Build, Deploy, and Evaluate ML Models Using IBM AutoAI (Zero Code)
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Training TypeLive Training
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CategoryArtificial Intelligence
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Duration3 Hours
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Rating4.8/5
Course Introduction
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.
Available Batches
| 25 Aug 2025 | Mon ( 1 Day ) | 12:00 PM - 03:00 PM (Eastern Time) |
| 22 Sep 2025 | Mon ( 1 Day ) | 12:00 PM - 03:00 PM (Eastern Time) |
| 20 Oct 2025 | Mon ( 1 Day ) | 12:00 PM - 03:00 PM (Eastern Time) |
Pricing
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Disruption across sectors (e.g., healthcare, retail, finance)
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AI engagement, insights, and automation
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CRISP-DM, SEMMA, KDD comparison
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Data transformation and model evaluation strategies
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Data types: nominal, ordinal, continuous, discrete
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The 5 Vs of Big Data: Volume, Variety, Velocity, Veracity, Value
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Structured vs. unstructured data
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Missing data handling
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Tidy data principles and transformation
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Feature engineering and one-hot encoding
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Descriptive statistics, variance, and standard deviation
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Data visualization techniques: histograms, boxplots, scatterplots
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Confusion matrix, accuracy, precision, recall
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F1 score and when to prioritize certain metrics
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Dangers of overfitting and generalization
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Key libraries: Pandas, NumPy, Scikit-learn, NLTK, SciPy
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Development environments (Jupyter Notebooks, Anaconda)
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Automated machine learning pipelines
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Model selection and interpretability
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Hands-on exercises using IBM Watson Studio