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AI+ Engineer™ 2.0
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Module 1: Foundations of Artificial Intelligence
1.1 Module intro: Foundations of Artificial Intelligence
1.2 Evolution and Impact of Artificial Intelligence
1.3 What is AI?
1.4 Mastering Machine Learning: Applications and Innovations
1.5 Introduction to Deep Learning
1.6 Data: The Fuel of AI
1.7 Navigating Ethical AI: Bias, Transparency, and Accountability
1.8 Hands-on Exercise: Evaluating an AI System for Ethical Considerations
1.9 Privacy and Data Governance
1.10 Summary: Foundations of Artificial Intelligence
Module 1: E-Book
Quiz
Module 2: Introduction to AI Architecture
2.1 Module intro: Introduction to AI Architecture
2.2 Topic intro: Overview of AI and its Various Applications
2.3 Historical Perspective
2.4 Contemporary Applications Across Industries
2.5 Topic: Introduction to AI Architecture
2.6 Key Components and Structures
2.7 Role in Solving Real-World Problems
2.8 AI Lifecycle Development: From Planning to Monitoring
2.9 Best Practices in Each Phase
2.10 Environment Setup Using Popular AI Frameworks
2.11 AI Development: From Setup to Mastery
2.12 Summary: AI Foundations: From History to Modern Applications
Module 2: E-Book
Quiz
Lab Practice 2.1
Lab Practice 2.2
Lab Practice 2.3
Lab Practice 2.4
Python File Download
Module 3: Fundamentals of Neural Networks
3.1 Module intro: Fundamentals of Neural Networks
3.2 Topic intro: Basics of Neural Networks
3.3 3.1.1 Neurons, Layers, and Architectures
3.4 3.1.2 Feedforward and Backpropagation Concepts
3.5 3.2.1 Common Activation Functions
3.6 3.2.2 Importance in Shaping the Network's Behaviour
3.7 3.3.1 Understanding the Backpropagation Process
3.8 3.3.2 Popular Optimization Algorithms (Gradient Descent, Adam, RMSprop)
3.9 3.4.1 Practical Implementation of a Basic Neural Network
3.10 Summary Mastering Neural Networks: A Comprehensive Recap
Module 3: E-Book
Quiz
Lab Practice 3.1
Lab Practice 3.2
Lab Practice 3.3
Python File Download
Module 4: Applications of Neural Networks
4.1 Introduction: : Applications of Neural Networks
4.2 Topic 4.1 Introduction: Introduction to Neural Networks in Image Processing
4.3 Real-world Applications in Image Recognition and Computer Vision
4.4 Topic 4.2 Introduction: Neural Networks for Sequential Data
4.5 Introduction to Handling Sequential Data Using Neural Networks
4.6 Applications in Natural Language Processing and Time Series Analysis
4.7 Utilizing Transfer Learning with Pre-Trained Models for Practical Applications
4.8 Summary: Applications of Neural Networks
Module 4: E-Book
Quiz
Lab Practice 4.1
Lab Practice 4.2
Lab Practice 4.3
Python File Download
Module 5: Significance of Large Language Models (LLM)
5.1 intro: Significance of Large Language Models (LLM)
5.2 Explore Large Language Models (LLMs)
5.3 Understanding the Role of LLMs in Natural Language Understanding
5.4 Implications for Various Practical Applications
5.5 Topic intro: Popular Large Language Models
5.6 Overview of Widely Used Large Language Models (BERT, GPT, and Others)
5.7 Unique Features and Use Cases in Real-world Scenarios Examine
5.8 Topic intro: Practical Finetuning of Language Models and Adapting Pre-trained Models for Domain Specific tasks
5.9 Techniques for Effective Finetuning of Language Models
5.10 Topic intro: Hands-on: Practical Fine-tuning for Text Classification
5.11 Summary: Significance of Large Language Models (LLM)
Module 5: E-Book
Quiz
Lab Practice 5.1
Lab Practice 5.2
Python File Download
Module 6: Application of Generative AI
6.1 Introduction: Application of Generative AI
6.2 Topic 6.1 Introduction to Generative Adversarial Networks (GANs)
6.3 6.1.1 Understanding the Basic Concept and Structure of GANs
6.4 6.1.2 Real-world Applications in Image Generation and Data Augmentation
6.5 Topic 6.2 Applications of Variational Autoencoders (VAEs)
6.6 6.2.1 Understanding VAEs and Their Applications for Generative Tasks
6.7 6.2.2 Use Cases in Image Synthesis and Data Representation
6.8 Topic 6.3 Generating Realistic Data Using Generative Models
6.9 6.3.1 Practical Techniques for Creating Synthetic Data
6.10 6.3.2 Addressing Challenges Related to Data Scarcity in Practical Scenarios
6.11 6.4 Hands-on: Implementing Generative Models for Image Synthesis
6.12 6.4.1 Real-World Implementation of a GAN for Image Generation
6.13 6.4.2 Training and Evaluating the Model on Practical Datasets
6.14 Summary: Application of Generative AI
Module 6: E-Book
Quiz
Lab Practice 6.1
Lab Practice 6.2
Python File Download
Module 7: Natural Language Processing
7.1 Introduction: Natural Language Processing
7.2 Topic 7.1 NLP in Real-world Scenarios
7.3 7.1.1 Practical Applications of NLP in Various Industries
7.4 7.1.2 Real-world Scenarios in Sentiment Analysis, Chatbots, and Language Translation
7.5 Topic 7.2 Attention Mechanisms and Practical Use of Transformers
7.6 Topic 7.3 In-depth Understanding of BERT for Practical NLP Tasks
7.7 7.3.1 Practical Insights into BERT (Bidirectional Encoder Representations from Transformers)
7.8 7.3.2 Real-world Applications in Various NLP Tasks
7.9 Topic 7.4 Hands-on: Building Practical NLP Pipelines with Pre-Trained Models
7.10 Summary: Natural Language Processing
Module 7: E-Book
Quiz
Lab Practice 7.1
Lab Practice 7.2
Lab Practice 7.3
Lab Practice 7.4
Lab Practice 7.5
Lab Practice 7.6
Lab Practice 7.7
Lab Practice 7.8
Lab Practice 7.9
Lab Practice 7.10
Lab Practice 7.11
Python File Download
Module 8: Transfer Learning with Hugging Face
8.1 Module Introduction: Transfer Learning with Hugging Face
8.2 Topic 8.1. Overview of Transfer Learning in AI
8.3 8.1.1 Principles and Advantages of Transfer Learning
8.4 8.1.2 Applications in Various Domains
8.5 Topic 8.2. Transfer Learning Strategies and Techniques & 8.2.1 Different Approaches to Transfer Learning
8.6 8.2.2 Choosing the Right Strategy for Specific Tasks
8.7 Topic 8.3 Hands-on: Implementing Transfer Learning with Hugging Face Models for Various Tasks & 8.3.1 Practical Exercises
8.8 8.3.2 Adapting Pre-trained Models.
8.9 Summary: Transfer Learning with Hugging Face
Module 8: E-Book
Quiz
Lab Practice 8.1
Python File Download
Module 9: Crafting Sophisticated GUIs for AI Solutions
9.1 Introduction: Crafting Sophisticated GUIs for AI Solution
9.2 Topic 9.1 Overview of GUI-based AI Applications and Importance of User-Friendly Interfaces
9.3 Various Ways for Implementing GUI
9.4 Topic 9.2 Web-Based Framework
9.5 Streamlit: A Python Library for Interactive Web Applications
9.6 Dash (Plotly): Creating Interactive Web-based Dashboards with Python
9.7 Desktop Application Framework
9.8 PyQt and PySide (Python): Python Bindings for the Qt Framework
9.9 Electron (JavaScript, HTML, CSS): Cross-platform Desktop Applications
9.10 Summary Crafting Sophisticated GUIs for AI Solution
Module 9: E-Book
Quiz
Lab Practice 9.1
Lab Practice 9.2
Lab Practice 9.3
Lab Practice 9.4
Python File Download
Module 10: AI Communication and Deployment Pipeline
10.1 Introduction: AI Communication and Deployment Pipeline
10.2 Topic 10.1 Communicating AI Results Effectively to Non-Technical Stakeholders
10.3 Strategies for Clear and Concise Communication
10.4 Visualizations and Storytelling with AI Results
10.5 Topic 10.2 Building a Deployment Pipeline for AI Models
10.6 Key Components of a Robust Deployment Pipeline
10.7 Continuous Integration and Continuous Deployment (CI/CD) Practices
10.8 Topic 10.3 Developing Prototypes Based on Client Requirements
10.9 Understanding Client Needs and Expectations and Prototyping Approaches and Methodologies
10.10 Topic 10.4 Hands-on: Deployment and Creating an End-to-End Deployment Pipeline for an AI Model
10.11 Summary: AI Communication and Deployment Pipeline
Module 10: E-Book
Quiz
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AI+ Engineer™ 2.0