Learn Fundamentals In Artificial Intelligence(Chatgpt4) 2023

Artificial Intelligence and Deep Learning

What you'll learn

Purpose of artificial intelligence technology

Concepts of deep learning and machine learning workflow

Unsupervised learning

Semisupervised learning

Unsupervised learning

Requirements

Professionals and students interested in learning Artificial Intelligence basics should have an understanding of the fundamentals of Python programming. Also, they need to have a basic knowledge of statistics.

Description

This Professional Certificate in Artificial Intelligence is an essential program designed to provide an overview of AI concepts and workflows. This program is ideal for individuals seeking to enhance their knowledge in the field of artificial intelligence and machine learning. It covers the fundamental concepts of machine learning and deep learning, along with specific use cases.Through this program, learners will develop a deep understanding of the purpose of artificial intelligence technology and how it works. They will also gain insights into the concepts of deep learning and machine learning workflows. This program is designed to equip learners with a comprehensive understanding of the different types of machine learning techniques and their applications.One of the critical areas that this program focuses on is supervised learning. Through this program, learners will understand how supervised learning works, the algorithms used, and the specific use cases. They will also learn about semisupervised learning, which is a blend of supervised and unsupervised learning.Additionally, learners will gain an in-depth understanding of unsupervised learning, which involves the use of algorithms to analyze and identify patterns in datasets without prior training. This program will teach learners how to use unsupervised learning techniques to cluster and classify data.This Professional Certificate in Artificial Intelligence is a comprehensive program that covers a wide range of topics, including the purpose of artificial intelligence, deep learning, and machine learning workflows. It provides learners with the essential skills required to analyze data, identify patterns, and apply machine learning algorithms to solve real-world problems.

Overview

Section 1: Introduction

Lecture 1 Introduction of this certification

Section 2: Decoding Artificial Intelligence

Lecture 2 Decoding Artificial Intelligence

Lecture 3 Meaning, Scope, and Stages Of Artificial Intelligence

Lecture 4 Three Stages of AI

Lecture 5 Application of AI

Lecture 6 Image Recognition

Lecture 7 Application of AI Examples

Lecture 8 Effects of AI on society

Lecture 9 Supervises Learning for Telemedicine

Lecture 10 Solves Complex Social Problems

Lecture 11 Benefits Multiple Industries

Lecture 12 Key Takeaways

Section 3: Fundamentals of Machine Learning and Deep Learning

Lecture 13 Fundamentals Of Machine Learning and Deep Learning

Lecture 14 Meaning of Machine Learning

Lecture 15 Relationship between Machine Learning and Statistical Analysis

Lecture 16 Process of Machine Learning

Lecture 17 Types of Machine Learning

Lecture 18 Meaning of Unsupervised Learning

Lecture 19 Meaning of Semi-supervised Learning

Lecture 20 Algorithms of Machine Learning

Lecture 21 Regression

Lecture 22 Naive Bayes

Lecture 23 Naive Bayes Classification

Lecture 24 Machine Learning Algorithms

Lecture 25 Deep Learning

Lecture 26 Artificial Neural Network Definition

Lecture 27 Definition of Perceptron

Lecture 28 Online and Batch Learning

Lecture 29 Key Takeaways

Section 4: Machine Learning Workflow

Lecture 30 Learning Objective

Lecture 31 Machine Learning Workflow

Lecture 32 Get more data

Lecture 33 Ask a Sharp Question

Lecture 34 Add Data to the Table

Lecture 35 Check for Quality

Lecture 36 Transform Features

Lecture 37 Answer the Questions

Lecture 38 Use the Answer

Lecture 39 Key takeaways

Section 5: Performance Metrics

Lecture 40 Performance Metrics

Lecture 41 Need For Performance Metrics

Lecture 42 Key Methods Of Performance Metrics

Lecture 43 Confusion Matrix Example

Lecture 44 Terms Of Confusion Matrix

Lecture 45 Minimize False Cases

Lecture 46 Minimize False Positive Example

Lecture 47 Accuracy

Lecture 48 Precision

Lecture 49 Recall Or Sensitivity

Lecture 50 Specificity

Lecture 51 F1 Score

Lecture 52 Key takeaways

Developers,Analytics Managers,Information Architects,Analytics Professionals