Lesson 1: Foundations of Python
Python Programming and IDEs
Basics of Python Programming
Advanced Data Types
Lesson 2: Functions and Control Flow
Flow Control in Python
User-Defined Modular Functions
Lesson 3: Working with External Data and Files
Importing External Data from CSV Files
Working with Text files
Lesson 4: Python Libraries, Web Scraping, and Dealing with APIs
Libraries and Functions
Pandas and Matplotlib Libraries
APIs for Web Scraping
Use cases of advanced Python programming in various industries.
Additional practice exercises ### Analyze Business Data and Create Interactive Dashboards using Python
Lesson 1: Understanding Data
Introduction to Python packages for data manipulation
Importing and exporting data
Importing datasets and understanding data
Basics of analyzing the data
Lesson 2: Data Wrangling
Dealing with data issues and preparation in Python
Data Formatting and conversions in Python
Data Wrangling
Working with Pandas
Lesson 3: Exploratory Data Analysis
Performing descriptive statistics with Python
Correlations, Scatter-plots, and charts with Matplotlib in Python
Understanding data analysis with respect to various business scenarios
Lesson 4: Model Development for Analysis
Hypothesis Testing
Linear Regression and Multiple Regression Models
Model Evaluation Methods
Model selection
Lesson 5: Data Visualization
Understanding basic metrics and KPIs
Visualizations using Python (Seaborn, Folium) ### Predictive Analytics and Machine Learning using Python
Lesson 1: Introduction to Machine Learning with scikit-learn
Introducing the machine learning flow and concepts
Functions within scikit-learn
Introduction to supervised and unsupervised machine learning
Lesson 2: Unsupervised Machine Learning
Understanding unsupervised ML algorithms
Introduction to clustering (k-means)
Implementing clustering with real use cases
Lesson 3: Supervised Machine Learning
Introduction to various supervised learning algorithms
Understanding feature engineering and feature sets
Understanding and implementing
Implementing the above algorithms with real use cases
Lesson 4: Evaluating machine learning models
Understanding model selection and evaluation methods
Optimize machine learning models ### Promote Customer Centric Innovations
Lesson 1: Promote Customer-Centric Innovations
CENTRIC Framework to Transform Customer Innovation
Key Drivers of Innovation
Converging and Diverging Thinking Process
Lesson 2: Develop Assumptions & Find Insights
Gather Insights, Build Understanding of Customer
Navigate: Customer Experience & Point-of-View
Lesson 3: Develop BLesson eprint to Address Challenges & Opportunities
Develop BLesson eprint
Transform: Address Challenges & Opportunities
Lesson 4: Develop Concepts & Prototypes
Run Through Prototype
Lesson 5: Devise Experiments
Devise Experiments to Run-through Concepts
Infer Data, Metrics & KPIs
Commission Implementation of SoLesson tion ### Fundamentals of Artificial Intelligence
Lesson 1
Introduction to Artificial Intelligence and its backbone
Emerging Tools and Trends in Artificial Intelligence
Discussing the major drivers for the growth of AI in various verticals
Lesson 2
General and Industrial use cases of AI – Showcasing successful AI deployments and its advantages
Common and Advanced AI Examples that can be applied to the industry across:
Convergence of traditional business and artificial intelligence – How it can benefit an organization.
Lesson 3
Understanding the code and no-code tools in the market for AI and its branches
Learning how to craft an AI architecture or blueprint for business cases
Understand the different types of machine learning
Lesson 4
Hands-on to create simple machine learning prototype models using no-code and code-based tools
Demonstration of Deep Learning and other popular models
Creating an AI-driven mindset ### ChatGPT for Beginners
Lesson 1: Introduction to Generative AI
Overview of generative AI and its potential applications
Real-world applications and use cases for business professionals
The future of generative AI and its potential impact on industries
Understanding the different types of generative AI models and how they work
Lesson 2: Introduction to ChatGPT
What is ChatGPT and its evolution?
How does ChatGPT work?
What are the applications of ChatGPT?
How was ChatGPT trained?
Lesson 3: Content Creation with ChatGPT
How to generate text with ChatGPT?
What are some examples of text generated by ChatGPT?
Text generation, summarization, and personalization
Using ChatGPT for qualitative insights in data analysis
How to fine-tune ChatGPT for specific tasks?
Lesson 4: Prompt Engineering for Effective Communication
Principles of effective prompt design
Techniques for crafting high-quality prompts
Examples of prompt engineering for various tasks (e.g., summarization, question-answering, creative writing)
Tailoring prompts for specific business use cases
Lesson 5: Ethical Considerations when Using ChatGPT
What are the ethical considerations when using ChatGPT?
How to avoid bias in ChatGPT?
How to use ChatGPT responsibly? ### Generative AI
Lesson 1: (Recap) Introduction to Generative AI
Overview of generative AI and its potential applications
Real-world applications and use cases for business professionals
The future of generative AI and its potential impact on industries
Understanding the different types of generative AI models and how they work
Lesson 2: More Practice: Generative Text and Prompt Engineering
Text generation, summarization, and personalization
Using ChatGPT for qualitative insights in data analysis
Tailoring prompts for specific business use cases
Lesson 3: Image Generation with DALL-E, Stable Diffusion and GANs
Overview of Generative AI for Image Generation
Understanding how to use Generative AI for Image Generation
Using Generative AI for image generation in advertising and e-commerce
Lesson 4: Generative Media: Audio and Video
Overview of Generative AI for audio and video synthesis
Using Audio and Video Synthesis for Marketing & Advertising
Generating lifelike avatars using Generative AI Tools
Lesson 5: Leveraging Generative AI for Market Research
Identifying trends and opportunities with AI-driven analysis
Enhancing customer segmentation and targeting
Gathering and analyzing consumer feedback with ChatGPT
Lesson 6: Generative AI using Python
Generative Text using APIs in Python
Image Generation, Style Transfer and Neural Art Generation in Python
Data Augmentation for Computer Vision using Python APIs
Video and Animation Generation using Generative AI APIs
Lesson 7: Ethical Considerations and Future Implications
Responsible AI usage for non-technical professionals
Privacy and data protection concerns
The evolving landscape of jobs and the future of work
Job displacement and creation due to AI advancements
Lesson 8: Case Studies
Overview of recent developments and advancements in generative AI
Review of relevant case studies of generative AI applications in different industries ### Deep Learning Models and AI using Python
Lesson 1: Introduction to AI and Basics of Neural Networks
Introduction to AI and Deep Learning
Neural Networks
Lesson 2: Introduction to TensorFlow
Python libraries for Deep Learning
TensorFlow basics
TensorFlow graphs, variables and placeholders
Creating NN with TensorFlow
Regression using TensorFlow
Classification using TensorFlow
Activity 1: Developing a regression model using TensorFlow
Activity 2: Developing a classification model using TensorFlow
Saving and restoring models
Deployment of inference ft. Gradio
Lesson 3: Convolutional Neural Networks
Understanding CNNs and Architecture of a CNN
MNIST data – Overview
Image classification using CNN
Activity: Developing CNN model to classify MNIST CNN dataset
Real-world industry examples of CNNs in action
Lesson 4: Recurrent Neural Networks
Understanding RNNs
Architecture of an RNN and Implementing RNN using Python
Introduction to LSTM and GRU
RNN with TensorFlow API
Activity: Time series forecasting using RNN
Real-world industry examples of RNNs in action
Lesson 5: Object Detection and Deep Fakes
Introduction to AutoEncoders
Introduction to Generative Adversarial Networks (GAN)
How deep fakes are created?
Activity: Object detection using GANs
Real-world industry examples of GANs in action ### AI for Business Innovation
Lesson 1: Understanding AI in Business Domains
Traditional AI vs. Generative AI
Industry-Specific AI Strategies
Lesson 2: AI for Process Automation
Automating Business Processes with AI
Implementing AI-Driven Process Automation
Lesson 3: AI for Customer Experience Enhancement
AI-Enhanced Customer Interactions
Measuring and Optimizing Customer Experience with AI
Lesson 4: AI for Data-Driven Decision-Making
AI-Enhanced Decision Support
AI-Driven Strategic Decision-Making
Lesson 5: Responsible AI and Ethical Business Innovation
Advanced AI Ethics in Business
AI for Sustainable and Socially Responsible Business Innovation ### Microsoft Azure AI Fundamentals
Lesson 1: Explore Fundamentals of Artificial Intelligence
Introduction to Artificial Intelligence
Artificial Intelligence in Microsoft Azure
Lesson 2: Explore Fundamentals of Machine Learning
Introduction to Machine Learning
Azure Machine Learning
Lesson 3: Explore Fundamentals of Computer Vision
Computer Vision Concepts
Creating Computer Vision solutions in Azure
Lesson 4: Explore Fundamentals of Natural Language Processing
Introduction to Natural Language Processing
Building Natural Language Solutions in Azure ### Building AI Models using Azure
Lesson 1: (RECAP) Introduction to Azure and AI
Overview of Microsoft Azure
Basics of Artificial Intelligence
Lesson 2: Setting up Azure Workspace
Creating an Azure Account
Configuring Azure Machine Learning Workspace
Lesson 3: Data Collection Techniques
Methods and Sources of Data Collection
Best Practices for Data Acquisition
Lesson 4: Data Storage in Azure
Azure Data Services for Data Storage
Managing and Organizing Data in Azure
Lesson 5: Building Machine Learning Models
Introduction to Azure Machine Learning
Model Training with Azure AutoML and Azure ML Designer
Lesson 6: Model Evaluation and Optimization
Model Evaluation Techniques
Model Fine-tuning and Hyperparameter Optimization
Lesson 7: Model Deployment in Azure
Deploying Machine Learning Models as Web Services
Implementing Azure Functions and Azure Container Instances
Lesson 8: Advanced Topics and Applications
Exploring Azure Cognitive Services
Real-world Industry-specific AI Applications ### Artificial Intelligence Ethics and Governance
Lesson 1: Introduction to AI Ethics
Defining AI ethics and its significance.
Historical context of AI ethics.
Ethical considerations in AI use cases.
Lesson 2: Principles of Ethical AI
Core principles of ethical AI (fairness, transparency, accountability).
Introduction to AI ethics frameworks and guidelines (e.g., IEEE, AI Ethics Impact Assessment).
Practical Activity: Group discussion on AI ethics principles.
Lesson 3: AI and Society
The societal impact of AI technologies.
Case studies on AI's influence on society.
Group exercise: Analyzing AI's impact on society.
Lesson 4: AI Governance and Compliance
Overview of AI governance structures.
Regulatory requirements for AI.
Practical Activity: Developing a compliance checklist for AI projects.
Lesson 5: Addressing Bias and Fairness
Understanding bias in AI and its consequences.
Mitigating bias through responsible AI practices.
Practical Activity: Identifying potential bias in AI algorithms.
Real-world examples of AI successes and failures in ethics and governance.
Group discussion and analysis of case studies.
Lesson 7: Creating an Ethical AI Action Plan
Developing an action plan for implementing ethical AI practices within your organization.
Group activity: Drafting an AI ethics and governance framework. ### (SCTP) Associate AI/ML Developer: Capstone Project
Lesson 1: Project Kick-off and Idea Generation
Understanding the capstone project requirements and guidelines.
Brainstorming AI project ideas and selecting a suitable topic.
Creating a project proposal and defining project objectives.
Lesson 2: Data Collection and Preprocessing
Collecting and sourcing data for the capstone project.
Data preprocessing, including data cleaning, transformation, and feature engineering.
Preparing data for model training and evaluation.
Lesson 3: Model Selection and Development
Choosing the appropriate AI model(s) for the project.
Implementing the selected model(s) and fine-tuning hyperparameters.
Developing and training the AI model(s) using industry-standard tools.
Lesson 4: Model Evaluation and Optimization
Evaluating model performance using relevant metrics.
Optimizing the model based on evaluation results.
Addressing issues related to overfitting and underfitting.
Lesson 5: Model Deployment and Integration
Deploying the trained AI model into a production environment.
Integrating the AI model with other software components or systems.
Ensuring scalability and efficiency in deployment.
Lesson 6: Testing, Validation, and Debugging
Testing the deployed AI system for reliability and accuracy.
Validation against real-world data and scenarios.
Debugging and addressing potential issues.
Lesson 7: Documentation and Reporting
Creating comprehensive documentation for the project.
Preparing a detailed project report, including the project's methodology and outcomes.
Presenting findings and insights to peers and instructors.
Lesson 8: Project Presentation and Showcase
Preparing and delivering a project presentation.
Showcasing the project to peers, instructors, and industry experts.
Receiving feedback and making final improvements to the project.
Lesson 9: Project Submission and Evaluation
Preparing the final project deliverables.
Submitting the completed capstone project for evaluation.
Assessment of the project based on predefined criteria.