Data Science Course
Description
This is a project-based course designed for trainees with some programming background interested to learn machine learning techniques and predictive analytics. As a programming language, we will Python to develop the machine learning algorithms.
COURSE OBJECTIVES
On successful completion of this course, students will be able to:
- Understand and describe the essential concepts of AI and Machine Learning
- Identify and implement appropriate Machine Learning techniques to build and validate the predictive models
- Clean the datasets and prepare them for machine Learning projects
- Understand and implement the supervised and un-supervised machine learning algorithms
- Practically design and run the Machine Learning projects in Python
COURSE SYLLABUS
- Introduction to Data Science: Concepts and Applications
- Fundamentals of Machine Learning
- Machine Learning pipeline
- Train and Test Datasets
- Data preprocessing, Cleaning, and visualization
- Supervised and unsupervised algorithms
- Building and validating the Regression models
- Building and validating the Decision Trees
- Building Clustering and Classification Models
- Final Project: Applying Machine Learning techniques to a real dataset and building the predictive models