| S. No. | NOS/Module Name | Topics | Duration (Hours) Theory / Lab |
Learning Outcomes |
|---|---|---|---|---|
| 1 | Programming with Python |
Installing and configuring programming environment for Python Writing basic programs and understanding datatypes, operators, looping constructs, functions Exploring various data structures Learn to work on modules and packages |
20 / 40 |
Students will be able to install and configure the Python IDE and work on collaborative cloud interface required for programming. Students will understand the basics of Python language and recognize Python syntax. Students will write programs in Python, compile, debug, and handle exceptions. Students will understand top-down and bottom-up approaches using functions. Students will understand modular programming by defining and calling functions. Students will explain various ways of passing parameters to functions and their differences. Students will learn to use different data structures to organize and store data. Students will apply algorithms to process data in meaningful ways. Students will learn efficient coding practices for reliability and performance. |
| 2 | 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-dimensional arrays Learning analysis on Numpy Exploring image handling using Numpy |
24 / 36 |
Students will understand Data Science concepts using mathematical and statistical formulas. Students will convert raw/unstructured data into meaningful insights. Students will explore how data can be used as an asset to improve revenue and customer experience. Students will learn about tools for processing large data volumes. Students will pre-process data for analysis. Students will work on Numpy library for mathematical operations on arrays. Students will optimize code using Numpy. Students will apply Numpy in Data Science and Data Analysis. Students will manipulate images stored in Numpy arrays. |
| 3 | 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 graphs and plots Exploring analysis through visualization |
34 / 56 |
Students will recognize Python library Pandas for Data Analysis/Data Science tasks. Students will differentiate between Numpy and Pandas. Students will analyze Big Data and make conclusions based on statistics. Students will clean messy datasets and make them readable using Pandas. Students will filter, merge, and segment datasets. Students will use visualization techniques to understand business problems. Students will use Matplotlib and Seaborn for creating advanced plots. Students will create graphs and visualize data trends and outliers. Students will demonstrate preprocessing and analysis through case studies. |
| 4 | Fundamentals of Machine Learning |
Introduction to Machine Learning Learning various ML categories Building models on datasets |
12 / 18 |
Students will understand concepts of Machine Learning and its applications. Students will differentiate between supervised, unsupervised, and reinforcement learning. Students will explore ML paths such as Computer Vision, Predictive Analysis, and NLP. Students will implement models using classification and regression algorithms. Students will understand the complete AI project cycle. Students will practice ML models with Scikit-learn. Students will experiment with datasets to understand model performance. |
| 5 | Performance and Accuracy of Machine Learning Models |
Implement predictive analysis using regression and classification algorithms Apply statistics in ML (correlation, hypothesis, distributions, etc.) Use metrics and feature engineering techniques Develop predictive analysis project |
35 / 55 |
Students will make predictions using regression and classification algorithms. Students will analyze historical data to identify patterns and trends. Students will apply statistical methods to improve ML models. Students will evaluate models using metrics for classification and regression. Students will enhance models using feature selection and engineering. Students will develop predictive projects across domains. Students will improve model accuracy with data manipulation techniques. |
| 6 | Fundamentals of Deep Learning |
Understand and implement deep learning with neural networks Work with Computer Vision using CNN and image-based models Understand and implement NLP algorithms |
25 / 35 |
Students will understand neural networks and deep learning architecture. Students will explore ANN activation functions and layers. Students will understand Convolutional Neural Networks (CNN). Students will apply CNN for image recognition tasks. Students will implement projects on classification using CNN. Students will learn NLP concepts using NLTK library. Students will apply feature engineering and sentiment analysis in NLP. Students will build models like spam detectors and sentiment analyzers. |
| Sub Total = 390 Hours (150 Theory / 240 Lab) | ||||
| 7 | Employability Skills | – | 60 | Students will gain additional professional and communication skills required for employment. |
| 8 | OJT / Project | – | 90 | Students will work on real-time projects and learn workplace readiness. |
| Total Duration = 540 Hours | ||||
Artificial Intelligence Application Developer
Artificial Intelligence (AI) has become one of the most powerful technologies transforming industries across the world. From intelligent chatbots and recommendation systems to healthcare, finance, automation and self-driving vehicles, AI is creating endless career opportunities for skilled professionals.
HARTRON Advanced Skill Centre offers an industry-focused Artificial Intelligence Application Developer programme designed to equip students with practical knowledge in Python programming, Data Science, Machine Learning and Deep Learning. The curriculum combines classroom learning, practical labs and real-world projects to prepare learners for today's AI-driven industries.
12 Months
Course Duration
540 Hours
Practical Learning
Level 4.5
NSQF Aligned
Offline + Labs
Learning Mode
Program Overview
The Artificial Intelligence Application Developer programme is a comprehensive 12-month (540 Hours) training programme designed to develop practical expertise in Artificial Intelligence technologies.
The curriculum begins with Python programming and gradually advances towards Data Science, Machine Learning and Deep Learning. Every module is supported with practical lab sessions, real-world assignments and project-based learning to help students confidently build AI-powered applications.
Students gain practical exposure through classroom training, hands-on laboratories and industry-oriented OJT projects, ensuring they are fully prepared for professional careers in Artificial Intelligence Application Development.
Learning Includes
- ✔ Python Programming
- ✔ Data Science
- ✔ Machine Learning
- ✔ Deep Learning
- ✔ Practical Lab Sessions
- ✔ Industry Projects
- ✔ OJT Training
- ✔ Employability Skills
Course Modules
The Artificial Intelligence Application Developer programme is divided into carefully structured modules that help learners build practical expertise from Python programming to Deep Learning and real-world AI application development.
Programming with Python
Build a strong programming foundation using Python for AI and automation.
Conceptualising Data Science with Python
Learn data manipulation, processing and analytics using Python libraries.
Data Analysis & Visualization
Analyse data and create interactive visual reports using modern tools.
Fundamentals of Machine Learning
Learn supervised, unsupervised learning and predictive AI models.
Performance & Accuracy of ML Models
Improve AI model accuracy through optimisation and evaluation techniques.
Fundamentals of Deep Learning
Learn Neural Networks, TensorFlow, CNNs and Deep Learning concepts.
Employability Skills
Develop communication, teamwork and interview skills for career success.
OJT / Industry Project
Gain practical industry exposure through live AI projects and OJT.
Who Can Join This Programme?
Our Artificial Intelligence Application Developer programme is specially designed for students who want to build a successful career in Artificial Intelligence, Machine Learning and Application Development.
The programme welcomes learners from different educational backgrounds and provides practical, industry-oriented training to help them become job-ready AI professionals.
B.Tech / BCA / B.Sc
Students pursuing or completed First Year.
3-Year Diploma
Pursuing or completed after 10th Standard.
2-Year Diploma
Pursuing or completed after 12th Standard.
Career Aspirants
Anyone passionate about AI, Programming and Technology.
Start Your AI Journey Today
HARTRON Advanced Skill Centre welcomes students from different educational backgrounds. If you're passionate about Artificial Intelligence and modern technologies, this programme provides the right platform to build industry-ready skills through practical learning and real-world projects.
Why Choose HARTRON Advanced Skill Centre?
Learn Artificial Intelligence from experienced industry professionals through practical training, live projects and government-recognised certification.
Experienced Faculty
Learn from highly experienced trainers with real industry knowledge in Artificial Intelligence, Machine Learning and Application Development.
Practical Lab Sessions
Every concept is supported with practical lab sessions, coding exercises and real-world implementation.
Industry Projects
Work on AI-based projects to gain practical experience and build a strong professional portfolio.
Placement Assistance
Resume building, interview preparation and career guidance to help students become industry ready.
Government Recognised
Government-approved training programmes aligned with NSQF standards for quality education.
Future Ready Skills
Master Artificial Intelligence, Machine Learning and Deep Learning with the latest industry curriculum.
Ready to Build Your Career in Artificial Intelligence?
Join HARTRON Advanced Skill Centre and gain practical experience through classroom learning, industry projects, practical labs and expert mentorship.
