Learn Machine Learning Concepts, Build your Model & get accurate predictions without writing any Code using Qlik AutoML
What you will learn
Machine Learning on Qlik AutoML without writing any Code
5 Live Projects with Sample Dataset
Training and Testing ML Models, Improving Accuracy
Basics of Machine Learning
Model Parameters like SHAP, Feature Importance, Confusion Matrix etc, both in theory and practical
Resources to get right set of data to practice and apply Machine Learning
All Features and Options in Qlik AutoML
Creating Projects, Analysis & Versions in Qlik AutoML
Using Scenario Editor to do What-If Analysis & to gain business insights
Description
This Qlik AutoML Course will help you to become a Machine Learning Expert and will enhance your skills by offering you comprehensive knowledge, and the required hands-on experience on this newly launched Cloud based ML tool, by solving real-time industry-based projects, without needing any complex coding expertise.
Top Reasons why you should learn Qlik AutoML :
- Qlik AutoML is an automated machine learning platform for analytics teams, or any individual, to generate models, make predictions, and test business scenarios using a simple, code-free experience.
- You do not need Advanced Coding expertise generally required in the field of Machine Learning.
- Complex knowledge of Statistics, Algorithms, Mathematics that is difficult to master is also not required.
- Machine Learning Models that usually takes many days to build, are available very quickly in just a few minutes.
- The demand for ML professionals is on the rise. This is one of the most sought-after profession currently in the lines of Data Science.
- There are multiple opportunities across the Globe for everyone with Machine Learning skills.
- Qlik AutoML has a small learning curve and you can pick up even advanced concepts very quickly.
- You do not need high configuration computer to learn this tool. All you need is any system with internet connectivity.
Top Reasons why you should choose this Course :
- This course is designed keeping in mind the students from all backgrounds – hence we cover everything from basics, and gradually progress towards advanced topics.
- We will not just do some clicks, create model and finish the course – we will learn all the basics, and the various parameters on which ML models are evaluated – in detail. We will learn how to improve the model and generate more accurate predictions.
- We take live Industry Projects and do each and every step from start to end in the course itself.
- This course can be completed in a Day !
- All Doubts will be answered.
Most Importantly, Guidance is offered beyond the Tool – You will not only learn the Software, but important Machine Learning principles. Also, I will share the resources where to get the best possible help from, & also the sources to get public datasets to work on to get mastery in the ML domain.
A Verifiable Certificate of Completion is presented to all students who undertake this Qlik AutoML course.
Content
Introduction to Machine Learning & Automated Machine Learning (AutoML)
Qlik AutoML Introduction & Features
Important ML Terms to learn
Qlik AutoML Signup and Setup
First Qlik AutoML Project – Breast Cancer Diagnostic Prediction Analysis
2nd Qlik AutoML Project
3rd Qlik AutoML Project
4th Qlik AutoML Project
5th Qlik AutoML Project
Next Steps
The No-Code Revolution: My Honest Take on Qlik AutoML
Let’s be real for a second—most of us in the data space have spent years feeling a bit of “ML envy.” We see the data scientists over in the corner tweaking Python scripts and deep learning frameworks, while those of us on the business intelligence side are left wondering how to bring that same predictive power into our dashboards without spending six months learning linear algebra. This course, “No-Code Machine Learning with Qlik AutoML,” is essentially the bridge over that gap. It’s designed for the person who understands the business logic but doesn’t want to get bogged down in syntax errors.
What I appreciated most about this curriculum is that it doesn’t treat “No-Code” as “No-Thought.” It’s a common trap in the industry to think that clicking a few buttons solves everything. Instead, this course forces you to engage with the industry-standard tools and logic that actually make a model viable. We aren’t just hitting ‘run’; we are looking at feature importance and SHAP values to understand why the machine is making the decisions it is. For an experienced tech professional, that transparency is the difference between a toy project and a job-ready skill.
Prerequisites: What Do You Actually Need?
One of the best things here is the beginner to advanced trajectory, but don’t walk in totally cold. You don’t need to know how to code—obviously—but you do need a foundational grasp of data structures. If you know what a row and a column are, and you understand the difference between a “Target” (what you want to predict) and a “Feature” (the data points influencing that prediction), you are ready. A basic familiarity with the Qlik Cloud environment helps, but even if you’re coming from Tableau or Power BI, the concepts translate quickly enough that you won’t feel lost.
Mastering the Skills & Tools
The course is surprisingly comprehensive when it comes to the technical nuances of the Qlik ecosystem. You’ll dive deep into Qlik AutoML, moving far beyond simple binary classification. You’ll learn to navigate the Scenario Editor, which, in my opinion, is the hidden gem of the platform. It allows for What-If analysis that provides actual business insights rather than just static numbers. We also spent a significant amount of time on Confusion Matrices and Model Training/Testing. Understanding the trade-offs between precision and recall is vital if you want to build models that stakeholders actually trust.
Career Benefits & Job Roles
From a career growth perspective, this course is a massive level-up. We are seeing a huge shift toward “Augmented Analytics.” Companies are no longer satisfied with reports that tell them they lost 10% of their customers last month; they want to know which 10% are likely to leave next month. By completing this, you’re positioning yourself for roles like Predictive Analytics Consultant, BI Developer, or Analytics Manager. This is excellent certification prep for anyone looking to validate their expertise in the Qlik ecosystem. Adding “Machine Learning” to your resume—even without a CS degree—is a proven way to increase your market value in today’s data-driven economy.
The Pros: Why This Course Hits the Mark
- Hands-on Labs: You aren’t just watching videos. The 5 live projects involve real-world projects with actual datasets. This isn’t “clean” classroom data; it’s the kind of messy stuff you encounter in the wild, which makes the hands-on labs incredibly valuable.
- Explainable AI (XAI): I love that the course prioritizes SHAP values and feature importance. It moves the needle from “black box” mystery to actionable business insights. If you can explain to a CEO why a model predicted a specific outcome, you become indispensable.
- The Scenario Editor: Learning to use the What-If analysis tool is a game changer. It allows you to simulate different business environments, making your data analysis proactive rather than reactive.
- Efficiency: It respects your time. You learn how to build, iterate, and deploy versions of a model in a fraction of the time it would take to write the equivalent code in a notebook.
The Cons: An Honest Critique
If I have one gripe, it’s the inherent “platform lock-in.” While the Machine Learning concepts you learn are universal, the specific execution is very tied to the Qlik AutoML interface. If your company moves to a different stack, you’ll know the theory, but you’ll have to relearn the “where to click” part from scratch. Also, because it’s no-code, there is a temptation to skip the Basics of Machine Learning theory and jump straight to the results. You have to be disciplined enough to really digest the “why” provided in the lectures, or you risk building models that are statistically significant but practically useless.
Overall, if you’re looking to transition from traditional reporting to predictive modeling without spending years in a terminal, this course is a top-tier choice for career growth and gaining job-ready skills.