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Introduction to Machine Learning by Kevin Brand
What you'll learn
Define the fundamental aspects of data pipelines that is necessary for machine learning
Identify the potential pitfalls when building data pipelines
Recognize the different types of machine learning models and explain their differences
Discuss popular supervised machine learning models
Understand popular unsupervised clustering algorithms
Broadly define what neural networks are
Know what some of the most popular neural network variants are and when to use them
Utilize machine learning fundamentals to implement basic solutions to classification and regression problems
Requirements
Some programming experience will be beneficial for exercises and examples, but is not required.
Description
This course aims to provide students with a broad overview of the field of machine learning and will introduce some important terms and techniques which will enable them to follow a discussion on the topic. I will discuss the fundamental aspects of data pipelines and will point out what some of the common pitfalls are when preparing data for a machine learning project. I will also discuss what the different types of machine learning models are and how they differ from deep learning models.Broad overviews will be provided of some of the most popular supervised and unsupervised models and students will be introduced to some of the popular neural network variants. This will be followed by a few practical demonstrations which will show students how they can combine the discussed topics to create basic machine learning solutions.This course will not provide in-depth explanations regarding the mathematical underpinnings of these models, nor will it provide detailed discussions regarding how to implement machine learning models from scratch. Instead, the aim is to simplify and condense the subject matter to provide students with an easily digestible introduction to the field.Whether students are employers or employees, we believe it to be highly beneficial to have a basic understanding of what machine learning models are and what they are not --- especially as machine learning tools become increasingly common in many domains.
Who this course is for
Software engineers that want to be able to follow discussions about the machine learning pipeline
Managers that are looking to incorporate machine learning into their business and want to better understand the intricacies of doing so
Prospective students that want to establish whether machine learning is the right field for them
This course is not intended for learners with prior machine learning knowledge
This course is not intended for learners that wants to understand the mathematical foundations of machine learning
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Published 10/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 5h 42m | Size: 3.54 GB
A beginners guide to commonly used machine learning models and terminology
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 5h 42m | Size: 3.54 GB
A beginners guide to commonly used machine learning models and terminology
What you'll learn
Define the fundamental aspects of data pipelines that is necessary for machine learning
Identify the potential pitfalls when building data pipelines
Recognize the different types of machine learning models and explain their differences
Discuss popular supervised machine learning models
Understand popular unsupervised clustering algorithms
Broadly define what neural networks are
Know what some of the most popular neural network variants are and when to use them
Utilize machine learning fundamentals to implement basic solutions to classification and regression problems
Requirements
Some programming experience will be beneficial for exercises and examples, but is not required.
Description
This course aims to provide students with a broad overview of the field of machine learning and will introduce some important terms and techniques which will enable them to follow a discussion on the topic. I will discuss the fundamental aspects of data pipelines and will point out what some of the common pitfalls are when preparing data for a machine learning project. I will also discuss what the different types of machine learning models are and how they differ from deep learning models.Broad overviews will be provided of some of the most popular supervised and unsupervised models and students will be introduced to some of the popular neural network variants. This will be followed by a few practical demonstrations which will show students how they can combine the discussed topics to create basic machine learning solutions.This course will not provide in-depth explanations regarding the mathematical underpinnings of these models, nor will it provide detailed discussions regarding how to implement machine learning models from scratch. Instead, the aim is to simplify and condense the subject matter to provide students with an easily digestible introduction to the field.Whether students are employers or employees, we believe it to be highly beneficial to have a basic understanding of what machine learning models are and what they are not --- especially as machine learning tools become increasingly common in many domains.
Who this course is for
Software engineers that want to be able to follow discussions about the machine learning pipeline
Managers that are looking to incorporate machine learning into their business and want to better understand the intricacies of doing so
Prospective students that want to establish whether machine learning is the right field for them
This course is not intended for learners with prior machine learning knowledge
This course is not intended for learners that wants to understand the mathematical foundations of machine learning
Homepage:
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