Understanding Algorithmic Foundations Of Ai & Ml

How To Understand The Algorithms That Make Machines Learn

What you'll learn

Differentiate between Core Algorithm Types: Explain the distinctions between supervised, unsupervised, and reinforcement learning algorithms.

mplement Regression and Classification: Design and implement algorithms for regression and classification tasks.

Utilize Decision Trees and Random Forests: Construct decision trees and random forests.

Explain Neural Network Concepts: Describe the key components of neural networks (layers, neurons, activation functions.

Evaluate and Optimize Algorithms: Apply performance metrics (e.g., accuracy, precision, F1-score) to evaluate algorithms.

Requirements

Familiarity with Python is highly encouraged.

Description

Unravel the intricate algorithms that underpin Artificial Intelligence (AI) and Machine Learning (ML) systems. This course delves into the fundamental mathematical and computational principles driving the field, empowering you to grasp the inner workings of these powerful technologies.Big Picture Goals:Gain a solid foundation: Develop a comprehensive understanding of the core algorithmic principles behind AI and ML.Practical Skills: Learn to design and implement basic algorithms to tackle real-world problems using AI and ML techniques.Key Topics:Supervised and Unsupervised Learning: Explore the distinct approaches used to train machine learning models, with and without labeled data.Regression and Classification: Understand how to predict continuous values (regression) and classify data into categories (classification).Decision Trees and Random Forests: Demystify these powerful algorithms for decision-making and prediction.Neural Networks: Dive into the architecture and principles of neural networks, the backbone of deep learning.Experimental Algorithms: Discover methods for experimenting with and designing new algorithms.Evaluation and Optimization: Learn essential strategies to evaluate algorithm performance and optimize them for enhanced results.By the end of this course, you will have the knowledge and skills to:Understand the key algorithms that power AI and ML systems.Apply algorithms to solve real-world problems.Evaluate the effectiveness of different algorithms in various contexts.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Supervised Learning

Lecture 3 Unsupervised Learning

Lecture 4 Reinforcement Learning

Lecture 5 Deep Learning

Lecture 6 Evolutionary Algorithms

Lecture 7 Solving Problems With Chaos Theory

Lecture 8 Corporate IT Land

Lecture 0 SWARM Algorithms

Intermediate or Higher Developers who are interested in learning about AI and ML algorithms.