Upon successful completion of the program, learners will possess a comprehensive understanding of Python programming for data analytics and artificial intelligence. They will demonstrate the ability to create functional AI applications and deploy them on cloud platforms. Furthermore, learners will be introduced to emerging domains like Generative AI, fostering their interest and exploration in this evolving field. Additionally, learners will acquire the necessary skills to pursue entry-level roles or further education in the field.
Upon completing this course, students will be able to develop the following skill sets:
The program is designed to be completed in 22 to 24 weeks covering around 160 hours of content and project.
Week |
Description of the content to be covered |
Duration (Hrs.) |
1 |
Unit I- AI Tools, Technologies, and Market Trends |
5 |
1 |
Getting familiar with AI and ML concepts
- Understanding AI terminologies, real-world applications, and market trends.
- Exploring open-source tools, platforms, and the latest AI models.
Hands-on:
- Use Microsoft platform Lobe, for advanced AI development and deployment.
|
2.5 |
1 |
Leveraging GitHub for AI Development
- Understanding GitHub for project development and repository creation.
- Version control with Git and GitHub, Git development and Git commands.
- Collaborative development workflows.
- Leveraging GitHub Co-Pilot for AI-Assisted Coding.
Hands-on:
- Exercise using Co-Pilot to accelerate code writing and suggest programming patterns.
|
2.5 |
2 |
Unit II- Python Programming for Data Science Applications |
17 |
2 |
Programming concepts with Python
- Fundamentals of open-source Python programming tools.
- Installation of Anaconda and Set up the Python Environment.
- Use Command Line and IDE to create and execute a Python program.
- Basics of Python: Data types, variables, basic operators, and input/output
- Control Structures: Conditional statements, loops.
- Functions and Modules: Writing functions and importing modules.
Data Handling
- Data Structures: Lists, Dictionaries, Sets, Tuples.
- File Handling: Reading from and writing to files.
|
5 |
3 |
Object-Oriented Programming
- Classes and Objects: Understanding OOP, defining classes, and creating objects.
- Inheritance and Polymorphism: Extending classes, using polymorphism.
Hands-on:
- Hacker Rank problem-solving practice to solve a series of coding problems that cover various Python programming concepts such as loops, conditionals, functions, data structures, String Manipulation and File Handling.
|
6 |
4 |
Advanced Python Concepts
- Exception Handling: Try, except blocks.
- List Comprehensions and Generators.
- Decorators and Context Managers.
SQL and Databases:
- Introduction to databases, Basic SQL queries, connecting MySQL Database using Python, Executing SQL Queries from Python, Fetching and Manipulating Data with Python
Hands-on:
- Create a mini project on Personal Fitness Tracker that helps users track their daily workouts, view progress summaries, and save, and load data by applying Python programming concepts such as data structures, file handling, functions, and object-oriented programming (OOP).
|
6 |
5 |
Unit III- Applied Data Analytics with Python Programming |
16 |
5 |
Data Processing and Analysis
- Introduction to data science and the data analysis process.
- Data preprocessing techniques: Data cleaning, normalisation, and transformation.
Python Packages for EDA applications:
- Introduction of Exploratory Data Analysis and Types of EDA.
- Python packages for EDA applications: Practicing with NumPy.
- Python packages for EDA applications: Practicing with Pandas.
- Practicing with Data Visualization Python packages (matplotlib and Seaborn))
|
6 |
5 |
Hands-on:
- Create a mini project to guide students through conducting Exploratory Data Analysis (EDA) on a dataset containing information about university students. The objective is to uncover insights and patterns related to academic performance, study habits, and extracurricular activities.
|
2 |
6 |
Data Analytics with Power BI
- Introduction to Power BI and its features.
- Data ingestion and transformation in Power BI.
- Visualization techniques in Power BI.
- Creating interactive dashboards and reports in Power BI.
|
6 |
6 |
Hands-on:
- Analyzing Air Quality Data using Power BI. This hands-on project will guide students through analyzing air quality data using Power BI. The goal is to provide insights into air pollution levels, identify trends, and understand the impact of various factors on air quality.
|
2 |
7 |
Unit IV: Implement and Deploy AI Models |
15 |
7 |
Classification of ML algorithms
- Supervised, Unsupervised and Semi-supervised ML Algorithms.
Supervised ML Algorithms
- Understanding Linear Regression, Decision Tree and Support Vector Machine.
- Model Evaluation and Validation.
- Train-test split, cross-validation.
- Performance metrics (accuracy, precision, recall, F1-score).
Hands-on:
- Use linear regression to predict housing prices based on features such as square footage, number of bedrooms, bathrooms, etc.
- Use decision trees to predict whether customers are likely to churn (cancel their subscription or leave the service).
|
5 |
8 |
Unsupervised ML Algorithm
- Understanding the K-Means Clustering algorithm.
- Evaluation metrics for model performance.
Hands-on:
- K-Means Clustering for Customer Segmentation: Use k-means clustering to segment customers based on their purchasing behaviors, demographics and other relevant features.
|
3 |
9 |
Neural Networks and Deep Learning
- Introduction to deep learning and neural networks.
- Building neural networks using TensorFlow.
- Advanced techniques in deep learning (CNNs, RNNs, etc.).
- Training deep learning models and transfer learning.
Hands-on:
- Building a simple feedforward neural network using TensorFlow.
- Image classification with CNNs using the MNIST dataset.
|
7 |
10 |
Unit V: Building user Interface for ML/DL Models and Cloud Deployment |
10 |
10 |
- Understanding the web development landscape.
- Overview of front-end technologies: HTML, CSS, JS.
- Structuring web pages with HTML.
- Styling web pages with CSS.
- Responsive designing using media queries, flex, and grid.
- Bootstrap Fundamentals: Component and Layout.
JavaScript Essentials:
- Basic syntax and operators.
- Functions and control structures.
- ES6 Features.
- DOM manipulation.
- Basics of tensorflow.js
Hands-on:
- Integrating User interface with the AI/ML Models.
|
7 |
11 |
Deployment using an Azure Cloud and open-source Platform
- Understand the Azure cloud and its applications in AI.
- Containerization of an AI application.
- Understanding Heroku and its features for web application deployment.
- Setting up a Heroku Account and environment.
- Creating a Heroku app and deploying a Docker Container.
- Connection of front-end to deployed back-end.
- Deployment of AI application on Azure.
Hands-on:
- Deploying an AI application using open-source tools and azure cloud.
|
3 |
12 |
Unit VI: Exploring NLP Fundamentals and Generative AI Basics |
12 |
12 |
Understanding of Generative AI
- Introduction to Generative AI and its applications.
- Understanding Large Language Models (LLMs).
- Working with Prompts.
Pre-trained Models and GPT
- Introduction to Pre-trained Models.
- Concept of transfer learning.
- Overview of popular pre-trained models.
|
5 |
13 |
Using GPT Models for NLP Tasks
- Understanding Natural Language Processing.
- Sentiment analysis and chatbot development.
- Fine-tuning Pre-trained Models.
- Customizing models for specific tasks using Hugging Face Transformers.
Hands-on:
- Implementing text generation with GPT-2.
- Use Azure AI Studio to build a natural language processing (NLP) model to analyse customer reviews and feedback.
- Create a chatbot for customer service that can understand and respond to user queries in multiple languages with Azure.
|
7 |
14 |
Unit VII: Interpret and understand visual information using Computer Vision |
7 |
14 |
Computer vision with Python and OpenCV
- Introduction to computer vision.
- Image representation, Basics of digital images (pixels, resolution, colour channels) and Image formats (RGB, grayscale, etc.).
- Basic image manipulation and visualization.
- Image processing techniques (Image Filtering, Thresholding and Morphological Operations).
- Implementing basic image filtering and thresholding.
- Feature extraction techniques.
- Implementing corner detection and edge detection algorithms.
|
5 |
15 |
Hands-on:
- Building an image classification application using a pre-trained Vision Transformer (ViT) model from Hugging Face and with Azure cognitive service.
|
2 |
16 |
Unit VIII: Responsible AI Implementation |
3 |
16 |
Responsible AI Implementation
- Discussing ethical considerations in AI development and deployment.
- Addressing bias, fairness, and transparency in AI algorithms.
- Understanding the societal impact of AI technologies and the responsibility of developers in AI development.
Case Study:
- Ethical Considerations in AI-Powered Hiring Tool.
|
3 |
|
Capstone Project |
|
17-24 |
Mentoring Framework: Integrating Design Thinking into AI Project Development |
40 |
|
1. Empathize with Users
- Encourage students to empathize with end-users through interviews, observations, and user research.
- Help them understand user needs, pain points, and preferences related to the AI solution.
2. Define the Problem
- Guide students to clearly define the problem statement from the perspective of end-users and stakeholders.
- Frame the problem statement focusing on underlying needs and motivations.
3. Ideate Creative Solutions
- Facilitate brainstorming sessions for generating a wide range of AI solution ideas.
- Encourage creative thinking and exploration of unconventional approaches.
4. Prototype Solutions
- Assist students in prototyping AI solutions using low-fidelity or high-fidelity prototypes.
- Encourage rapid iteration and refinement based on user feedback and testing.
5. Test and Iterate
- Help students conduct user testing and feedback sessions to gather insights on usability and effectiveness.
- Facilitate iterative refinement of prototypes based on user feedback.
6. Implement Solutions
- Guide students in translating prototyped solutions into functional AI systems.
- Assist in selecting appropriate AI techniques, algorithms, and tools for implementation.
7. Evaluate Impact
- Facilitate discussions on evaluating the impact of the AI solution on users, stakeholders, and the ecosystem.
- Help students assess the success of their designs based on predefined metrics and criteria.
8. Iterative Design Process
- Encourage continuous refinement and improvement of AI solutions based on user feedback and evolving requirements.
|
|
Below are several potential problem statements in which students can employ their learned skills to create and implement a practical application.
