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AI+ Developer™ 2.0
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Module 1: Foundations of Artificial Intelligence
1.1 Introduction: Foundations of Artificial Intelligence
1.2 Topic 1.1 Introduction: Introduction to AI
1.3 History
1.4 What is AI?
1.5 Topic 1.2 Introduction: Types of Artificial Intelligence
1.6 Artificial Intelligence based on Capabilities
1.7 Artificial Intelligence Based on Functionalities
1.8 Topic 1.3 Introduction: Branches of Artificial Intelligence
1.9 Method Based - Machine Learning, Deep Learning, Fuzzy Logic, Generative AI
1.10 Application Based - Computer Vision, NLP, Robotics, Expert Systems
1.11 Topic 1.4 Introduction: Applications and Business Use Cases
1.12 Healthcare
1.13 Retail
1.14 Finance
1.15 Marketing
1.16 Summary: Foundations of Artificial Intelligence
Module 1: E-Book
Quiz
Module 2: Mathematical Concepts for AI
2.1 Introduction: Mathematical Concepts for AI
2.2 Topic 2.1 Introduction: Linear Algebra
2.3 Vectors and Matrices: Basics and Operations
2.4 Eigenvalues, Eigenvectors, and Linear Transformations
2.7 Determinants
2.8 Topic 2.2 Introduction: Calculus
2.9 Derivatives and partial derivatives
2.10 Gradients
2.11 Optimization techniques
2.12 Integration
2.13 Topic 2.3 Introduction: Probability and Statistics
2.14 Probability distributions
2.15 Hypothesis testing
2.16 Bayesian inference
2.17 Topic 2.4 Introduction: Discrete Mathematics
2.18 Sets and logic
2.19 Graph theory
2.20 Combinatorics
2.21 Summary: Mathematical Concepts for AI
Module 2: E-Book
Quiz
Module 3: Python for Developer
3.1 Introduction: Python for Developer
3.2 Topic 3.1 Introduction: Python: A Powerful and Versatile Foundation for AI
3.3 Installing Anaconda for Python
3.4 Topic 3.2 Introduction: Python Fundamentals
3.5 Basic Syntax
3.6 Control Flow
3.7 Data Structures
3.8 Modules and Packages
3.9 Topic 3.3 Introduction: Python Libraries
3.10 NumPy: The Numerical Workhorse
3.11 Pandas: The Data Wrangler
3.12 Matplotlib and Seaborn
3.13 Summary: AI Communication and Documentation
Module 3: E-Book
Quiz
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
Module 4: Mastering Machine Learning
4.1 Introduction: Mastering Machine Learning
4.2 Topic 4.1 Introduction: History of Machine Learning
4.3 The Early History of Machine Learning (Pre-1940)
4.4 Machine Learning at Present
4.5 Topic 4.2 Introduction: Introduction to Machine Language
4.6 Functions of Machine Learning Systems = Understanding Machine Learning: Definition, Scope, and Functions of Machine Learning Systems
4.7 Scope of Machine Learning
4.8 Types of Machine Learning
4.9 Supervised Learning
4.10 Unsupervised Learning
4.11 Reinforcement Learning
4.12 Key Concepts in Reinforcement Learning
4.13 Topic 4.3 Introduction: Algorithms and Approaches in Reinforcement Learning
4.14 Algorithms
4.15 Challenges and Limitations of Reinforcement Learning
4.16 Applications of Reinforcement Learning
4.17 Key Terminologies
4.18 Overview of the Machine Learning Lifecycle
4.19 Topic 4.4 Introduction: Supervised Machine Learning Algorithms
4.20 Regression
4.21 Stock Prediction and Classification Techniques
4.23 Hands-on: Image Classification for Cat vs. Dog, Spam Email Detection, and Sentiment Analysis
4.24 Topic 4.5 Introduction: Unsupervised Machine Learning Algorithms
4.25 K-Means and Hierarchical Clustering Overview
4.27 Hands-on: Customer Segmentation, Market Research
4.28 Dimensionality Reduction
4.29 Hands-on: Visualizing High-Dimensional Data for Easy Analysis
4.30 Topic 4.6 Introduction: Model Evaluation and Selection
4.31 Metrics for Regression and Classification
4.32 Model Comparison and Cross-Validation
4.33 Hands-on: Evaluating and choosing the best model for your project
4.34 Summary: Mastering Machine Learning
Module 4: E-Book
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
Lab Practice 4.7
Lab Practice 4.8
Lab Practice 4.9
Python File Download
Module 5: Deep Learning
5.1 Introduction: Deep Learning
5.2 Topic 5.1 Introduction: Module
5.3 Fundamentals of Deep Learning And Deep Learning Frameworks and Libraries
5.5 Deep Learning Applications and Case Studies
5.6 Topic 5.2 Introduction: Neural Networks
5.7 Perceptron
5.8 Activation Functions
5.9 Feed Forward Networks
5.10 Deep Learning Frameworks
5.11 Hands-on: Building a Simple Neural Network to classify Handwritten Digits
5.12 Topic 5.3 Introduction: Convolutional Neural Networks (CNNs)
5.13 LeNet
5.14 AlexNNet
5.15 VGG
5.16 ResNet (Residual Network)
5.17 Hands-on: Image Classification for a Custom Dataset (e.g., flowers, animals, clothing items)
5.18 Topic 5.4 Introduction: Recurrent Neural Networks (RNNs)
5.19 Long Short-Term Memory (LSTM)
5.20 Gated Recurrent Units (GRUs)
5.21 Applications of Recurrent Neural Networks (RNNs) and Variants for Sequential Data
5.22 Hands-on: Text generation, Sentiment Analysis, and Machine Translation
5.23 Summary: Deep Learning
Module 5: E-Book
Quiz
Lab Practice 5.1
Lab Practice 5.2
Lab Practice 5.3
Lab Practice 5.4
Lab Practice 5.5
Python File Download
Module 6: Computer Vision
6.1 Introduction: Computer Vision
6.2 Image Representation, Filtering, and Transformations = Introduction to Image Processing: Basics and Techniques
6.3 Hands-on: Image Manipulation and Enhancement
6.4 Topic 6.2 Introduction: Object Detection
6.5 Object Detection Process
6.6 Object Detection Techniques
6.7 Region Proposal Methods
6.8 Topic 6.3 Introduction: Image Segmentation
6.9 Types of Image Segmentation
6.10 Techniques for Image Segmentation
6.11 Applications of Image Segmentation
6.12 U-Net
6.13 Hands-on: Medical image segmentation and autonomous driving tasks
6.14 Topic 6.4 Introduction: Generative Adversarial Networks (GANs)
6.15 Components, Training, and Applications of GANs
6.18 Challenges and Variants of GANs
6.20 Hands-on: Generating Realistic Images or Transforming Styles
6.21 Summary: Computer Vision
Module 6: E-Book
Quiz
Lab Practice 6.1
Lab Practice 6.2
Lab Practice 6.3
Lab Practice 6.4
Lab Practice 6.5
Python File Download
Module 7: Natural Language Processing
7.1 Introduction: Natural Language Processing
7.2 Topic 7.1 Introduction: Text Preprocessing and Representation
7.3 Text Preprocessing and Representation.
7.5 Hands-on: Cleaning and Preparing Text Data for NLP Tasks
7.6 Topic 7.2 Introduction: Text Classification
7.7 Key Components of Text Classification and Its challenges
7.9 Sentiment Analysis
7.10 Topic Modelling
7.11 Spam Detection
7.12 Hands-on: Building a Sentiment Analyzer for Social Media Posts
7.13 Topic 7.3 Introduction: Named Entity Recognition (NER)
7.14 Key Components of Named Entity Recognition
7.15 Identifying people, places, organizations
7.16 Hands-on: Extracting key information from News Articles or Legal Documents
7.17 Topic 7.4 Introduction: Question Answering (QA)
7.18 BERT Question-Answering Systems
7.19 T5 (Text-To-Text Transfer Transformer)
7.20 Hands-on: Building a Simple QA System
7.21 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
Python File Download
Module 8: Reinforcement Learning
8.1 Introduction: Reinforcement Learning
8.2 Topic 8.1 Introduction: Introduction to Reinforcement Learning
8.3 Key Components of Reinforcement Learning Include
8.4 Agents, Environments, Rewards, Actions, and States
8.5 Applications for Reinforcement Learning
8.6 Challenges for Reinforcement Learning
8.7 Hands-on: Building a Simple Game Environment for RL Experimentation
8.8 Topic 8.2 Introduction: Q-Learning and Deep Q-Networks (DQNs)
8.9 Q-Learning
8.10 Deep Q-Networks (DQNs)
8.11 Applications and Considerations
8.12 Hands-on: Training an AI to Play Atari Games or Navigate a Maze
8.13 Training an AI to Navigate a Maze
8.14 Topic 8.3 Introduction: Policy Gradient Methods
8.15 Policy-based Reinforcement Learning (RL)
8.16 REINFORCE Algorithm
8.17 Actor-Critic Methods
8.18 Hands-on: Training a robot to perform a task with continuous control
8.19 Summary: Reinforcement Learning
Module 8: E-Book
Quiz
Lab Practice 8.1
Lab Practice 8.2
Lab Practice 8.3
Python File Download
Module 9: Cloud Computing in AI Development
9.1 Introduction: Cloud Computing in AI Development
9.2 Topic 9.1 Introduction: Cloud Computing for AI
9.3 Definition of Cloud Computing
9.4 Overview of Artificial Intelligence (AI) Development
9.5 Importance of Cloud Computing in AI
9.6 Fundamentals of Cloud Computing
9.7 Integration of Cloud and AI
9.8 Benefits of Cloud Computing in AI Development
9.9 Challenges and Solutions in Cloud Computing for AI Development
9.10 Use Cases and Applications of Cloud Computing in AI Development
9.11 Best Practices for Cloud-Based AI Development
9.12 Hands-on: Setting up a cloud-based AI development environment
9.13 Topic 9.2 Introduction: Cloud-Based Machine Learning Services
9.14 Pre-trained Models and Auto ML
9.15 Hands-on: Building an AI Application using Cloud Services
9.16 Summary: Cloud Computing in AI Development
Module 9: E-Book
Quiz
Lab Practice 9.1
Python File Download
Module 10: Large Language Models
10.1 Introduction: Large Language Models
10.2 Topic 10.1 Introduction: Understanding LLMs
10.3 Architecture of Large Language Models (LLMs)
10.4 Training LLMs
10.5 Applications for LLMs
10.6 Large Language Models (LLMs) Variants
10.7 Bias and fairness in Large Language Models (LLMs)
10.8 Privacy and security in LLMs
10.9 Requirement Resources for LLMs
10.10 Open source Tools and Libraries
10.11 Hands-on: Exploring LLM capabilities through open-source examples
10.12 Topic 10.2 Introduction: Text Generation and Translation
10.13 Text Generation
10.14 Text Translation
10.15 Creative text formats and language translation
10.16 Hands-on: Generating different text styles or translating between languages
10.17 Multimodal Large Language Models (LLMs)
10.18 Topic 10.3 Introduction: Question Answering and Knowledge Extraction
10.19 Information retrieval and knowledge base construction
10.20 Hands-on: Building a knowledge base or question-answering system using LLMs
10.21 Summary: Large Language Models
Module 10: E-Book
Quiz
Lab Practice 10.1
Lab Practice 10.2
Python File Download
Module 11: Cutting-Edge AI Research
11.1 Introduction: Cutting-Edge AI Research
11.2 Topic 11.1 Introduction: Neuro-Symbolic AI
11.3 Fundamentals of Neuro-Symbolic AI
11.4 Symbolic reasoning
11.5 Combining symbolic reasoning and deep learning
11.6 Topic 11.2 Introduction:Explainable AI (XAI)
11.7 Overview of Explainable AI (XAI)
11.8 Significance, challenges, and emerging trends in XAI
11.9 Interpreting AI models and building trust
11.10 Topic 11.3 Introduction: Federated Learning
11.11 Introduction to Federated Learning
11.12 Importance of Federated Learning
11.13 Privacy-preserving collaborative learning
11.14 Topic 11.4 Introduction: Meta-Learning and Few-Shot Learning
11.15 Meta Learning
11.16 Importance of Meta Learning
11.17 Implementation for Meta Learning
11.18 Few-shot learning
11.19 Challenges Addressed by Few-Shot Learning
11.20 Approaches in Few-Shot Learning
11.21 Applications of Few-Shot Learning
11.22 Challenges in Few-Shot Learning
11.23 Implementation for few-shot learning
11.24 Summary: Cutting-Edge AI Research
Module 11: E-Book
Quiz
Lab Practice 11.1
Python File Download
Module 12: AI Communication and Documentation
12.1 Introduction: AI Communication and Documentation
12.2 Topic 12.1 Introduction: Communicating AI Projects
12.3 Introduction to AI Communication and Documentation
12.4 Challenges in AI Communication and Documentation
12.5 Objectives of AI Communication and Documentation
12.6 Presenting to technical and non-technical audiences
12.7 Hands-on: Preparing a presentation or blog post about an AI project
12.8 Topic 12.2 Introduction: Documenting AI Systems
12.9 Code Documentation
12.10 Model explanations
12.11 Hands-on: Writing Clear and Concise Documentation for an AI Model
12.12 Topic 12.3 Introduction: Ethical Considerations
12.13 Bias, Fairness, Transparency, and Accountability
12.14 Hands-on: Evaluating an AI system for ethical considerations
12.15 Summary: AI Communication and Documentation
Module 12: E-Book
Quiz
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AI+ Developer™ 2.0