Taught by industry professionals with many years of experience in the field
It contains numerous examples right from the first lecture until the end.
Every concept is explained using a business scenario or case study.
It offers the right mix of theory and hands-on labs.
The course simplifies complex statistical concepts.
The course offers Python code and a sufficient number of data sets.
The course is self-sufficient. You do not need any other resource or reference to grasp the concepts.
Target Audience for this Course
Everybody who wants to get started with machine learning & deep learning
Reporting analysts who aim to become data scientists.
Predictive modeling profile candidates who want to learn ML and DL
Data visualization experts
Any Data science aspirants
Graduates and undergraduate students
Computer Science Engineering students
There is no strict pre-requisite
Anyone with a primary degree can get started with this course
Basic mathematical skills are sufficient
Statistical Knowledge is NOT a prerequisite. It will be taught in class.
Cutting-edge programming knowledge is NOT a pre-requisite. Coding will be taught in the class.
Windows 10 with 8 GB RAM.
Proxy-free internet, Admin rights to execute scripts
Firefox and Chrome browsers
Course Delivery Plan
The training has four major phases in each concept discussion:
Theory, Demo, in-class exercise, and project assignment.
The algorithm theory will be explained first. The instructor will take a dataset and demonstrate the concept on a dataset. In the third phase, participants will try the code on a new dataset. In the fourth phase, a real-time dataset will be considered and floated as a project.