This intermediate-level course explores the core principles and practical applications of Artificial Intelligence (AI) and Machine Learning (ML). Designed for learners with a foundational understanding of programming and basic data science, the course builds upon fundamental AI/ML concepts to deepen theoretical knowledge and enhance practical skills using real-world datasets and modern tools.
Participants will explore supervised and unsupervised learning techniques, model evaluation and selection, neural networks, and key AI concepts such as natural language processing, computer vision, and reinforcement learning. Emphasis is placed on hands-on experience through coding exercises, projects, and case studies using Python and popular libraries such as Scikit-learn, TensorFlow, and Keras.
By the end of the course, learners will be equipped to build, evaluate, and deploy ML models and understand how to apply AI techniques in real-world scenarios across different industries.
Upon completing this course, you will be automatically enrolled in the Advanced course. Outstanding students may qualify for a scholarship waiver.
- Teacher: Kiti Admin
- Teacher: Grace Leah
Cybersecurity Intermediate Level
- Teacher: Kiti Admin
This intermediate blockchain course is designed for learners with a basic understanding of blockchain technology who want to explore its practical applications. The course covers blockchain architecture, consensus mechanisms, smart contracts, and decentralized applications (dApps). Learners will also gain insights into blockchain security, scalability, and use cases across industries. Through hands-on exercises, participants will learn how to build and deploy smart contracts and interact with blockchain networks, preparing them for real-world blockchain development and innovation.
- Teacher: Kiti Admin
- Teacher: Jerim Kaura
This intermediate course builds on foundational knowledge of Big Data and introduces learners to practical tools, architectures, and techniques used in modern data-driven systems. The course covers key components of the Big Data ecosystem, including distributed storage systems, NoSQL databases, and data warehousing concepts. Learners will explore data modeling and schema design approaches suitable for large-scale systems, as well as methods for efficient data storage and retrieval.
The course also focuses on data processing and computation using industry-standard frameworks such as Hadoop MapReduce and Apache Spark. Students will gain hands-on understanding of both batch and stream processing, real-time data pipelines using Apache Kafka and Spark, and in-memory computing for high-performance analytics. By the end of the course, learners will be able to design and evaluate scalable Big Data solutions for real-world applications.
- Teacher: Kiti Admin
This intermediate-level course on the Internet of Things (IoT) is designed for learners who have a basic understanding of IoT concepts and want to deepen their knowledge. The course covers IoT architectures, communication protocols, data management, and security considerations, while also exploring practical applications across industries. Learners will gain hands-on experience with IoT devices, sensors, and cloud platforms, preparing them to design and implement real-world IoT solutions.
- Teacher: Kiti Admin
- Teacher: Paul Kamau
- Teacher: Paul Rabala