Professional Advanced
Programs.
Short courses, workshops and certificate programs designed for working professionals, lecturers, and lifelong learners. Each program is led by industry experts or senior UTE faculty.
2 programs available.
7. Internet of Things with NodeMCU
Course Description The Internet of Things (IoT) with NodeMCU course introduces learners to the fundamentals of IoT systems using the NodeMCU (ESP8266/ESP32) microcontroller. Participants will learn how to connect sensors and devices to the internet, collect and transmit data, and build simple IoT applications. The course emphasizes hands-on experiments, real-world IoT use cases, and basic networking concepts, making it ideal for beginners in IoT and embedded systems.
3. Introduction to AI and Machine Learning with Python
Introduction to AI and Machine Learning with Python 1. Course Description The Introduction to AI and Machine Learning with Python course provides learners with a foundational understanding of artificial intelligence and machine learning concepts using Python. Participants will explore how machines learn from data, understand common algorithms, and build simple AI-driven models. The course emphasizes practical implementation using Python libraries, real-world examples, and hands-on exercises, making it suitable for beginners who want to enter the field of AI and machine learning. 2. Course Outlines Chapter 1: Introduction to Artificial Intelligence • Definition and History of Artificial Intelligence • Types of AI: Narrow AI vs. General AI • Real-World Applications of AI Chapter 2: Fundamentals of Machine Learning • What is Machine Learning? • Types of Machine Learning (Supervised, Unsupervised, Reinforcement) • Machine Learning Workflow Chapter 3: Python Tools for AI and Machine Learning • Overview of NumPy, Pandas, and Matplotlib • Introduction to Scikit-Learn • Data Preparation for Machine Learning Chapter 4: Supervised Learning Techniques • Linear Regression • Classification Algorithms (Logistic Regression, K-Nearest Neighbors) • Model Training and Evaluation Chapter 5: Unsupervised Learning Techniques • Clustering Concepts • K-Means Clustering 3. Who Should Join • Students interested in AI • Beginners in machine learning • Data professionals exploring AI
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