Lesson 1: What is AI?

• • Overview of Artificial Intelligence
  • History and Evolution of AI

Lesson 2: Types of AI

• • Narrow AI vs General AI
  • Machine Learning, Deep Learning, and Neural Networks

Lesson 3: Applications of AI

• • AI in Various Industries
  • Case Studies and Real-World Applications

Lesson 1: Tools and Libraries for AI Development

• • Python, Jupyter Notebooks, Anaconda
  • Key Libraries: NumPy, Pandas, Matplotlib, Scikit-learn

Lesson 2: Installing and Configuring AI Tools

• • Setting Up the Development Environment
  • Introduction to Cloud-Based AI Platforms (e.g., Google Colab, AWS SageMaker)

Lesson 1: Python Basics

• • Variables, Data Types, and Control Structures
  • Functions, Modules, and Packages

Lesson 2: Data Manipulation with Pandas

• • DataFrames, Series, and Indexing
  • Data Cleaning, Transformation, and Visualization

Lesson 1: Introduction to Machine Learning

• • Supervised vs Unsupervised Learning
  • Key Concepts: Training, Testing, and Validation

Lesson 2: Supervised Learning Algorithms

• • Linear Regression, Logistic Regression
  • Decision Trees, Random Forests, Support Vector Machines

Lesson 3: Unsupervised Learning Algorithms

• • Clustering: K-means, Hierarchical Clustering
  • Dimensionality Reduction: PCA, LDA

Lesson 1: Introduction to Neural Networks

• • Understanding Neurones and Layers
  • Activation Functions and Loss Functions

Lesson 2: Building Neural Networks with TensorFlow and Keras

• • Setting Up TensorFlow and Keras
  • Creating, Training, and Evaluating Neural Networks

Lesson 3: Convolutional Neural Networks (CNNs)

• • CNN Architecture and Applications
  • Implementing CNNs for Image Classification

Lesson 4: Recurrent Neural Networks (RNNs)

• • RNN Architecture and Applications
  • Implementing RNNs for Sequence Data

Lesson 1: Introduction to NLP

• • Key Concepts and Techniques
  • Text Preprocessing and Tokenization

Lesson 2: NLP with Python

• • Using NLTK and spaCy
  • Sentiment Analysis and Text Classification

Lesson 3: Advanced NLP Techniques

• • Word Embeddings and Word2Vec
  • Transformers and BERT

Lesson 1: Model Deployment and Monitoring

• • Saving and Loading Models
  • Deploying AI Models with Flask and FastAPI

Lesson 2: Scaling AI Solutions

• • Using Cloud Platforms for Deployment
  • Monitoring and Maintaining AI Models

Lesson 1: Understanding AI Ethics

• • Bias and Fairness in AI
  • Privacy and Security Considerations

Lesson 2: Responsible AI Development

• • Best Practices for Ethical AI
  • Tools and Frameworks for Responsible AI

Project Planning and Implementation

• • Developing an End-to-End AI Solution
  • Integrating Machine Learning and Deep Learning Models
  • Deploying the AI Solution to Production

Course Review and Q&A

• • Final Examination
  • Certification and Career Guidance