No-Code and No-Math Machine Learning

No-Code and No-Math Machine Learning, available at $64.99, has an average rating of 4.6, with 41 lectures, based on 115 reviews, and has 1151 subscribers.

You will learn about Build machine learning models to use on real problems without a single line of code and no math skills Use the main tools applied in classification, regression and time series forecasting problems Implement machine learning in the following tools: Google Vertex AI, Data Robot AI, Obviously AI, Big ML, Microsoft Azure and Orange Learn how to deploy machine learning models This course is ideal for individuals who are People interested in building real machine learning models without a single line of code or People interested in starting their studies in Machine Learning and Data Science or Students who have little programming or math experience or Undergraduate and graduate students who are studying subjects related to the area of Artificial Intelligence It is particularly useful for People interested in building real machine learning models without a single line of code or People interested in starting their studies in Machine Learning and Data Science or Students who have little programming or math experience or Undergraduate and graduate students who are studying subjects related to the area of Artificial Intelligence.

Enroll now: No-Code and No-Math Machine Learning

Summary

Title: No-Code and No-Math Machine Learning

Price: $64.99

Average Rating: 4.6

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Build machine learning models to use on real problems without a single line of code and no math skills

Use the main tools applied in classification, regression and time series forecasting problems

Implement machine learning in the following tools: Google Vertex AI, Data Robot AI, Obviously AI, Big ML, Microsoft Azure and Orange

Learn how to deploy machine learning models

Who Should Attend

People interested in building real machine learning models without a single line of code

People interested in starting their studies in Machine Learning and Data Science

Students who have little programming or math experience

Undergraduate and graduate students who are studying subjects related to the area of Artificial Intelligence

Target Audiences

People interested in building real machine learning models without a single line of code

People interested in starting their studies in Machine Learning and Data Science

Students who have little programming or math experience

Undergraduate and graduate students who are studying subjects related to the area of Artificial Intelligence

If you want to learn machine learning but you feel intimidated by programming or math fundamentals, this course is for you!

You are going to learn how to build projects using six tools that do not require any prior knowledge of computer programming or math! This course was designed for you to create hands-on projects quickly and easily, without a single line of code. It is suitable for beginners and also for students with intermediate or advanced knowledge, who need to increase productivity but at the same time do not have the time to implement code from scratch. You can perform exploratory data analysis, build, train, test and put machine learning models into production with a few clicks!

We are going to cover 6 tools that are widely used for commercial projects: Google Vertex AI, Data Robot AI, Obviously AI, Big ML, Microsoft Azure Machine Learning, and Orange! All projects will be developed calmly and step by step, so that you can make the most of the content. There is an exercise along with the solution at the end of each section, so you can practice the steps for each tool! There are more than 30 lectures and 5 hours of videos!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course content

Lecture 2: Machine Learning – intuition

Lecture 3: Course materials

Chapter 2: Google Vertex AI

Lecture 1: Overview

Lecture 2: Datasets

Lecture 3: Training

Lecture 4: Regression metrics

Lecture 5: Deploy and predictions

Lecture 6: HOMEWORK

Lecture 7: Homework solution

Chapter 3: Data Robot AI

Lecture 1: Overview

Lecture 2: Datasets

Lecture 3: Classification metrics

Lecture 4: Training

Lecture 5: Deploy and predictions

Lecture 6: HOMEWORK

Lecture 7: Homework solution

Chapter 4: Obviously AI

Lecture 1: Overview

Lecture 2: Dataset, training, and predictions

Lecture 3: HOMEWORK

Lecture 4: Homework solution

Chapter 5: Big ML

Lecture 1: Overview

Lecture 2: Dataset

Lecture 3: Training and evaluating

Lecture 4: Predictions

Lecture 5: HOMEWORK

Lecture 6: Homework solution

Chapter 6: Microsoft Azure

Lecture 1: Overview

Lecture 2: Workspace and dataset

Lecture 3: Machine learning pipeline

Lecture 4: Evaluation and deploy

Lecture 5: Auto ML

Lecture 6: HOMEWORK

Lecture 7: Homework solution

Chapter 7: Orange

Lecture 1: Overview

Lecture 2: Classification

Lecture 3: Time series

Lecture 4: HOMEWORK

Lecture 5: Homework solution

Chapter 8: Final remarks

Lecture 1: Final remarks

Lecture 2: BONUS

Instructors

Jones Granatyr
Professor

AI Expert Academy
Instructor

Rating Distribution

1 stars: 2 votes

2 stars: 2 votes

3 stars: 10 votes

4 stars: 32 votes

5 stars: 69 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?

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