Master In Artificial Intelligence (Ai)

Develop and deploy machine learning and deep learning models to become a successful AI Engineer using Data Science etc

What you’ll learn
What is Artificial Intelligence and career opportunities in this field?
What are the responsibilities of a AI Engineer?
How to effectively deliver on these responsibilities?
How to become a successful AI Engineer?

Requirements
Willing to spend 7+ hours learning about Artificial Intelligence

Description
Want to become an Successful AI Engineer but don’t know what to do and how?Take a look at this course where you willNot only learn about the Artificial Intelligence and role of AI Engineer but alsoHow to develop and deploy machine learning and deep learning models to address complex business issues andBecome a Successful AI EngineerPreview many lectures for free to see the content for yourselfClear your doubts on this topic any time while doing the courseGet Udemy’s 30 days Money Back GuaranteeMy exposure to Artificial Intelligence began in 2020 when the demand for the AI Engineer role started increasing globally at a rapid paceI went about understanding the AI Engineer job requirements from the industry to meet the growing demand for this emerging role and had a chance to prepare many students from a company I was working with in this domainDuring these years, I learnt all about Artificial Intelligence that can help develop and deploy the machine learning and deep learning models to address specific business problemsI bring in this course my learnings from this journey and share with you how can you also become a Successful AI EngineerPreview for yourself many lectures free. If you like the content, enroll for the course, enjoy and skill yourself to become a Master in Artificial Intelligence! If don’t like the content, please message about how can we modify it to meet your expectations.Please remember that this course comes with Udemy’s 30 days Money Back Guarantee

Overview
Section 1: Introduction

Lecture 1 Introduction

Section 2: Overview

Lecture 2 Overview I

Lecture 3 Overview II

Lecture 4 Overview III

Section 3: Problem Definition

Lecture 5 Problem Definition I

Lecture 6 Problem Definition II

Lecture 7 Problem Definition III

Lecture 8 Problem Definition IV

Section 4: Data Collection & Preprocessing

Lecture 9 Data Collection & Preprocessing I

Lecture 10 Data Collection & Preprocessing II

Lecture 11 Data Collection & Preprocessing III

Lecture 12 Data Collection & Preprocessing IV

Lecture 13 Data Collection & Preprocessing V

Lecture 14 Data Collection & Preprocessing VI

Section 5: Algorithm Selection & Development

Lecture 15 Algorithm Selection & Development I

Lecture 16 Algorithm Selection & Development II

Lecture 17 Algorithm Selection & Development III

Lecture 18 Algorithm Selection & Development IV

Lecture 19 Algorithm Selection & Development V

Lecture 20 Algorithm Selection & Development VI

Lecture 21 Algorithm Selection & Development VII

Lecture 22 Algorithm Selection & Development VIII

Lecture 23 Algorithm Selection & Development IX

Lecture 24 Algorithm Selection & Development X

Lecture 25 Algorithm Selection & Development XI

Lecture 26 Algorithm Selection & Development XII

Lecture 27 Algorithm Selection & Development XIII

Lecture 28 Algorithm Selection & Development XIV

Lecture 29 Algorithm Selection & Development XV

Lecture 30 Algorithm Selection & Development XVI

Lecture 31 Algorithm Selection & Development XVII

Lecture 32 Algorithm Selection & Development XVIII

Lecture 33 Algorithm Selection & Development XIX

Lecture 34 Algorithm Selection & Development XX

Lecture 35 Algorithm Selection & Development XXI

Lecture 36 Algorithm Selection & Development XXII

Lecture 37 Algorithm Selection & Development XXIII

Section 6: Feature Engineering

Lecture 38 Feature Engineering I

Lecture 39 Feature Engineering II

Lecture 40 Feature Engineering III

Lecture 41 Feature Engineering IV

Lecture 42 Feature Engineering V

Lecture 43 Feature Engineering VI

Section 7: Deployment

Lecture 44 Deployment I

Lecture 45 Deployment II

Lecture 46 Deployment III

Section 8: Monitoring and Maintenance

Lecture 47 Monitoring and Maintenance I

Lecture 48 Monitoring and Maintenance II

Lecture 49 Monitoring and Maintenance III

Lecture 50 Monitoring and Maintenance IV

Lecture 51 Monitoring and Maintenance V

Section 9: Collaboration

Lecture 52 Collaboration I

Lecture 53 Collaboration II

Lecture 54 Collaboration III

Section 10: Research and Innovation

Lecture 55 Research and Innovation I

Lecture 56 Research and Innovation II

Lecture 57 Research and Innovation III

Section 11: Ethical Considerations

Lecture 58 Ethical Considerations I

Lecture 59 Ethical Considerations II

Lecture 60 Ethical Considerations III

Section 12: Roadmap to become AI Engineer

Lecture 61 Roadmap to become AI Engineer

Section 13: Summary

Lecture 62 Summary

Aspiring Artificial Intelligence Engineers