Machine Learning Training

Machine Learning Training


Machine learning is the science (and art) of getting computers to act without being explicitly programmed. In recent years, machine learning has been the most prevalent tool that has allowed us to be able to design self-driving cars, practical speech recognition, effective web search, predict protein unfolding, and vastly improve understanding of the human genome. In this machine learning training, you will learn about the most effective machine learning techniques, and gain practice tinkering and implementing them yourself. More importantly, not only you’ll learn about the theoretical foundation of learning, but also gain the practical hands-on experience needed to quickly and powerfully apply these techniques to new real-world problems.



To introduce students to the basic concepts and tools of Machine Learning

To develop skills of using recent machine learning software for solving practical problems

To use appropriate models for the given data

To understand how to evaluate models generated from data



By the end of the course/ training, the participants will be able to:

Understand the machine learning process; from problem definition to model creation to its deployment

Data handling with Pandas library in Python (handling missing data, handling categorical data, splitting data into train/ test set, and more)

Discover insights with Matplotlib data visualization

Implement various supervised and unsupervised machine learning models to problems

Assess performance of different models

Implement and deploy computer vision model in production


Prerequisite for joining course

Interest to learn and implement machine learning systems (essential)

Intro level Python (essential)

Good math foundation (optional)



Introduction to Machine Learning Systems

High level introduction to the machine learning world


Data manipulation with Pandas

Data visualization with Matplotlib

Data evaluation and feature selection

Model (With case study: real world examples and data)

Problem definition

Model creation



Classical machine learning algorithms

Neural networks

Loss function

Evaluation metric




Model evaluation and tuning

K-fold cross validation

Selecting performance evaluation metrics

Hyperparameter tuning


Model conversion

Model deployment



6 weeks times

2 sessions per week

2 hours per session

Time & Location: 18:00 – 20:00 @ UBT

Total hours: 24