Artificial Intelligence - AI
Artificial Intelligence - AI
Artificial Intelligence - AI Course Curriculum
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