Supervised Machine Learning
Improve productivity at work with automated systems and software powered by AI and machine learning.
Enrol in Introduction to Machine Learning with CADS to embark on your journey in building intelligent applications.
Best suited for business and IT professionals involved in building smart and intelligent applications.
Duration:
5 days
Make wonders with machine learning.
This module will enable professionals to develop and train data models to uncover underlying patterns in data.
Algorithms to analyze patterns.
Machine learning describes the process of identifying underlying patterns in data. This module aims to give you a thorough understanding of the relevant algorithms to use when labelled training data is available.
Evaluate the model’s performance.
After completion, you will be able to train and tune models on data and robustly evaluate their performances. These skills will allow analysts to identify patterns in data and gain new insights not possible with conventional statistical methods.
Future prediction is possible.
Most suited for analysts, data scientists, and business intelligence professionals who want to build machine learning models to make predictions using their own data.
Learning Outcome
Upon completion, participants should be able to demonstrate each of the following;
- Understand the landscape of machine learning possibilities.
- Ability to train a model using supervised learning algorithms.
- Ability to train and evaluate a model using training and/or test data.
Learning Path
Supervised Machine Learning is one of the modules under the CADS Enterprise Data Scientist (EDS) Programme. EDS is a 23-26 days training program that super-charges Business Data Scientists with new skills to analyze and communicate insights effectively.
CADS Certification
Each exam in this program certifies job-ready knowledge and skill. Those that pass all are recognized as being able to distil insight from data and communicate its value to a decision-maker. Enter the world of Data Professionals.
