
Introducing
MACHINE LEARNING AND AI
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.
Machine Learning and AI
Course Details
Background
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.
Rationale
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.
Code
MLA111Fees
K3,500 Per PersonPayment Plan Available with Initial Deposit of K2000 to Enroll in the Course.
Location / Learning Mode
OnlineContact
Coordinator: Mr L SimukondaDates
Intakes
Intake |
Start Date |
End Date |
Time |
Group 1 |
31st January 2022 |
11th February 2022 |
18 – 20 |
Group 2 |
14th February 2022 |
25th February 2022 |
18 – 20 |
Group 3 |
28th February 2022 |
11th March 2022 |
18 – 20 |
Group 4 |
14th March 2022 |
25th March 2022 |
18 – 20 |
Group 5 |
28th March 2022 |
8th April 2022 |
18 – 20 |
Group 6 |
11th April 2022 |
22nd April 2022 |
18 – 20 |
|
|
||
Group 6 to 10 |
Full Schedule To be Announced in April |
Aim
The aim of this course is to provide learners with the necessary tools for them to begin solving problems with machine learning algorithms.
Objectives
At the end of the program Learners should be able to:
- Master Machine Learning on Python
- Make accurate predictions
- Make robust Machine Learning models
- Create strong added value to your business
- Handle specific topics like Reinforcement Learning, NLP and Deep Learning
- Build an army of powerful Machine Learning models and know-how to combine them to solve any problem
- Make powerful analysis
- Use Machine Learning for personal purpose
Competencies
- Analytical problem-solving skills.
- Competence in data analysis
- Develop applications from scratch with the help of Machine Learning algorithms that solve real-life problems.
- Ability to interpret data using powerful Machine learning and AI algorithms.
- Handle advanced techniques like Dimensionality Reduction
Entry requirements
You will need a working computer.
Expected prior knowledge
Must have competency in using a computer and have knowledge of programming.
COURSE DELIVERY.
Intensive 2 weeks of lectures, hands-on practical and tutorials sessions.
QUALIFICATION
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.
Course Content
- Data Preprocessing
- Regression
- Classification
- Clustering
- Association Rule Learning
- Reinforcement Learning
- Natural Language Processing
- Deep Learning
- Dimensionality Reduction
- Model Selection & Boosting
Lesson Schedule
Day |
Lesson/activity |
Responsible/Lecturer |
Day 1 |
Data Preprocessing |
Mr L Simukonda |
Day 2 |
Regression |
Mr L Simukonda |
Day 3 |
Classification |
Mr L Simukonda |
Day 4 |
Clustering |
Mr L Simukonda |
Day 5 |
Association Rule Learning |
Mr L Simukonda |
Day 6 |
Reinforcement Learning |
Mr L Simukonda |
Day 7 |
Natural Language Processing |
Mr L Simukonda |
Day 8 |
Deep Learning |
Mr L Simukonda |
Day 9 |
Dimensionality Reduction |
Mr L Simukonda |
Day 10 |
Model Selection & Boosting |
Mr L Simukonda |
Day 11-14 |
Personal Projects and Final Exam |
Mr L Simukonda |
Teaching Methods
- Lecture using virtual classrooms
- Practical hands-on online tutorials.
- Assessments using ICT technologies
- Zoom interactive software
Timing and schedules
Tentatively from 18 hours to 20 hours
Assessment Method
- Assignment 30%
- Quizzes 10%
- Milestone project 60%
Certifications
Mulungushi University certificate will be provided