Introductory Machine Learning Theory and Practice

Introductory Machine Learning Theory and Practice

Machine Learning has become one of the most important tools in industry and academia. Its uses range from simple business analytics to economic forecasting to even image similarity and facial recognition, among others. This tool uses several techniques and concepts, from programming and statistics to linear algebra and real analysis.

M&S Research Hub organizes full and comprehensive online/onsite structured training for researchers, programmers, and data scientists at all proficiency levels to acquire detailed knowledge and become fully capable of using these tools in their research and programming projects.

Training Calendar

May  / - Normal Group (7-10 participants)                                  

Aug  / - Normal Group (7-10 participants)                                  




It was excellent training for me. I think Dr. Arhsian is the best in his field. He is a well-qualified person. I will recommend this institute for all my colleagues if they need it

Ferhat Citak 

Source - Home Page


A good platform to learn data analysis.

Mahdi Hassan

Source - Facebook Page


After finishing the first module, I decided to to do all modules because the first one has opened my mind, because here in Zambia there is no university or college where they offer as full program they way you offer it, it only comes as a course to those who are doing economics.


Source - Home Page

Learning by "Researching" TM

Live Training


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Video 1: Neural networks / Machine learning 

(5 Minutes)

Machine Learning Training Program

A fully-fledged intensive training on the fundamentals of Machine Learning and Programming Coding for econometric modeling and data analysis using Julia programming language.

Duration: Approx. 12 Hours

Training Mode: Normal group (7-10 trainees), small group (2-5 trainees), and one-to-one

Platform: online (Zoom)

Extra benefits:

  • Certified certificate after completing the training and 10% permanent discount on all events, workshops, and webinars.
  • Trainees become eligible to submit their papers at MSR working paper series (SSRN and RePEc indexed).

Training content is systematic and structured as follows:

1. Introduction to Julia

- Exposition on data structures, statistics and linear algebra, solving system of equations, optimization, and automatic differentiation

2. Implementing Gradient Descent

- Theory behind Gradient Descent optimization, implementing Ordinary Least Squares and Maximum Likelihood Estimation

3. Artificial Neural Networks (ANNs)

- Mathematics of ANNs, implementing one in Julia, and performing two examples: detecting numbers in images, and performing a regression

4. Machine Learning Algorithms

- Bootstrapping, Bootstrap Aggregation, Gradient Boosting