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.
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
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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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Can I take my courses with me wherever I go?
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