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Lec 29 Linear Regression

Fundamentals of Artificial Intelligence Introduction

Lec 01 Introduction to AI

Lec 02 Problem Solving as State Space Search

Lec 03 Uniformed Search

Lec 04 Heuristic Search

Lec 05 Informed Search

Lec 06 Constraint Satisfaction Problems

Lec 07 Searching AND OR Graphs

Lec 08 Game Playing

Lec 09 Minimax Alpha Beta

Lec 10 Introduction to Knowledge Representation

Lec 11 Propositional Logic

Lec 12 First Order Logic I

Lec 13 First Order Logic II

Lec 14 Inference in First Order Logic I

Lec 15 Inference in FOL II

Lec 16 Answer Extraction

Lec 17 Procedural Control of Reasoning

Lec 18 Reasoning under Uncertainty

Lec 19 Bayesian Network

Lec 20 Decision Network

Lec 21 Introduction to Planning

Lec 22 Plan Space Planning

Lec 23 Planning Graph and GraphPlan

Lec 24 Practical Planning and Acting

Lec 25 Sequential Decision Problems

Lec 26 Making Complex Decisions

Lec 27 Introduction to Machine Learning

Lec 28 Learning Decision Trees

Lec 29 Linear Regression

Lec 30 Support Vector Machines

Lec 31 Unsupervised Learning

Lec 32 Reinforcement Learning

Lec 33 Learning in Neural Networks

Lec 34 Deep Learning A Brief Overview

ChatGPT for Developers

Machine Learning Roadmap for beginners

AI Basics

ChatGPT Content For Beginners

Machine Learning Model Deployment

Chat GPT

Kubernetes and MLOps for Machine Learning

Generative Model Development

Deep Learning Fundamentals

AI Techniques

Cloud Machine Learning for beginners

Artificial Intelligence Fundamentals

Google Business Intelligence Strategy

Deep Learning Foundations

Installing TensorFlow Basics

TensorFlow Binary for Machine Learning