AI Maker | NobleProg HR

Course Code: aimakerbspk

Duration: 10 hours

Overview:

By the end of this training, participants will be able to:

Identify and appreciate Artificial Intelligence and its applications in daily life.
Relate, apply and reflect on the Human-Machine Interactions.
Identify and interact with the three domains of AI: Data, Computer Vision and Natural Language Processing.
Undergo an assessment for analysing progress towards acquired AI-Readiness skills.
Relate to latest applications of Artificial Intelligence.
Understand the impact of Artificial Intelligence on Sustainable Development Goals to develop responsible citizenship.
Research and develop awareness of skills required for jobs of the future.
Imagine, examine and reflect on skills required for futuristic opportunities.
Develop effective communication and collaborative work skills.
Understand and reflect on the ethical issues around AI.
Gain awareness around AI bias and AI access.
Identify the AI Project Cycle Framework.
Learn problem scoping and ways to set goals for an AI project.
Understand the iterative nature of problem scoping for in the AI project cycle.
Foresee the kind of data required and the kind of analysis to be done.
Share what have the students discussed so far.
Identify data requirements and find reliable sources to obtain relevant data.
Understand the purpose of Data Visualisation.
Understand and visualise computer’s ability to identify alphabets and handwritings.
Acquire introductory Python programming skills in a very user-friendly format.

Course Outline:

UNIT	SUB-UNIT	SESSION/ACTIVITY/PRACTICAL	LEARNING OUTCOMES
Introduction to AI	Excite	Session: Introduction to AI and setting up the context of the curriculum	To identify and appreciate Artificial Intelligence and its applications in daily life.
		Ice Breaker Activity: Google Lens and AWS Video Recognition Learners to see how Google Lens and AWS Image and Video Recognition systems work.
		Recommended Activity: The AI Game Learners to participate in three games based on different AI domains. Game 1: Rock, Paper and Scissors (based on data) Game 2: Mystery Animal (based on Natural Language Processing – NLP) Game 3: Emoji Scavenger Hunt (based on Computer Vision – CV)	To relate, apply and reflect on the Human-Machine Interactions. To identify and interact with the three domains of AI: Data, Computer Vision and Natural Language Processing.
		Recommended Activity – AI Quiz	To undergo an assessment for analysing progress towards acquired AI-Readiness skills.
	Relate	Video Session: To watch a video Introducing the concept of Face Detection, Face verification, Identification of Postures.	Learners to relate to latest applications of Artificial Intelligence.
	Purpose	Session: Introduction to Sustainable Development goals	To understand the impact of Artificial Intelligence on Sustainable Development Goals to develop responsible citizenship.
	Purpose	Recommended Activity – Go Goals Game Learners to answer questions on Sustainable Development Goals
	Possibilities	Session: Theme-based research and Case Studies Learners will listen to various case-studies of inspiring start-ups, companies or communities where AI has been involved in real-life. Learners will be allotted a theme around which they need to search for present AI trends and have to visualize the future of AI in and around their respective theme.	To research and develop awareness of skills required for jobs of the future. To imagine, examine and reflect on skills required for futuristic opportunities. To develop effective communication and collaborative work skills.
	AI Ethics	Video Session: Discussing about AI Ethics	To understand and reflect on the ethical issues around AI.
		Recommended Activity: Ethics Awareness Students play the role of major stakeholders and they have to decide what is ethical and what is not for a given scenario.	To understand and reflect on the ethical issues around AI.
		Session: AI Bias and AI Access Discussing about the possible bias in data collection Discussing about the implications of AI technology	To gain awareness around AI bias and AI access.

AI Project Cycle	Problem Scoping	Session: Introduction to AI Project Cycle Problem Scoping Data Acquisition Data Exploration Modelling Evaluation	Identify the AI Project Cycle Framework
		Activity: Brainstorm around the theme provided and set a goal for the AI project. Discuss various topics within the given theme and select one. List down/Draw a mindmap of problems related to the selected topic and choose one problem to be the goal for the project.	Learn problem scoping and ways to set goals for an AI project.
		Activity: Data and Analysis What are the data features needed? Where can you get the data? How frequent do you have to collect the data? What happens if you don’t have enough data? What kind of analysis needs to be done? How will it be validated? How does the analysis inform the action?	Understand the iterative nature of problem scoping for in the AI project cycle. Foresee the kind of data required and the kind of analysis to be done.
		Presentation: Presenting the goal, actions and data.	Share what have the students discussed so far.
	Data Acquisition	Activity: Introduction to data and its types. Students work around the scenarios given to them and think of ways to acquire data.	Identify data requirements and find reliable sources to obtain relevant data.
	Data Exploration	Session: Data Visualisation Need of visualising data Ways to visualise data using various types of graphical tools.	To understand the purpose of Data Visualisation
	Modelling	Recommended Activity: Pixel It To create an “AI Model” to classify handwritten letters. Students develop a model to classify handwritten letters by dividing the alphabets into pixels. Pixels are then joined together to analyse a pattern amongst same alphabets and to differentiate the different ones.	Understand and visualise computer’s ability to identify alphabets and handwritings.
Introduction to Python		Session: Introduction to Python Language Introducing Python programming and its applications	Acquire introductory Python programming skills in a very user-friendly format.
Introduction to Python		Practical: Python Basics Students go through lessons on Python Basics (Variables, Arithmetic Operators, Expressions, Data Types – integer, float, strings, using print() and input() functions) Students will try some simple problem-solving exercises using lists on Python Compiler.
Neural Networks		Session: Introduction to Neural Networks Describing the function of Neural Networks What are Deep Neural Networks? Introduction to Deep Learning Transfer Learning using Tensorflow
Neural Networks		Activity: Examples using Transfer Learning for Face Detection, Face verification and Identification of Postures AWS Rekognition for Posture identification FaceNet and HaarCascades for Face Detection. DNN’s for Face Verification