Artificial Intelligence Application Developer (AAD)

Develops AI-powered applications and systems that can perform tasks typically requiring human intelligence, such as visual perception and decision-making.

Course Fee

30,000

Duration

1 Years (450 Hours)

Eligibility

Completed 1st year of UG

Pursuing 1st year of UG (with continuous education)

Pursuing 3rd year of 3-year diploma after 10th (with continuous education)

Completed 3-year diploma after 10th

Completed 2nd year of 2-year diploma after 12th

Pursuing 2nd year of 2-year diploma after 12th (with continuous education)

NSQF Level

4.0

National Skill Qualification Framework

Credentials

Microsoft AI Engineer
Google Professional ML Engineer
AI Application Developer Certification
Machine Learning Specialist
Deep Learning Practitioner

Available Batch Timings

Morning: 8:00 AM - 10:00 AM

Morning: 9:00 AM - 11:00 AM

Morning: 10:00 AM - 12:00 PM

Morning: 11:00 AM - 1:00 PM

Evening: 12:00 PM - 2:00 PM

Evening: 1:00 PM - 3:00 PM

Evening: 2:00 PM - 4:00 PM

Evening: 3:00 PM - 5:00 PM

Evening: 4:00 PM - 6:00 PM

Frequently Asked Questions

Programming with Python

Installing and configuring programming environment for python
Writing basic programs and understanding data types, operators, looping constructs, functions
Exploring various data structures
Learn to work on modules and packages

Conceptualizing Data Science with Python

Concept of Data Science and tools used
Pre-Processing Concepts in Data Science
Introduction to Numpy and Working on N-d arrays
Learning Analysis on Numpy
Exploring Image handling using Numpy

Data Analysis and Visualization

Introduction to Pandas
Exploring Data Frames and Series
Learning EDA and Data Analysis
Performing Analysis on datasets
Introduction to Visualization and Learning Tools for making Graphs and plots
Exploring analysis through visualization

Fundamentals of Machine Learning

Introduction to Machine Learning
Learning various ML categories
Learning to build models on datasets
Exploring the outputs of models

Performance and Accuracy of Machine Learning Models

Implement Predictive Analysis using various Regression and Classification algorithms
Learn and apply statistics used in Machine Learning
Using various metrics and Feature Engineering techniques
Develop and Implement Project in Predictive Analysis using ML
Working on tools for getting interactive insights from data - Power BI desktop, Tableau Public

Artificial Intelligence Application Developer (AAD)

Eligibility

NSQF Level

Credentials

Available Batch Timings

Frequently Asked Questions

Hartron Study Group

Quick Links

Address

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