ITI Artificial Intelligence Programming Assistant Syllabus: Course Structure, Detailed Syllabus

Safe working practices

• Visit the IT Lab of the institutes and locate the power supply to the computer and its peripherals.
• Safety symbols and hazard identification.
• Practice safe methods of fire fighting in case of electrical fire.
• Use of fire extinguishers.

Computer Components

• Demonstrate various parts of the computer system such as motherboard, RAM, CPU, serial and parallel ports.
• Power on the start button of a computer system and check the power supply to the computer and all the connected peripherals of the system.

Using Operating System and Software Installation

• Practice the Windows interface using the start menu, taskbar, title bar, Windows help, My Computer, and Recycle Bin.
• Customize the desktop settings and manage user accounts.
• View system properties and customize the same using control panel details.
• Work with keyboard shortcut commands.
• Create, delete, copy, move, rename, view, sort, zip, and unzip files & folders.
• Use Multimedia tools and Windows media player for image, audio, and video.
• View the BIOS settings and their modifications.
• Install Windows operating system.
• Format the hard disk and create a partition.
• Install necessary application software for Windows i.e. Office Package, PDF Reader, Media Player, Antivirus, etc.
• Install Drivers for printer, scanner, webcam DVD, etc.
• Dongle and Pen drive and SSD handling, range extenders
• Remote Desktop accessibility and handling (Any Desk, team viewer)
• Install Photoshop, Maya and CANVA Fire Fly, AI, Paint Brush

Using MS Office/Google WorkSpace

• Open MS Word/ Google Workspace, familiarize yourself with basic Word components and practice creating, saving, closing, and renaming Word documents.
• Edit the document using basic formatting tools.
• Practice Inserting and formatting tables, pictures, videos, and other objects.
• Use the Mail merge tool. Use conditional Mail Merge, External Data Source. Practice Letters, Labels & envelope printing using Mail Merge.
• Open MS Excel, familiarize yourself with basic application components and practice creating, saving, and formatting Excel spreadsheets.
• Practice Excel functions of all major categories i.e., Financial, Logical, Text, date & time, Lookup, Math, Statistical, etc.
• Use various data types in Excel, sorting, filtering, and validating data, PIVOT Tables.
• Modify the Excel page setup and print a worksheet.
• Open PowerPoint presentation, familiarize yourself with basic application components and practice creating Slide shows and inserting pictures and themes.
• Add new slides, format text, and link with Word and Excel documents.

Programming Language (Python) Use Python from the command line

• Install, set up the environment & run Python.
• Use Command Line and IDE to create and execute a Python program.
• Practice debugging using IDE.
• Perform Operations using Data Types and Operators
• Write and test a Python program to demonstrate print statements, comments, and different types of variables.
• Write and test a Python program to perform data and data type operations, string operations, date, input and output, output formatting, and operators.
• Determine the sequence of execution based on operator precedence. Control Flow with Decisions and Loops.
• Construct and analyze code segments that use branching statements.
• Construct and analyze code segments that perform iteration.
• Write a program using single-dimensional arrays.
• Write a program using multi-dimensional arrays/ matrices. Document and Structure Code.
• Document code segments using comments and documentation strings.
• Construct and analyze code segments that include List comprehensions, tuple, set, and Dictionary comprehensions.
• Perform Operations Using Modules and Tools
• Perform basic operations using built-in modules.
• Solve complex computing problems by using built-in modules.

• Perform basic operations using functions.
• Perform basic programming using the Object Oriented Programming concept.
• Construct and analyze code segments for file handling, folder and date, time handling, and exception handling.
• Construct code segments for interacting with web requests, web scraping, and web crawling.
• Demonstrate Installation of MySQl.
• Troubleshooting basic installation issues.
• Creation and use of the database.
• Designing of tables.
• Applying data integrity rules.
• Using the DDL, DCL, and DML statements.
• Enforcing constraints, primary key and foreign key.
• Adding indices to Tables
• Simple select queries.
• Insert and delete queries Update queries
• Demonstrate Using the Number, Date, and Character functions.
• Joins, Group by, Having, Sub query.
• Indexing and Optimizing Query.

• Working with the following: (python/pandas / matplotlib)
• Identify structured data & unstructured data, data integration, data cleaning
• Working on data visualization.
• Working with the following: Data representation &Cluster Analysis.
• Pre-processing the data.
• Segregating pre-processed data into different patterns.
• Trend Analysis.
• Statistical approach with Regression Analysis, Correlation Analysis, Cluster Analysis.

• Machine Learning with Statistics 70. Identify the Training Set and Test set with real-life examples.
• Calculate mean, median, and mode.
• Calculating measures of central tendency, population variance, sample variance, and standard deviation.
• Calculate Skewness and Kurtosis in a graph.
• Solving problems on Clustering.
• Solving problems on Model Selection.
• Solving problems on Classification.
• Solving problems on the ChiSquare test.
• Solving problems on t-Test.
• Solving problems on Correlation analysis.
• Solving problems related to Inferential statistics.

• Use Python / Tensorflow / Keras / Numpy / PyTorch.
• Implementation and training of Neural Networks.
• Working with Perception, activation function, and derivatives.
• Perform Forward and Backward Propagation.
• Working with computing loss, gradient descent, regularization, Optimization, Shallow, and deep neural network.
• Identify and understand the components of Deep Learning with pre-trained models.

• Practice NLPK & Open NLP
• Interpret and practice Natural Language features – basic NLP requests, specifying text content.
• Part of basic analysis and Chunking.
• Practice syntactic analysis – analyzing syntax in a string, from the cloud, syntactic analysis requests, and responses.
• Perform sentiment analysis – analyzing sentiment in a string, from a cloud storage, sentiment analysis response fields, interpreting sentiment analysis values.
• Plan and perform entity analysis – analyzing entities in a string, from cloud, entity analysis response fields.
• Execute entity sentiment analysis – entity sentiment analysis requests and responses.
• Practice Content Document classification and spam filtering classification – Practice sequence-to-sequence operations such as machine translation.

• Introduction to Computer Vision
• OpenCV-Python
• Perform environmental setup.
• Read and write an image.
• Resize and rotate an image.
• Working with image threshold, image filtering, edge detection, image features and alignment, image stitching and creating panoramas, high dynamic range imaging (HDR), histogram, and histogram using Matplotlib.
• Practice with color spaces, Morphological transformations, erosion, dilation, and image contours – find contours and draw contours.
• Work with template matching, image pyramids, image addition, image blending with pyramids, and Fourier transform.
• Capture video from a camera, play video from a file, extract images from video, video from images, face detection, mean-shift, and camshaft.
• Working with object tracking and detection, pose estimation, feature detection, feature matching, digit recognition with KNN, Working with pre-trained Vision models.

• Working with Generative AI Tools and models to perform NLP and Computer Vision activities.
• Creating a simple chatbot using any Bot Service.
• Testing and refining the chatbot.
• Working with pre-trained age language models.
• Generating Images using large image models.

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1.

Professional Skill (Trade Practical)

840

2.

Professional Knowledge (Trade Theory)

240

3.

Employability Skills

120

Total

1200