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AI+ Engineer™
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Introduction
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Lab Instructions
Module 1: Foundations of Artificial Intelligence
Module 1: Foundations of Artificial Intelligence
1.1 Introduction to AI
1.2 Core Concepts and Techniques in AI
1.3 Ethical Considerations
Module 1: Foundations of Artificial Intelligence
Quiz
Lab Practice 1.1
Lab Practice 1.2
Lab Practice 1.3
Python File Download
Module 2: Introduction to AI Architecture
Module 2: Introduction to AI Architecture
2.1 Overview of AI and its Various Applications
2.2 Introduction to AI Architecture
2.3 Understanding the AI Development Lifecycle
2.4 Hands-on: Setting up a Basic AI Environment
Module 2: Introduction to AI Architecture
Quiz
Lab Practice 2.1
Lab Practice 2.2
Python File Download
Module 3: Fundamentals of Neural Networks
Module 3: Fundamentals of Neural Networks
3.1 Basics of Neural Networks
3.2 Activation Functions and Their Role
3.3 Backpropagation and Optimization Algorithms
3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework
Module 3: Fundamentals of Neural Networks
Quiz
Lab Practice 3.1
Python File Download
Module 4: Applications of Neural Networks
Module 4: Applications of Neural Networks
4.1 Introduction to Neural Networks in Image Processing
4.2 Neural Networks for Sequential Data
4.3 Practical Implementation of Neural Networks
Module 4: Applications of Neural Networks
Quiz
Lab Practice 4.1
Lab Practice 4.2
Lab Practice 4.3
Lab Practice 4.4
Lab Practice 4.5
Lab Practice 4.6
Python File Download
Module 5: Significance of Large Language Models (LLM)
Module 5: Significance of Large Language Models (LLM)
5.1 Exploring Large Language Models (LLMs)
5.2 Popular Large Language Models
5.3 Practical Finetuning of Language Models
5.4 Hands-on: Practical Finetuning for Text Classification
Module 5: Significance of Large Language Models (LLM)
Quiz
Lab Practice 5.1
Lab Practice 5.2
Lab Practice 5.3
Lab Practice 5.4
Lab Practice 5.5
Lab Practice 5.6
Lab Practice 5.7
Lab Practice 5.8
Lab Practice 5.9
Python File Download
Module 6: Application of Generative AI
Module 6: Application of Generative AI
6.1 Introduction to Generative Adversarial Networks (GANs)
6.2 Applications of Variational Autoencoders (VAEs)
6.3 Generating Realistic Data Using Generative Models
6.4 Hands-on: Implementing Generative Models for Image Synthesis
Module 6: Application of Generative AI
Quiz
Lab Practice 6.1
Python File Download
Module 7: Natural Language Processing
Module 7: Natural Language Processing
7.1 NLP in Real-world Scenarios
7.2 Attention Mechanisms and Practical Use of Transformers
7.3 In-depth Understanding of BERT for Practical NLP Tasks
7.4 Hands-on: Building Practical NLP Pipelines with Pretrained Models
Module 7: Natural Language Processing
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
Python File Download
Module 8: Transfer Learning with Hugging Face
Module 8: Transfer Learning with Hugging Face
8.1 Overview of Transfer Learning in AI
8.2 Transfer Learning Strategies and Techniques
8.3 Hands-on: Implementing Transfer Learning with Hugging Face Models for Various Tasks
Module 8: Transfer Learning with Hugging Face
Quiz
Lab Practice 8.1
Lab Practice 8.2
Lab Practice 8.3
Lab Practice 8.4
Python File Download
Module 9: Crafting Sophisticated GUIs for AI Solutions
Module 9: Crafting Sophisticated GUIs for AI Solutions
9.1 Overview of GUI-based AI Applications
9.2 Web-based Framework
9.3 Desktop Application Framework
Module 9: Crafting Sophisticated GUIs for AI Solutions
Quiz
Lab Practice 9.1
Lab Practice 9.2
Lab Practice 9.3
Python File Download
Module 10: AI Communication and Deployment Pipeline
Module 10: AI Communication and Deployment Pipeline
10.1 Communicating AI Results Effectively to Non-Technical Stakeholders
10.2 Building a Deployment Pipeline for AI Models
10.3 Developing Prototypes Based on Client Requirements
10.4 Hands-on: Deployment
Module 10: AI Communication and Deployment Pipeline
Quiz
Lab Practice 10.1
Python File Download
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