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Module 1: Foundations of Artificial Intelligence
1.0 Introduction: Foundations of Artificial Intelligence
1.1 What is AI?
1.2 Artificial Intelligence based on Capabilities
1.3 Artificial Intelligence Based on Functionalities
1.4 Method Based - Machine Learning, Deep Learning, Fuzzy Logic, Generative AI
1.5 Application Based - Computer Vision, NLP, Robotics, Expert Systems
1.6 Summary: Foundations of Artificial Intelligence
Module 1: E-Book
Quiz
Module 2: Mathematical Concepts for AI
2.0 Introduction: Mathematical Concepts for AI
2.1 Vectors and Matrices: Basics and Operations
2.2 Eigenvalues, Eigenvectors, and Linear Transformations
2.3 Determinants
2.4 Probability distributions
2.5 Hypothesis testing
2.6 Bayesian inference
2.7 Summary: Mathematical Concepts for AI
Module 2: E-Book
Quiz
Module 3: Python for Developer
3.0 Introduction: Python for Developer
3.1 Installing Anaconda for Python
3.2 Basic Syntax
3.3 Control Flow
3.4 Data Structures
3.5 Summary: Python for Developer
Module 3: E-Book
Lab Practice 3.1
Lab Practice 3.2
Lab Practice 3.3
Lab Practice 3.4
Lab Practice 3.5
Lab Practice 3.6
Lab Practice 3.7
Python File Download
Quiz
Module 4: Mastering Machine Learning
4.0 Introduction: Mastering Machine Learning
4.1 Understanding Machine Learning
4.2 Types of Machine Learning
4.3 Regression
4.4 Stock Prediction and Classification Techniques
4.5 K-Means and Hierarchical Clustering Overview
4.6 Dimensionality Reduction
4.7 Summary: Mastering Machine Learning
Module 4: E-Book
Lab Practice 4.1
Lab Practice 4.2
Lab Practice 4.3
Lab Practice 4.4
Lab Practice 4.5
Lab Practice 4.6
Lab Practice 4.7
Lab Practice 4.8
Lab Practice 4.9
Python File Download
Quiz
Module 5: Deep Learning
5.0 Introduction: Deep Learning
5.1 Essentials of Deep Learning: Theory and Frameworks
5.2 Deep Learning Applications and Case Studies
5.3 Significance of Perceptron
5.4 Significance of Activation Functions
5.5 Feed Forward Networks
5.6 Deep Learning Frameworks
5.7 Summary: Deep Learning
Module 5: E-Book
Lab Practice 5.1
Lab Practice 5.2
Lab Practice 5.3
Lab Practice 5.4
Lab Practice 5.5
Python File Download
Quiz
Module 6: Computer Vision
6.0 Introduction: Computer Vision
6.1 Introduction to Image Processing: Basics and Techniques
6.2 Object Detection Process
6.3 Object Detection Techniques
6.4 Region Proposal Methods
6.5 Types of Image Segmentation
6.6 Techniques for Image Segmentation
6.7 Applications of Image Segmentation
6.8 U-Net
6.9 Summary: Computer Vision
Module 6: E-Book
Lab Practice 6.1
Lab Practice 6.2
Lab Practice 6.3
Lab Practice 6.4
Lab Practice 6.5
Python File Download
Quiz
Module 7: Natural Language Processing
7.0 Introduction: Natural Language Processing
7.1 Text Preprocessing and Representation.
7.2 Key Components of Text Classification and Its challenges
7.3 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
Python File Download
Quiz
Module 8: Reinforcement Learning
8.0 Introduction: Reinforcement Learning
8.1 Key Components of Reinforcement Learning Include
8.2 Agents, Environments, Rewards, Actions, and States
8.3 Applications for Reinforcement Learning
8.4 Summary: Reinforcement Learning
Module 8: E-Book
Lab Practice 8.1
Lab Practice 8.2
Lab Practice 8.3
Python File Download
Quiz
Module 9: Cloud Computing in AI Development
9.0 Introduction: Cloud Computing in AI Development
9.1 Definition of Cloud Computing
9.2 Importance of Cloud Computing in AI
9.3 Integration of Cloud and AI
9.4 Challenges and Solutions in Cloud Computing for AI Development
9.5 Summary: Cloud Computing in AI Development
Module 9: E-Book
Lab Practice 9.1
Python File Download
Quiz
Module 10: Large Language Models
10.0 Introduction: Large Language Models
10.1 Architecture of Large Language Models (LLMs)
10.2 Training LLMs
10.3 Applications for LLMs
10.4 Bias and fairness in Large Language Models (LLMs)
10.5 Requirement Resources for LLMs
10.6 Summary: Large Language Models
Module 10: E-Book
Lab Practice 10.1
Lab Practice 10.2
Python File Download
Quiz
Module 11: Cutting-Edge AI Research
11.0 Introduction: Cutting-Edge AI Research
11.1 Fundamentals of Neuro-Symbolic AI
11.2 Symbolic reasoning
11.3 Combining symbolic reasoning and deep learning
11.4 Introduction to Federated Learning
11.5 Importance of Federated Learning
11.6 Meta Learning
11.7 Few-shot learning
11.8 Summary: Cutting-Edge AI Research
Module 11: E-Book
Lab Practice 11.1
Python File Download
Quiz
Module 12: AI Communication and Documentation
12.0 Introduction: AI Communication and Documentation
12.1 Introduction to AI Communication and Documentation
12.2 Objectives of AI Communication and Documentation
12.3 Presenting to technical and non-technical audiences
12.4 Summary: AI Communication and Documentation
Module 12: E-Book
Quiz
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AI+ Developer™ Self-Paced Learning
