Essentials of Machine Learning

Essentials of Machine Learning, available at Free, has an average rating of 4.75, with 10 lectures, based on 79 reviews, and has 4185 subscribers.

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You will learn about An overview of the workflow from starting to launching an ML project Essential terms that will pop up often during ML conversations Overview of classification and regression goals Understanding of some of the techniques you can use to optimize your ML model This course is ideal for individuals who are Anyone who wants to get a high-level overview of the different components involved in machine learning It is particularly useful for Anyone who wants to get a high-level overview of the different components involved in machine learning.

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Summary

Title: Essentials of Machine Learning

Price: Free

Average Rating: 4.75

Number of Lectures: 10

Number of Published Lectures: 10

Number of Curriculum Items: 10

Number of Published Curriculum Objects: 10

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

An overview of the workflow from starting to launching an ML project

Essential terms that will pop up often during ML conversations

Overview of classification and regression goals

Understanding of some of the techniques you can use to optimize your ML model

Who Should Attend

Anyone who wants to get a high-level overview of the different components involved in machine learning

Target Audiences

Anyone who wants to get a high-level overview of the different components involved in machine learning

Machine Learning has become an exciting route to go down by many teams and companies. However, it’s not always realistic that everyone is expected to catch up with all of the latest ML trends.

Usually Machine learning teams are made up of different people. On the technical side you can have a mixture of the different data scientists and engineers, like a Machine Learning Data Scientists, as well as Machine Learning and Data Engineers. The data scientists’ main responsibility would be building out or improving the models, and the engineers will help with everything else around deployment and that the models are getting the data they need.

From the non-technical side it’s likely you’ll have a project manager and possibly also several other business stakeholders. This course is aimed for these people, who need to understand what’s going on at a higher level, without necessarily having to dive into the technical components. Those that need to know enough to help with product vision, and be able to have and understand discussions about current statuses, blockers, as well as estimations.

In this course we’ll look at some of the different components involved in an ML project so that you can feel like you can have fruitful conversations when working on an ML project without needing to get bogged up on all the technical details.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Essentials of Machine Learning

Lecture 2: Machine Learning Essential Terms

Lecture 3: ML Essential Terms Contd.

Lecture 4: Wrapping Up Essential Terms

Lecture 5: Data Preparation

Lecture 6: Data Preparation Contd.

Lecture 7: Classification Algorithms

Lecture 8: Classification Algorithms Contd.

Lecture 9: Regression Algorithms

Lecture 10: Optimization Techniques

Instructors

Max S
Data Engineer

Rating Distribution

1 stars: 1 votes

2 stars: 3 votes

3 stars: 12 votes

4 stars: 32 votes

5 stars: 31 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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