AutoML Automated Machine Learning BootCamp (No Code ML)

AutoML Automated Machine Learning BootCamp (No Code ML), available at $19.99, has an average rating of 4.04, with 10 lectures, based on 237 reviews, and has 28978 subscribers.

You will learn about Understanding the Lifecycle of a Machine Learning Project. Introduction to Cloud Computing and how to use Cloud Computing for Machine Learning. Learn about AWS SageMaker Canvas. Perform Diabetes Prediction Machine Learning Practical on AWS SageMaker Canvas without writing a single line of code. This course is ideal for individuals who are Anyone who’s interested in building practical real-world Machine Learning applications but doesn’t have any coding skills (or have basic coding skills) or Beginner Data Scientists who are passionate about Machine Learning and want to learn a new skill of AWS SageMaker Canvas. It is particularly useful for Anyone who’s interested in building practical real-world Machine Learning applications but doesn’t have any coding skills (or have basic coding skills) or Beginner Data Scientists who are passionate about Machine Learning and want to learn a new skill of AWS SageMaker Canvas.

Enroll now: AutoML Automated Machine Learning BootCamp (No Code ML)

Summary

Title: AutoML Automated Machine Learning BootCamp (No Code ML)

Price: $19.99

Average Rating: 4.04

Number of Lectures: 10

Number of Published Lectures: 10

Number of Curriculum Items: 10

Number of Published Curriculum Objects: 10

Original Price: ₹999

Quality Status: approved

Status: Live

What You Will Learn

Understanding the Lifecycle of a Machine Learning Project.
Introduction to Cloud Computing and how to use Cloud Computing for Machine Learning.
Learn about AWS SageMaker Canvas.
Perform Diabetes Prediction Machine Learning Practical on AWS SageMaker Canvas without writing a single line of code.

Who Should Attend

Anyone who’s interested in building practical real-world Machine Learning applications but doesn’t have any coding skills (or have basic coding skills)
Beginner Data Scientists who are passionate about Machine Learning and want to learn a new skill of AWS SageMaker Canvas.

Target Audiences

Anyone who’s interested in building practical real-world Machine Learning applications but doesn’t have any coding skills (or have basic coding skills)
Beginner Data Scientists who are passionate about Machine Learning and want to learn a new skill of AWS SageMaker Canvas.

“No code” machine learning (ML) refers to the use of ML platforms, tools, or libraries that allow users to build and deploy ML models without writing any code. This approach is intended to make ML more accessible to a wider range of users, including those who may not have a strong programming background.

Amazon SageMaker is a fully managed machine learning service provided by Amazon Web Services (AWS) that enables developers and data scientists to build, train, and deploy machine learning models at scale. SageMaker also includes built-in algorithms, pre-built libraries for common machine learning tasks, and a variety of tools for data pre-processing, model tuning, and model deployment. SageMaker also integrates with other AWS services to provide a complete machine learning environment.

AutoML in SageMaker refers to the automatic selection and tuning of machine learning models to improve the accuracy and performance of the models. This can be done by using SageMaker’s built-in algorithms and libraries or by using custom algorithms and libraries. SageMaker also includes a feature called Automatic Model Tuning which allows for tuning of the hyper-parameters of the models to improve their performance.

SageMaker Studio Canvas is a feature that allows users to interact with their data, build and visualize workflows, and create, run, and debug Jupyter notebooks, all within the same web-based interface. The Canvas provides a visual and interactive way to explore, manipulate and visualize data, and allows users to create Jupyter notebooks and drag-and-drop pre-built code snippets, called “recipes” to quickly perform common data pre-processing, data visualization, and data analysis tasks.

SageMaker Studio Canvas also allows users to easily share their notebooks, recipes, and data with other users and collaborate on projects. This helps to simplify the machine learning development process, accelerate the development of machine learning models, and improve collaboration among teams.

IN THIS COURSE YOU WILL LEARN :

LifeCycle of a Machine Learning Project
Machine Learning Fundamentals
Cloud Computing for Machine Learning
AWS SageMaker Canvas (NO CODE ML)

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to the Course

Chapter 2: Machine Learning Fundamentals

Lecture 1: Machine Learning Introduction

Lecture 2: Supervised Machine Learning

Lecture 3: Unsupervised Machine Learning

Lecture 4: Machine Learning LifeCycle

Lecture 5: ML Model Evaluation Metrics

Chapter 3: Cloud Computing

Lecture 1: Introduction to Cloud Computing

Lecture 2: Getting started with AWS

Lecture 3: Different AWS Services

Chapter 4: AWS SageMaker Canvas Practical

Lecture 1: Diabetes Prediction

Instructors

Raj Chhabria
Computer Science Engineer with Specialization in DataScience

Rating Distribution

1 stars: 13 votes
2 stars: 17 votes
3 stars: 49 votes
4 stars: 73 votes
5 stars: 80 votes

Frequently Asked Questions

How long do I have access to the course materials?

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

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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