✅ AI Learning Path – Beginner to Advanced

Embark on your AI learning journey with this structured path. 🌱

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📚 Introduction to AI

• What is AI? : Learn the basics of Artificial Intelligence.
• History of AI : Explore the timeline and evolution of AI technology.
• AI in the Real World : Understand how AI is applied in everyday life.

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🧩 Core Branches of AI

Explore the major subfields of AI:

• Rule-Based Systems 💡: A system that makes decisions based on predefined "if-then" rules and logical reasoning.

• Symbolic AI 🔢 : An approach to AI that represents knowledge through symbols and logical rules, mimicking human-like reasoning.

• Machine Learning (ML) 🤖 : A subset of AI where machines learn patterns from data and improve their performance without explicit programming.

• Natural Language Processing (NLP) 🗣 : A branch of AI that focuses on enabling computers to understand, interpret, and generate human language.

• Computer Vision 👁️ : An AI field that enables machines to interpret and make decisions based on visual data from the world, such as images or videos.

• Robotics 🤖: The design and creation of robots that can perform tasks autonomously or semi-autonomously using AI techniques.

• Planning & Reasoning 🧩: AI techniques that allow systems to plan actions and make logical decisions based on goals, constraints, and environments.

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🤖 Machine Learning Basics

• What is Machine Learning? Understand its core principles.
• ML vs Traditional Programming: Learn the difference between ML and conventional programming methods.

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🔍 ML Types & Concepts

Machine Learning (ML) can be categorized into different types based on how the model learns from data.

Each type serves a unique purpose in solving real-world problems.

• Supervised Learning – Learning from labeled data.
• Unsupervised Learning – Discover patterns in unlabeled data.
• Reinforcement Learning – Learn by trial and error (think of training a pet!).
• Self-Supervised / Semi-Supervised Learning – Learn with minimal labels.

📌 These types form the backbone of ML — choosing the right one depends on the problem, data, and desired outcome.

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⚙️ Core ML Algorithms

Familiarize yourself with popular machine learning algorithms:

• Linear Regression – Predict continuous values.
• Logistic Regression – Binary classification tasks.
• Decision Trees – Visualize decisions.
• k-Nearest Neighbors (k-NN) – Classification based on proximity.
• Support Vector Machines (SVM) – Classification and regression tasks.
• K-Means / Clustering – Group similar items.
• Q-Learning / SARSA – Reinforcement learning algorithms.

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🧠 Deep Learning Basics

• What is Deep Learning? Dive into the subfield of ML based on artificial neural networks.
• Difference Between ML and DL: Understand how deep learning is a specialized subset of machine learning.

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🕸️ Neural Network Types

Get to know various types of neural networks:

• Feedforward Neural Networks (FNN) – Simple structure for basic tasks.
• Convolutional Neural Networks (CNN) – Used in image processing.
• Recurrent Neural Networks (RNN) – Time series & sequential data.
• Long Short-Term Memory (LSTM) – Advanced RNN for handling long-term dependencies.

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🏗️Key Architectures In Deep Learning

Key architectures in deep learning:

• Transformers – Revolutionized NLP and language models.
• Autoencoders – Used for unsupervised learning and anomaly detection.
• Generative Adversarial Networks (GANs) – Generate realistic data (images, videos).
• Variational Autoencoders (VAEs) – Generate new data points, especially in image and video generation.

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🗣️ Natural Language Processing (NLP)

Learn NLP techniques used in text processing:

• Text Classification – Assign labels to text.
• Sentiment Analysis – Analyze opinions from text.
• Named Entity Recognition (NER) – Identify proper names in text.
• Machine Translation – Automatically translate between languages.

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📏 Large Language Models (LLMs)

• What are LLMs? Deep dive into models like GPT, BERT, T5.
• Pretraining and Fine-tuning: Learn how LLMs are trained and refined.

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✨ Generative AI (GAI)

Explore models used to generate various media:

• Text: ChatGPT, Claude
• Images: DALL·E, MidJourney
• Audio: MusicGen, VALL-E
• Video: Sora

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🧱 Foundation Models

• What is a Foundation Model? Understand large pre-trained models that can be adapted for various tasks.
• Examples: GPT-4, Gemini, Claude, PaLM
• Multimodal Capabilities: These models are trained across text, image, video, and audio data.

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🧠 Artificial General Intelligence (AGI)

• What is AGI? A deeper understanding of AI that can match human intelligence.
• Progress & Challenges: Explore the current progress and limitations.
• Future of AGI: Learn about the possibilities and challenges in achieving AGI.

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🧰 AI Tools & Frameworks

Get hands-on with the essential tools for building AI models:

• Python Basics – The core language for AI development.
• NumPy, pandas – For numerical and data manipulation.
• Scikit-learn – A machine learning library in Python.
• TensorFlow / PyTorch – Deep learning libraries.
• Hugging Face Transformers – Pretrained models for NLP tasks.

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🌍 Ethics & Real-World Applications

• AI Bias & Fairness – Understand the importance of building fair models.
• Explainability in AI – Learn about the interpretability of models.
• AI Safety: Understand the need for safe AI deployment in the real world.