Fundamentals of Machine Learning for Business Professionals
|File Name:||Fundamentals of Machine Learning for Business Professionals|
|Genre / Category:||Development|
|File Size :||744 MB|
|Updated and Published:||June 5, 2023|
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Unlocking Success for Managers, Leaders, Solution Architects, Project Managers, and Engineers – No Coding Required
What you’ll learn
Developers who want to start their Machine Learning Journey
Managers and Leaders
Product Managers and Project Managers
No prior knowledge about Machine Learning, Cloud or Coding is required.
Introduction:Machine learning has revolutionized numerous industries, including business, architecture, project management, and engineering. To stay competitive in this rapidly evolving landscape, professionals in these fields must grasp the fundamentals of machine learning. The course “Fundamentals of Machine Learning for Business Professionals” is explicitly designed for managers, leaders, entrepreneurs, product managers, business analysts, solution architects, project managers, and engineers, equipping them with the essential knowledge to leverage machine learning effectively.Course Overview:The comprehensive course “Fundamentals of Machine Learning for Business Professionals” bridges the gap between business professionals and the complexities of machine learning. It empowers managers, business professionals, solution architects, project managers, and engineers alike with the necessary skills and knowledge to harness the potential of machine learning in their respective roles. The course emphasizes practical applications and real-world case studies, ensuring a holistic understanding of the subject. No coding skills or knowledge of programming (i.e. Python) is required.Key Learning Objectives:1. Introduction to Machine Learning: Gain a solid understanding of core concepts, terminologies, and algorithms used in machine learning. Explore supervised and unsupervised learning, classification, regression, clustering, and other essential techniques.2. Data Preparation and Feature Engineering: Master data preprocessing, cleaning, feature selection, and engineering. Learn how to transform raw data into a suitable format for machine learning algorithms.3. Model Development and Evaluation: Dive into model development, including algorithm selection, training, and performance evaluation. Explore standard evaluation metrics and techniques to assess model accuracy and reliability.4. Business Applications of Machine Learning: Highlight specific machine learning applications in the business domain. Explore customer segmentation, demand forecasting, fraud detection, recommendation systems, and predictive analytics using real-world case studies and industry examples.5. Ethics and Bias in Machine Learning: Understand machine learning algorithms’ ethical implications and potential biases. Explore ethical considerations in data collection, model training, and decision-making processes. Learn strategies to mitigate bias and ensure fairness in machine learning applications.6. Implementation and Deployment: Gain insights into implementing and deploying machine learning models in real-world scenarios. Topics include scalability, model deployment options, integration with existing systems, and performance monitoring and updates.The course “Fundamentals of Machine Learning for Business Professionals” unlocks the potential of machine learning for anyone who wants to learn machine learning but does not want to become a professional ML engineer. Anyone who works in a business-focused role, such as C-suit managers, product managers, project managers, solution architects, entrepreneurs and even developers, can bring themselves up to speed with machine learning and AI. Enrol now to unlock your success in machine learning for business professionals.
Who this course is for
If you want to learn about machine learning and AI in great detail but want to avoid becoming a full-blown ML engineer, then this course is for you. The course teaches ML and AI in-depth and with a focus on business applications but takes out the complexities of the Python libraries and the learning curve of programming.
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