Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. The course is packed with practical exercises that are based on real-life examples. So not only will the learners know the theory, but will also get some hands-on practice building their own models. The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

The aim of this course is to provide learners with the necessary tools for them to begin solving problems with machine learning algorithms.

At the end of the program Learners should be able to:

1. Master Machine Learning on Python
2. Make accurate predictions
3. Make robust Machine Learning models
4. Create strong added value to your business
5. Handle specific topics like Reinforcement Learning, NLP and Deep Learning
6. Build an army of powerful Machine Learning models and know-how to combine them to solve any problem
7. Make powerful analysis
8. Use Machine Learning for personal purpose

1. Analytical problem-solving skills.
2. Competence in data analysis
3. Develop applications from scratch with the help of Machine Learning algorithms that solve real-life problems.
4. Ability to interpret data using powerful Machine learning and AI algorithms.
5. Handle advanced techniques like Dimensionality Reduction

You will need a working computer.

Must have competency in using a computer and have knowledge of programming.

Intensive 2 weeks of lectures, hands-on practical and tutorials sessions.

Upon successful completion, the candidates will be awarded a certificate in Machine Learning and AI and a grade appended to the certificate. This qualification will only apply to learners who pass the final exam and complete the assignments or quizzes.

1. Data Preprocessing
2. Regression
3. Classification
4. Clustering
5. Association Rule Learning
6. Reinforcement Learning
7. Natural Language Processing
8. Deep Learning
9. Dimensionality Reduction
10. Model Selection & Boosting

1. Lecture using virtual classrooms
2. Practical hands-on online tutorials.
3. Assessments using ICT technologies
4. Zoom interactive software

1. Assignment 30%
2. Quizzes 10%
3. Milestone project 60%

Mulungushi University certificate will be provided