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.

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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.

1

Code

MLA111
2

Fees

K3,500 Per Person
Groups of more than 5 persons K3,000 per person

Payment Plan Available with Initial Deposit of K2000 to Enroll in the Course.
Balance to be paid within 2 weeks.
3

Location / Learning Mode

Online
4

Contact

Coordinator: Mr L Simukonda
Email: lsimukonda@mu.ac.zm
5

Dates

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:

  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

Competencies

  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

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

  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

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

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

Timing and schedules

Time: Most Lectures will be conducted in the evenings.
Tentatively from 18 hours to 20 hours

Assessment Method

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

Certifications

Mulungushi University certificate will be provided

Connect with us

26 km from Kabwe Town along Great North Rd, Kabwe, Central Province, Zambia

  • dummy+(260) 215 228 004

  • dummy academic@mu.ac.zm

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