Essential Data Science Building Blocks (Modular Course)

November 29, 2024

For whom is this course?

The course will familiarize the learners with all essential skill sets required to be a good data scientist and machine learning engineer with real-world practical case studies regardless of their educational and professional background. The course is designed with independent modules starting from basic Python, Exploratory Data Analysis, Data Visualization, Machine Learning, Deep Learning, Computer Visions, and Natural Language Processing.

Objective

The objective is to provide a foundational course to get started with Data Science and Machine Learning Engineering with real-world projects and hands-on experiences. The essential skills are almost from all domains of data sciences including Machine Learning, Natural Language Processing, and Computer Vision.

What will you learn?

After completing this course learners will be able to:

Learn and apply Machine learning techniques with real-world applications
Learn and apply Natural Language processing techniques with real-world applications
Learn and apply Computer Vision techniques with real-world applications
Developing a web application with Streamlit
Working with GitHub to maintain Data Science Portfolio

Prerequisites

None

Syllabus

Sr.#	Module	Duration	Instructor
1	Basic Python Libraries for Data Science Applications	4 Hours	Erum Afzal
2	EDA: Exploratory Data Analysis and Feature Engineering	4 Hours	Erum Afzal
3	Data Visualization for Data Science Projects	2 Hours	Erum Afzal
4	Machine Learning Algorithms Application	3 Hours	Mohammed Zuhair
5	Getting started with Natural Language Processing and Language Models	4 Hours	Mohammed Zuhair
6	Deep Learning	2 Hours	Opeyemi
7	Neural Network Application	4 Hours	Likhitha pulluru
8	Computer Vision Application	3 Hours	Amal Mathew
9	Transfer Learning Application	3 Hours	Sijuade
10	Streamlit Application	3 Hours	Aruna
11	Working with GitHub	2 Hours	Rosana
12	Developing and Publishing Data Science Package	3 Hours	Juber Rehman