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AI+ Engineer™ Self-Paced Learning
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Module 1: Foundations of Artificial Intelligence
1.0 Introduction: Foundations of Artificial Intelligence
1.1 What is AI?
1.2 Mastering Machine Learning: Applications and Innovations And Machine Learning Fundamentals
1.3 Introduction to Deep Learning
1.4 Data: The Fuel of AI
1.5 Navigating Ethical AI: Bias, Transparency, and Accountability
1.6 Privacy and Data Governance
1.7 Summary: Foundations of Artificial Intelligence
Module 01: E-Book
Quiz
Module 2: Introduction to AI Architecture
2.0 Introduction: Introduction to AI Architecture
2.1 Historical Perspective
2.2 Contemporary Applications Across Industries
2.3 Key Components and Structures
2.4 Role in Solving Real-World Problems
2.5 AI Lifecycle Development: From Planning to Monitoring
2.6 Best Practices in Each Phase
2.7 Summary: Introduction to AI Architecture
Module 02: E-Book
Lab Practice 2.1
Lab Practice 2.2
Lab Practice 2.3
Lab Practice 2.4
Python File Download
Quiz
Module 3: Fundamentals of Neural Networks
3.0 Introduction: Fundamentals of Neural Networks
3.1 Neurons, Layers, and Architectures
3.2 Feedforward and Backpropagation Concepts
3.3 Common Activation Functions
3.4 Importance in Shaping the Network's Behaviour
3.5 Understanding the Backpropagation Process
3.6 Popular Optimization Algorithms (Gradient Descent, Adam, RMSprop)
3.7 Summary: Fundamentals of Neural Networks
Module 03: E-Book
Lab Practice 3.1
Lab Practice 3.2
Lab Practice 3.3
Python File Download
Quiz
Module 4: Applications of Neural Networks
4.0 Introduction: Applications of Neural Networks
4.1 Understanding How Neural Networks Process Images
4.2 Real-world Applications in Image Recognition and Computer Vision
4.3 Introduction to Handling Sequential Data Using Neural Networks
4.4 Applications in Natural Language Processing and Time Series Analysis
4.5 Summary: Applications of Neural Networks
Module 04: E-Book
Lab Practice 4.1
Lab Practice 4.2
Lab Practice 4.3
Python File Download
Quiz
Module 5: Significance of Large Language Models (LLM)
5.0 Introduction: Significance of Large Language Models (LLM)
5.1 Explore Large Language Models (LLMs)
5.2 Understanding the Role of LLMs in Natural Language Understanding
5.3 Implications for Various Practical Applications
5.4 Overview of Widely Used Large Language Models (BERT, GPT, and Others)
5.5 Unique Features and Use Cases in Real-world Scenarios Examine
5.6 Techniques for Effective Finetuning of Language Models
5.7 Summary: Significance of Large Language Models (LLM)
Module 05: E-Book
Lab Practice 5.1
Lab Practice 5.2
Python File Download
Quiz
Module 6: Application of Generative AI
6.0 Introduction: Application of Generative AI
6.1 Understanding the Basic Concept and Structure of GANs
6.2 Real-world Applications in Image Generation and Data Augmentation
6.3 Understanding VAEs and Their Applications for Generative Tasks
6.4 Use Cases in Image Synthesis and Data Representation
6.5 Practical Techniques for Creating Synthetic Data
6.6 Addressing Challenges Related to Data Scarcity in Practical Scenarios
6.7 Summary: Application of Generative AI
Module 6: E-Book
Lab Practice 6.1
Lab Practice 6.2
Python File Download
Quiz
Module 7: Natural Language Processing
7.0 Introduction: Natural Language Processing
7.1 Practical Applications of NLP in Various Industries
7.2 Real-world Scenarios in Sentiment Analysis, Chatbots, and Language Translation
7.3 Attention Mechanisms and Practical Use of Transformers
7.4 In-depth Understanding of BERT for Practical NLP Tasks
7.5 Practical Insights into BERT (Bidirectional Encoder Representations from Transformers)
7.6 Summary: Natural Language Processing
Module 7: E-Book
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
Quiz
Module 8: Transfer Learning with Hugging Face
8.0 Introduction: Transfer Learning with Hugging Face
8.1 Principles and Advantages of Transfer Learning
8.2 Applications in Various Domains
8.3 Effective Strategies and Approaches in Transfer Learning
8.4 Choosing the Right Strategy for Specific Tasks
8.5 Summary: Transfer Learning with Hugging Face
Module 8: E-Book
Lab Practice 8.1
Python File Download
Quiz
Module 9: Crafting Sophisticated GUIs for AI Solutions
9.0 Introduction: Crafting Sophisticated GUIs for AI Solution
9.1 Various Ways for Implementing GUI
9.2 Streamlit: A Python Library for Interactive Web Applications
9.3 Dash (Plotly): Creating Interactive Web-based Dashboards with Python
9.4 Building Desktop Applications with Python's Tkinter Framework
9.5 PyQt and PySide (Python): Python Bindings for the Qt Framework
9.6 Electron (JavaScript, HTML, CSS): Cross-platform Desktop Applications
9.7 Summary: Crafting Sophisticated GUIs for AI Solution
Module 9: E-Book
Lab Practice 9.1
Lab Practice 9.2
Lab Practice 9.3
Lab Practice 9.4
Python File Download
Quiz
Module 10: AI Communication and Deployment Pipeline
10.0 Introduction: AI Communication and Deployment Pipeline
10.1 Strategies for Clear and Concise Communication
10.2 Visualizations and Storytelling with AI Results
10.3 Key Components of a Robust Deployment Pipeline
10.4 Continuous Integration and Continuous Deployment (CI/CD) Practices
10.5 Understanding Client Needs and Expectations and Prototyping Approaches and Methodologies
10.6 Summary: AI Communication and Deployment Pipeline
Module 10: E-Book
Quiz
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AI+ Engineer™ Self-Paced Learning