Certainly! Here’s a comprehensive outline of points to cover and tools to include in a full course on Artificial Intelligence for beginners:

Part 1: Introduction to Artificial Intelligence

1. Definition and significance of AI
2. Historical overview of AI development
3. Types of AI: Narrow AI vs. General AI
4. The role of AI in various industries

Part 2: Basics of Machine Learning

1. Introduction to Machine Learning (ML)
2. Supervised, unsupervised, and reinforcement learning
3. Machine learning algorithms: Linear regression, logistic regression, decision trees, SVM, k-nearest neighbors, etc.
4. Model evaluation and validation techniques
5. Introduction to Python programming language for ML

Part 3: Deep Learning Fundamentals

1. Introduction to Deep Learning (DL)
2. Neural networks architecture: Perceptrons, multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs)
3. Deep learning libraries: TensorFlow, Keras, PyTorch
4. Training and fine-tuning neural networks
5. Hands-on projects with deep learning frameworks

Part 4: Natural Language Processing (NLP)

1. Introduction to NLP and its applications
2. Text preprocessing techniques: Tokenization, stemming, lemmatization
3. Sentiment analysis, text classification, and named entity recognition
4. Word embeddings and deep learning for NLP
5. Building NLP applications using libraries like NLTK, SpaCy, and Gensim

Part 5: Computer Vision

1. Introduction to computer vision and its applications
2. Image preprocessing techniques: Filtering, edge detection, resizing
3. Object detection, image classification, and image segmentation
4. Convolutional neural networks (CNNs) for computer vision tasks
5. Hands-on projects with image processing and computer vision libraries like OpenCV and TensorFlow Object Detection API