Artificial Intelligence Application Developer
| 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
At Hartron we provide Artificial intelligence course which is one of the most powerful technologies driving innovation today across the world. From the chatbots to the recommendation system to the advanced system which is related to healthcare, finance, technology and self-driving cars, AI is changing the world and how businesses and societies operate. Get your Artificial Intelligence development course in Chandigarh with us and find out more relevant details step by step about how it will increase the practical learning inside you.
To prepare students and professionals for this future-ready career path, we offer the Artificial Intelligence Application Development Program.
This 12 month, 540 hour programme is designed to build expertise in Python programming, data science, machine learning and deep learning while also developing employability skills to ensure students are job-ready.
With our strong mix of classroom learning, project-based training, and lab sessions, you are able to learn how to develop such advanced technology that can help the world to solve every problem. You will learn python programming, data science, machine learning and deep learning while also developing employability skills to ensure students are job-ready.
Program Overview
- This Artificial intelligence application development course will be completed in around 12 months, or we can say 540 hours of duration. The modes of learning are classroom offline training, practical labs and OJT projects.
- The curriculum has been carefully structured to cover everything from the basics of programming to advanced machine learning and deep learning concepts. By the end of the programme, students will have the ability to conceptualise, build and deploy AI applications, which are very important these days for learning.
Course Modules
- 1 Programming with Python (60 Hours | 2 NOS Credits | Level 4.5)
- 2. Conceptualising Data Science with Python (60 Hours | 2 NOS Credits | Level 4.5)
- 3. Data Analysis and Visualization (90 Hours | 3 NOS Credits | Level 4.5)
- 4. Fundamentals of Machine Learning (30 Hours | 1 NOS Credit | Level 4.5)
- 5. Performance and Accuracy of Machine Learning Models (90 Hours | 3 NOS Credits | Level 4.5)
- 6. Fundamentals of Deep Learning (60 Hours | 2 NOS Credits | Level 4.5)
- 7. Employability Skills (60 Hours | 2 NOS Credits | Level 4.5)
- 8. OJT/Project (90 Hours | 3 NOS Credits | Level 4.5)
- The above-given modules will make you efficient to develop AI based applications.
Eligibility Criteria
The eligibility criteria are very simple, as we have already shown above. The student should be:-
- - Completed/Pursuing 1st Year of B.Tech / BCA / B.Sc
- - Pursuing/Completed 3 Years of Diploma after 10th
- - Pursuing/Completed 2nd Year of Diploma after 12th
- This makes the program accessible to students with different types of academic backgrounds who are interested in our AI development course in Chandigarh by Hartron.
Why Choose Us?
- Because we have years of experience in the field of technology and coding. Hands-on real-world projects, lab work and practical experience. We give in-depth practical knowledge about the application development that can help to improve the overall high definition and efficient application like never before.