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)
2023
REVIEWS
EXPERIENCED TRAINERS
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
COMPLETE GUIDE TO DATA ANALYTICS
A good platform to learn data analysis.
Mahdi Hassan
Source - Facebook Page
FULLY-FLEDGED TRAINING
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.
George
Source - Home Page
Learning by "Researching" TM
Live Training
Snapshots
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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
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