Learn Machine Learning Concepts, Build your Model & get accurate predictions without writing any Code using Qlik AutoML
⏱️ Length: 2.7 total hours
⭐ 4.06/5 rating
👥 53,818 students
🔄 March 2022 update
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- Course Overview
- This transformative course offers a gateway into the world of artificial intelligence and machine learning, specifically designed for individuals eager to harness the power of predictive analytics without the traditional barriers of coding.
- It demystifies complex ML concepts, presenting them through the intuitive, user-friendly interface of Qlik AutoML, a leader in augmented analytics.
- Participants will discover how to transition from raw, disparate data to actionable business insights with unprecedented speed and simplicity.
- The curriculum is meticulously structured to empower business analysts, data enthusiasts, and decision-makers to independently develop, evaluate, and deploy robust machine learning models for real-world scenarios.
- By focusing on practical application over theoretical abstraction, the course enables a paradigm shift, making advanced analytics accessible and operational for everyday business challenges.
- It underscores the strategic advantage gained by democratizing machine learning, allowing organizations to foster a culture of data-driven innovation and foresight across all departments.
- This program is not just about using a tool; it’s about unlocking a new dimension of decision intelligence and competitive agility for both individuals and enterprises in a rapidly evolving market landscape.
- Requirements / Prerequisites
- A foundational curiosity for data and its potential to solve real-world problems and drive impactful decisions.
- Basic computer literacy and comfort navigating cloud-based applications and web interfaces.
- No prior experience in programming languages (e.g., Python, R) or advanced statistical modeling is necessary, making it ideal for beginners.
- An eagerness to explore how machine learning can enhance various business operations, strategic planning, and customer engagement.
- Access to a stable internet connection and a modern web browser to utilize the Qlik Cloud platform.
- While not strictly required, a basic understanding of business metrics, reporting, and data terminology can enhance the learning experience.
- Skills Covered / Tools Used
- Proficiency in AI-Driven Data Storytelling: Learn to articulate complex predictive outcomes into clear, concise, and compelling narratives for diverse stakeholders.
- Conceptual Data Imputation & Feature Engineering: Understand how Qlik AutoML implicitly handles crucial data preparation steps to optimize model performance, even without manual coding.
- Ethical AI Interpretation: Develop an understanding of model transparency and fairness through built-in explainability features like SHAP, fostering responsible AI deployment.
- Strategic Business Foresight: Gain the ability to anticipate market trends, customer behaviors, and operational inefficiencies using robust predictive models.
- Rapid Prototype Development: Acquire skills in quickly building, evaluating, and iterating on ML models to test business hypotheses and accelerate innovation cycles.
- Collaborative Analytics Integration: Understand how Qlik AutoML outputs can be seamlessly integrated into existing Qlik Sense dashboards for holistic decision-making and cross-functional collaboration.
- Advanced Performance Metric Interpretation: Delve into the practical implications of various model evaluation metrics for different business objectives, moving beyond basic accuracy.
- What-If Scenario Simulation for Business Planning: Master the art of exploring hypothetical situations to inform strategic decisions, optimize resource allocation, and conduct comprehensive risk assessment.
- Tool Used: Qlik AutoML, an integral component of the Qlik Cloud Platform, enabling end-to-end no-code machine learning workflows and predictive analytics.
- Benefits / Outcomes
- Accelerated Decision-Making: Empower yourself to make faster, more confident, and data-backed decisions by leveraging accurate predictions without delay.
- Democratization of AI: Bridge the gap between intricate business needs and technical capabilities, enabling non-data scientists to confidently drive AI initiatives.
- Enhanced Business Agility: Equip your organization with the ability to respond swiftly and intelligently to changing market dynamics and evolving customer demands.
- Cost-Efficiency in ML Adoption: Significantly reduce the time and resources typically required for traditional machine learning development and deployment, maximizing ROI.
- Innovation Catalyst: Foster an environment where predictive insights drive new product development, service enhancements, and optimization of operational efficiencies.
- Competitive Edge: Leverage advanced analytics to outperform competitors through superior forecasting, optimization strategies, and personalized customer experiences.
- Career Advancement in a Data-Driven World: Add a highly sought-after, future-proof skill to your professional toolkit, opening new career avenues and growth opportunities.
- Personalized Insight Generation: Gain the capability to generate targeted predictions that inform tailored strategies for various business functions, from sales to marketing to operations.
- PROS
- Unparalleled Accessibility: Opens the complex field of machine learning to anyone, irrespective of their coding or advanced statistical background.
- Rapid Time-to-Value: Enables quick model development and deployment, translating directly into faster business insights and tangible outcomes.
- Focus on Business Outcomes: Shifts the learning emphasis from technical complexities to effectively solving real-world business problems.
- Visual & Intuitive Interface: Facilitates an engaging and understandable learning experience through Qlik’s renowned user-friendly interface.
- Practical, Project-Based Learning: Reinforces theoretical concepts through hands-on application, ensuring immediate applicability of acquired skills.
- Reduced Barrier to Entry for AI: Significantly lowers the investment in specialized skills and infrastructure, making AI adoption more feasible for organizations of all sizes.
- CONS
- Platform Dependency: The skills acquired are primarily tied to the Qlik AutoML environment, which might limit direct transferability to other ML platforms requiring code or different ecosystems.
Learning Tracks: English,Development,Data Science