Build, Deploy, and Evaluate ML Models Using IBM AutoAI (Zero Code)

About the Course:

This course is divided into two parts: In the first 90 minutes, learners are introduced to the end-to-end lifecycle of machine learning (ML) model development using IBM AutoAI. It begins with an overview of the impact of AI on modern industry, then covers the fundamentals of data types, preparation, and analytics methodologies such as CRISP-DM. Students will gain practical knowledge in structuring, cleansing, and transforming data, followed by a hands-on understanding of model evaluation techniques, including accuracy, precision, recall, F1 score, and the confusion matrix. The second part of the course enrolls learners in the IBM Watsonx.ai platform for a 30-day free trial, where they will conduct AutoAI experiments.

Students will explore the foundations of data analytics, AI applications, model evaluation metrics, and the role of data preparation in AI-driven workflows.

Course Objective:

By the end of this course, students will be able to:

• Understand the current and emerging impact of AI across various industries.
• Differentiate between key data types and assess data quality and structure.
• Apply data analytics methodologies such as CRISP-DM, SEMMA, and KDD.
• Perform data cleaning, transformation, and feature engineering tasks.
• Use Python libraries to manipulate and analyze structured datasets.
• Evaluate machine learning models using confusion matrices, precision, recall, accuracy, and F1 scores.
• Identify and mitigate issues such as overfitting.
• Deploy and interpret models using IBM AutoAI.

Who is the Target Audience?

This course is ideal for:

• Business Analysts and Domain Experts looking to apply AI without writing code
• Data Science Beginners who want hands-on experience with automated ML tools
• Project Managers and Product Owners seeking to understand AI workflows for strategic decision-making
• Non-technical Professionals in fields like healthcare, finance, retail, or marketing who want to explore AI-driven insights
• Educators and Trainers introducing students to machine learning concepts using low-code/no-code platforms
• Anyone interested in AI who wants to build and deploy models quickly, without coding

Basic Knowledge:

No coding experience required, just curiosity and basic familiarity with data and analytics.

Students and current employees seeking careers as AI Engineers, Prompt Engineers, Data Scientists, Data Analysts, Data Engineers, and Data Journalists.

Basic knowledge of statistics.

To get the most out of this session, participants should have:

• A basic understanding of machine learning concepts (e.g., models, training data, predictions)
• Familiarity with data science fundamentals, such as data preparation and evaluation
• Basic knowledge of statistics, including terms like mean, variance, and data visualization
• An understanding of data types (structured vs. unstructured) and the basics of Big Data
• Comfort using web-based tools (e.g., cloud platforms or dashboards)

No programming experience is required.

Optional: Prior exposure to Python or IBM Watson Studio is helpful but not necessary.