40 Hours 
TDXBI-100 Course outline
In this course we will learn the basics of artificial intelligence and machine learning. We will get to know and apply the development cycle of data science Including information gathering, information analysis, information preparation, model building, training, analysis of results. We will learn the leading algorithms in Machine Learning - and we will understand when to use each algorithm and different control processes.
By completing this course, participants will Get familiar with machine learning concepts and trends and understand the E2E process and challenges in machine learning projects.
Upcoming meetings
There are no upcoming meetings for this course. Contact us to schedule this course, which will be customized specifically for your organization.
info@hackerupro.comModules
- Introduction to ML
- Bias-Variance tradeoff
- Types of ML
- CRISP-DM methodology - the typical work cycle of the Machine Learning project
- Managing Data science projects
- Methods for evaluation
- Monitoring ML models
- Data representation types: Scatters, pie, column diagram
- Cleaning the data
- Data completion, Data normalization and scaling
- Feature selection: forward and backward selection
- Feature extraction
- Balancing the data
- Supervised learning algorithms (Random Forest, Knn etc.)
- Unsupervised learning algorithms
Prerequisites
- 01 Basic knowledge of the Microsoft Windows operating system and its core functionality
- 02 Working knowledge of relational databases
