Intelligence and Learning - Nature of Code Part 2

Coding Train via YouTube

Start learning Write review

Explore artificial intelligence and machine learning concepts through hands-on coding projects in this comprehensive video series covering algorithms, data structures, genetic algorithms, and neural networks. Master fundamental AI techniques starting with pathfinding algorithms including A* search, breadth-first search, and maze generation, then progress through binary search trees and graph traversal methods. Dive deep into genetic algorithms by implementing evolutionary systems, fitness functions, population selection methods, and solving complex problems like the traveling salesperson problem with crossover techniques. Learn machine learning fundamentals including linear regression with ordinary least squares and gradient descent, nearest neighbors recommendation engines, and essential calculus concepts like the power rule, chain rule, and partial derivatives. Build neural networks from scratch by understanding perceptrons, multilayer perceptrons, matrix mathematics, feedforward algorithms, and backpropagation techniques while updating code to modern ES6 standards. Practice implementing these concepts through numerous coding challenges and exercises designed to reinforce theoretical understanding with practical programming skills in JavaScript and p5.js.

Syllabus

Introduction to "Intelligence and Learning"
1.1: Introduction to Session 1 - Intelligence and Learning
Coding Challenge #65.1: Binary Search Tree
Coding Challenge #65.2: Visualizing a Binary Tree
Coding Challenge #68: Breadth-First Search Part 1
Coding Challenge #68: Breadth-First Search Part 2
Coding Challenge 10: Maze Generator
Coding Challenge 10: Maze Generator (Part II)
Coding Challenge 10: Maze Generator (Part III)
Coding Challenge 10: Maze Generator (Part IV)
A* Pathfinding Algorithm (Coding Challenge 51 - Part 1)
Coding Challenge 51.2: A* Pathfinding Algorithm - Part 2
Coding Challenge 51.3: A* Pathfinding Algorithm - Part 3
1.2: Exercise Ideas: Session 1 - Intelligence and Learning
2.1: Introduction to Session 2 - Intelligence and Learning
9.1: Genetic Algorithm: Introduction - The Nature of Code
9.2: Genetic Algorithm: How it works - The Nature of Code
9.3: Genetic Algorithm: Shakespeare Monkey Example - The Nature of Code
9.4: Genetic Algorithm: Looking at Code - The Nature of Code
9.5: Genetic Algorithm: Fitness, Genotype vs Phenotype - The Nature of Code
9.6: Genetic Algorithm: Improved Fitness Function - The Nature of Code
9.7: Genetic Algorithm: Pool Selection - The Nature of Code
9.8: Weighted Selection (for Genetic Algorithms) - The Nature of Code
9.9: Genetic Algorithm: Interactive Selection - The Nature of Code
9.10: Genetic Algorithm: Continuous Evolutionary System - The Nature of Code
Coding Challenge #35.5: TSP with Genetic Algorithm and Crossover
Coding Challenge #69: Evolutionary Steering Behaviors - Part 1
Coding Challenge #69: Evolutionary Steering Behaviors - Part 2
Coding Challenge #69: Evolutionary Steering Behaviors - Part 3
Coding Challenge #69: Evolutionary Steering Behaviors - Part 4
Coding Challenge #69: Evolutionary Steering Behaviors - Part 5 (Bonus)
Coding Challenge 35: Traveling Salesperson
Coding Challenge #35.2: Lexicographic Order
Coding Challenge #35.3: Traveling Salesperson with Lexicographic Order
Coding Challenge #35.4: Traveling Salesperson with Genetic Algorithm
2.2: Exercise Ideas: Session 2 - Intelligence and Learning
3.1: Introduction to Session 3 - What is Machine Learning?
Coding Challenge #70: Nearest Neighbors Recommendation Engine - Part 1
Coding Challenge #70: Nearest Neighbors Recommendation Engine - Part 2
Coding Challenge #70: Nearest Neighbors Recommendation Engine - Part 3
3.2: Linear Regression with Ordinary Least Squares Part 1 - Intelligence and Learning
3.3: Linear Regression with Ordinary Least Squares Part 2 - Intelligence and Learning
3.4: Linear Regression with Gradient Descent - Intelligence and Learning
3.5: Mathematics of Gradient Descent - Intelligence and Learning
3.5a: Calculus: Power Rule - Intelligence and Learning
3.5b: Calculus: Chain Rule - Intelligence and Learning
3.5c: Calculus: Partial Derivative - Intelligence and Learning
10.2: Neural Networks: Perceptron Part 1 - The Nature of Code
10.3: Neural Networks: Perceptron Part 2 - The Nature of Code
10.4: Neural Networks: Multilayer Perceptron Part 1 - The Nature of Code
10.5: Neural Networks: Multilayer Perceptron Part 2 - The Nature of Code
10.6: Neural Networks: Matrix Math Part 1 - The Nature of Code
10.7: Neural Networks: Matrix Math Part 2 - The Nature of Code
10.8: Neural Networks: Updating Code to ES6 - The Nature of Code
10.9: Neural Networks: Matrix Math Part 3 - The Nature of Code
10.10: Neural Networks: Matrix Math Part 4 - The Nature of Code
10.11: Neural Networks: Matrix Class Improvements - The Nature of Code
10.12: Neural Networks: Feedforward Algorithm Part 1 - The Nature of Code
10.13: Neural Networks: Feedforward Algorithm Part 2 - The Nature of Code
10.14: Neural Networks: Backpropagation Part 1 - The Nature of Code
10.15: Neural Networks: Backpropagation Part 2 - The Nature of Code
10.16: Neural Networks: Backpropagation Part 3 - The Nature of Code
10.17: Neural Networks: Backpropagation Part 4 - The Nature of Code
10.18: Neural Networks: Backpropagation Part 5 - The Nature of Code