • Python programming
  • Information Engineering
  • Machine learning

The AI ​​workflow: deploying models in an organization

Course 5

Duration: 9 hours

Rating: 4.2 (51 ratings)

What will you learn?

The course deals with the deployment of models in an organization and the processes required to implement the built models.

At the end of the course you will be able to:

  • Explain the process of deploying models in an organization.
  • Manage the life of the model after deployment.
  • Apply model management techniques.

Who should take this course?

The course is aimed at existing data scientists with expertise in building machine learning models.

What skills should you have?

  • Basic understanding of linear algebra.
  • Understanding of sampling, probability theory, and probability distributions.
  • Knowledge of descriptive statistics concepts and inference.
  • General understanding of machine learning techniques.
  • A practical understanding of Python and the packages used in data science.
  • Introducing IBM Watson Studio.
  • Introduction to the design thinking process.

Skills you will acquire

  • Artificial Intelligence (AI)
  • Data Science
  • Python programming
  • Information Engineering
  • Machine learning

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