Fundamentals of Artificial Intelligence

Kickstart your AI journey with a solid foundation in the basics of Artificial Intelligence. Understanding AI fundamentals is where most learners struggle, but don’t worry—we’ve got you covered! This guided path covers all the essential AI concepts in a simplified and practical way, helping you build strong intuition step-by-step. Whether you're a student or a working professional, mastering these core ideas will prepare you for real-world applications and advanced AI topics ahead. So, enroll in the Basics of AI Guided Path today and start building your future with confidence!

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Chapter 1

Introduction to Artificial Intelligence

2 Notes & 2 Problems

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Foundations of Artificial Intelligence

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AI in Practice

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AI, ML, and DL Relationship

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The AI Engineer's Role

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Chapter 2

Machine Learning for Developers

8 Notes & 8 Problems

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What is ML?

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Data in Machine Learning

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The ML Lifecycle

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Data Pre-processing

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Machine Learning Paradigms

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Supervised Learning

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Unsupervised Learning

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Reinforcement Learning

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Machine Learning Paradigms

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Supervised Learning Task

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Clustering

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Core Concepts of Reinforcement Learning

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Model Training, Validation, and Testing

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Computer Vision

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Model Evaluation

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Computer Vision Tasks

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Chapter 3

Foundations of Natural Language Processing (NLP)

6 Notes & 6 Problems

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What is NLP?

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Key NLP Tasks

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Text Preprocessing Techniques

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Core Sub-Disciplines of NLP

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NLP Task Types

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Text Preprocessing

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Traditional NLP Techniques

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The Embedding Revolution

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TF-IDF

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Word Embedding Principle

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Introduction to the Transformer Architecture

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Transformer Architecture

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Chapter 4

Deep Learning and Neural Networks

3 Notes & 3 Problems

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Introduction to Neural Networks

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Different types of Neural Networks

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Core Component of Neural Networks

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Neural Network Architectures

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Introduction to Deep Learning

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Defining Deep Learning

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Chapter 5

Introduction to Generative AI

8 Notes & 8 Problems

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Discriminative vs. Generative Models

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Gen AI in ML Landscape

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Discriminative vs. Generative Models

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Generative AI's Role

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Multimodality in Gen AI

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Text-to-Text Generation

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Text-to-Image Generation

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Text-to-Code Generation

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Defining Modality

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Token-by-Token Generation

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Diffusion Models

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Text-to-Code Generation

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Use Cases for Developers

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Understanding the Guardrails

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Generative AI in Testing

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AI Limitations

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Chapter 6

Deep Dive into Large Language Models

6 Notes & 6 Problems

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Introduction to Large Language Models (LLM)

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Anatomy of an LLM

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LLM Training Objective

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LLM Architecture

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Key LLM Concepts

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RLHF

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Retrieval-Augmented Generation

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RLHF

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Survey of Foundational LLMs

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Scaling Laws

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Foundational LLM Contributions

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Scaling Laws

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Chapter 7

Practice MCQ's

36 Problems

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Evolution of AI Paradigms

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Tiers of Intelligence

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ML vs. DL

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AI Application Analysis

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Modern AI Engineering

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ML Paradigm Shift

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Data Pre-processing

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ML Paradigms

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Supervised Learning Tasks

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Unsupervised Learning Tasks

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Reinforcement Learning

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Model Validation Process

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Computer Vision Tasks

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Core NLP Challenges

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NLP Task Differentiation

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Text Preprocessing Trade-offs

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Traditional NLP Limitations

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Word Embedding Concepts

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Transformer Architecture

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Neural Network Fundamentals

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Neural Network Architectures

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Deep Learning Fundamentals

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Discriminative vs. Generative Models

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Gen AI & Traditional ML Synergy

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Multimodality in Gen AI

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Text Generation Decoding

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Text-to-Image Generation

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Text-to-Code Generation Tools

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Developer Use Cases

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Gen AI Limitations & Guardrails

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LLM vs. Traditional Models

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LLM Architecture

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LLM Training & Interaction

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RLHF Process

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Foundational LLMs

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Scaling Laws

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