AI With Python Tutorial

This AI with Python tutorial covers the fundamental and advanced artificial intelligence (AI) concepts using Python. Whether we're a complete beginner or an experienced professional this tutorial will help us to learn AI step by step.

Why Python for AI?

Python is the go-to language for AI due to:

1. Clear Syntax: Simple and easy to learn.
2. Rich Ecosystem: Libraries like TensorFlow, PyTorch and Scikit-learn.
3. Strong Community Support: Large, active AI community.

A solid foundation in Python is important for learning Artificial Intelligence and for more details related to Python, refer to: Python tutorial

Artificial Intelligence(AI)

Artificial Intelligence (AI) help machines to perform tasks requiring human intelligence such as problem-solving, decision-making and image generation. Its key subsets include machine learning, deep learning, NLP, computer vision, robotics and generative AI.

To build complex AI models, we use Python frameworks like:

• Artificial Intelligence (AI)
• TensorFlow
• PyTorch
• Scikit-Learn

These frameworks provide flexibility and scalability for building intelligent solutions across applications.

Knowledge Representation in AI

1. It refers to how information is structured, stored and used by systems to reason and make decisions. Key techniques include:

• Semantic Networks
• Ontologies
• Logical Representation

2. First Order Logic (FOL) is used to represent knowledge and reason about relationships between objects and their properties.

• First Order Logic (FOL)

3. Reasoning in Artificial Intelligence (AI) involves drawing conclusions or making decisions from existing information. Types of reasoning include:

• Reasoning in Artificial Intelligence (AI)
• Deductive Reasoning
• Inductive Reasoning
• Abductive Reasoning
• Fuzzy Reasoning
• Artificial Intelligence Tutorial

Machine learning(ML)

Machine learning help developers to create algorithms and models that allow computers to learn and make predictions or decisions without explicit programming. There are three types of machine learning techniques:

1. Supervised Learning

In supervised learning, the algorithm is trained on a labeled dataset where each input is paired with its corresponding output. It has two types of algorithms:

1. Regression Algorithms:

• Supervised learning
• Linear Regression
• Gradient Descent in Linear Regression
• Multiple Linear Regression
• Ridge Regression
• Lasso regression
• Elastic net Regression

2. Classification Algorithm:

• Logistic Regression
• Decision trees
• Ensemble Classifiers
• Support Vector Machines (SVM)
• k-Nearest Neighbors (KNN)
• Naive Bayes
• Random Forest

2. Unsupervised Learning

In Unsupervised learning, the algorithm works with unlabeled data to identify hidden patterns or groups within it without any specific guidance on what to look for.

1. Clustering Algorithms:

• Unsupervised learning
• K-Means clustering
• K-Mode clustering
• Fuzzy C-Means (FCM) Clustering
• Hierarchical clustering
• Agglomerative Clustering
• Divisive clustering
• DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

2. Association Algorithms:

• Apriori Algorithm
• FP-Growth (Frequent Pattern-Growth)
• ECLAT

3. Dimensionality Reduction:

• Principal Component Analysis (PCA)
• Isomap

3. Reinforcement Learning

In Reinforcement learning, the algorithm learns by interacting with the environment and receiving rewards or penalties to maximize long-term rewards.

Reinforcement Learning algorithms are:

• Reinforcement learning
• Markov decision processes (MDPs)
• Bellman equation
• Q-Learning
• Monte Carlo Methods
• Machine Learning Tutorial

ML struggles with features needing manual work, high-dimensional data and complex patterns that’s where deep learning comes in.

Deep Learning(DL)

Deep learning is inspired by the human brain's structure where billions of neurons communicate through signals.

Introduction to Neural Networks

Neural Networks are fundamentals of deep learning inspired by human brain. It consists of layers of interconnected nodes called "neurons".

Components of Neural Networks:

• Neural Networks
• Neurons
• Layers in Neural Networks
• Weights and Biases
• Forward Propagation
• Activation Functions
• Loss Functions
• Backpropagation
• Learning Rate

Hyperparameters:

• Epochs
• Batch Size

Deep Learning Architecture

Below are foundational structures in deep learning:

• Perceptron
• Single Layer Perceptron
• Multi-Layer Perceptron
• Artificial Neural Networks (ANNs)
• Feedforward Neural Networks (FNN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM) networks
• Gated Recurrent Units Networks (GRU)

Optimization Algorithm in Deep Learning

The most common Algorithm are:

• Gradient Descent
• Stochastic Gradient Descent (SGD)
• Mini-batch Gradient Descent
• Regularization

For more details refer to: Deep Learning Tutorial

Natural Language Processing (NLP)

Natural language processing helps machines to understand, interpret and generate human-like text which helps in allowing for seamless communication.

Key tasks include:

• Natural language processing
• Tokenization
• Stop Words Removal
• Stemming
• Lemmatization
• Part-of-Speech (POS) Tagging
• Named Entity Recognition
• Bag-of-Words (BoW)
• Word Embeddings
• TF- IDF (Term Frequency-inverse Document Frequency)
• Doc2Vec
• Sentiment Analysis

For more details refer to: Natural Language Processing tutorial

Computer Vision

Computer Vision enables machines to interpret, analyze and understand visual information from the world much like the human visual system.

Image Processing and Transformation

• Computer Vision
• Image Transformation
• Image Enhancement
• Noise Reduction Techniques
• Edge Detection
• Smoothing and Blurring Image
• Image Denoising

Feature Extraction

• Canny Edge Detector
• Sobel Operator
• SIFT (Scale-Invariant Feature Transform)
• SURF (Speeded-Up Robust Features)
• HOG (Histogram of Oriented Gradients)
• Laplacian of Gaussian (LoG)

Deep Learning for Computer Vision

Deep learning has revolutionized the field of computer vision by enabling machines to understand and interpret visual data.

• Deep learning
• Convolutional Neural Networks
• Generative Adversarial Networks (GANs)
• Variational Autoencoders (VAEs)
• Vision Transformers (ViT)

For more details refer to: Computer Vision Tutorial

Generative AI

Generative AI refers to creative models that generate new content such as images, text, audio or other data forms.

• Transformers
• GPT (Generative Pre-trained Transformer)
• BERT (Bidirectional Encoder Representations from Transformers)
• T5 (Text-to-Text Transfer Transformer)
• Conditional GAN (cGAN)
• CycleGAN
• Style GANs

Fine-tuning Large Language Models Technique

• Reinforcement Learning from Human Feedback (RLHF)
• Fine Tuning LLMs using PEFT
• Fine Tuning LLMs using QLoRA
• Prompt Tuning Techniques
• Instruction Tuning
• Supervised Fine-Tuning (SFT)

For more details refer to: Roadmap to Generative AI

We've covered the AI tutuorial and seen ML, DL, computer vision (CV), generative AI and NLP using Python which is an important for developing intelligent systems, offering the perfect balance of simplicity and capability.