This exceeded my expectations. The lessons flowed logically and the real-world applications were spot on. Great job!
Machine Learning with Qlik AutoML: No-Code Predictive Modeling
Build and deploy predictive models for business insights using automated machine learning tools without writing a single line of code.
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AI instructor
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In English
Lessons, tasks and certificate — all fully in your language.
About this course
Course contents
6 modules · 30 lessons · 3h
1
Introduction to Automated Machine Learning
Establish foundational knowledge and understand the AutoML environment.
- What is Machine Learning?
Understand ML as a way to find patterns in data to make predictions without explicit programming.6 min - The Vocabulary of ML
Define key terms like features, targets, training sets, and testing sets.6 min - The Machine Learning Lifecycle
Walk through the stages from business problem definition to model deployment.6 min - Introduction to Qlik AutoML
Navigate the Qlik Cloud interface where automated ML experiments happen.6 min - Types of ML Problems
Distinguish between classification and regression in a business context.6 min
2
Data Preparation & Experiment Setup
Learn how to format data and initiate an AutoML experiment.
- Preparing Your Dataset
Learn the requirements for clean, tabular data suitable for Qlik AutoML.6 min - Identifying the Target Variable
Choose the specific outcome you want the model to predict.6 min - Configuring Your First Experiment
Set up the initial parameters and upload data to start an automated run.6 min - Data Profiling in Qlik
Use built-in tools to check data distributions and potential issues.6 min - Handling Missing Values
Understand how AutoML automatically manages nulls and gaps in your data.6 min
3
Feature Engineering & Model Refining
Improve model performance through smart data manipulation.
- Basics of Feature Engineering
Create new input variables that help the model learn better patterns.6 min - Transforming Categorical Data
Understand how text-based categories are converted for machine processing.6 min - Analyzing Feature Importance
Identify which columns have the most impact on your target prediction.6 min - The Art of Feature Selection
Remove noisy or redundant data to streamline and improve your model.6 min - Iterating for Quality
Refine the model by adjusting features and re-running the experiment.6 min
4
Evaluating Model Performance
Interpret metrics to determine if a model is ready for business use.
- The AutoML Leaderboard
Compare different algorithms automatically trained by Qlik.6 min - Understanding Accuracy
Learn when simple accuracy is a good metric and when it is misleading.6 min - Precision and Recall
Balance the cost of false positives versus false negatives.6 min - The F1-Score and AUC
Use advanced metrics to get a holistic view of model health.6 min - Using the Confusion Matrix
Visualize exactly where the model is making mistakes.6 min
5
Interpreting Results & Ethical AI
Explain model decisions and ensure fairness.
- Global vs. Local Interpretability
Distinguish between overall model drivers and individual prediction reasons.6 min - Interpreting Prediction Drivers
Use SHAP values to explain why a specific prediction was made.6 min - Identifying Bias in Data
Spot historical biases that could lead to unfair model outcomes.6 min - Mitigating Bias & Ethical AI
Apply techniques to ensure models are fair and ethically sound.6 min - Communicating Insights
Translate technical model results into actionable business stories.6 min
6
Deployment & Real-World Application
Move from a trained model to generating actual business value.
- Deploying Your Model
Promote a successful experiment to a production-ready deployment.6 min - Generating Batch Predictions
Apply your model to new datasets to predict future outcomes.6 min - Real-Time Prediction Use Cases
Explore how models can provide instant feedback in business apps.6 min - Integrating with Qlik Sense
Visualize predictions alongside historical data in dashboards.6 min - Monitoring & Maintenance
Keep an eye on model performance over time to prevent 'drift'.6 min
The Customer Churn Predictor
You will take a raw dataset of customer behavior, prepare it for Qlik AutoML, build a model to predict who will leave the service, evaluate its fairness, and deploy it to generate a list of high-risk customers.
What you'll get
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Certificate of completion
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Personal AI tutor
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Lifetime access
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Phone or computer
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14-day refund
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Short & focused
3h of practical content
Certificate of completion
Every course you complete on PickAClass issues a credential like this — original, with its own code, verifiable by URL, and detailed about what was actually demonstrated.
Reviews (4)
This course exceeded my expectations. The real-world applications discussed are incredibly useful. Great job!
Wow, what a fantastic learning experience. The structure was logical, and I felt like I learned so much in a short time. Definitely recommend.
Fantastic learning experience. The pace was perfect, and the examples really solidified the concepts. Big thumbs up!
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Data Science Profession: A Beginner's Guide to Real-World Applications
Frequently asked
What do I need to take this course? +
Just a phone or computer with internet. No installs, no special hardware.
How do I pay? +
By card via Stripe. We don’t store card details — Stripe handles them securely.
Can I get a refund? +
Yes — full refund within 14 days, no questions asked.
How long will I have access? +
Forever. Once you purchase, the course is yours to revisit anytime.
Will I get a certificate? +
Yes. On completion you'll receive a certificate you can add to your LinkedIn profile.
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