Practical AI and Machine Learning with Model Builder AutoML

Practical AI and Machine Learning with Model Builder AutoML, available at $19.99, has an average rating of 4.65, with 39 lectures, based on 26 reviews, and has 166 subscribers.

You will learn about See an end-to-end, supervised machine learning process to tackle a regression problem, using Microsoft's Model Builder and ML .Net. Understand the tasks and activities that take place behind the scenes. From data preparation all the way to model training and evaluation. Understand data transformation, feature scaling, iterating through algorithms, evaluation metrics, overfitting, cross-validation and regularization. Understanding the impact of evaluation metrics on model performance, and how to check for overfitting. Understand the lasting fundamentals of machine learning that are independent of the tools or platforms one can use. Gain a deep understanding of machine learning concepts by seeing them in action, during a practical machine learning demonstration. Understand the importance of Exploratory Data Analysis (EDA) and the impact that the statistical distribution of the data has on model performance. Learn how to set up Visual Studio and to configure it to enable Model Builder, the graphical tool that will be used to demonstrate the machine learning process. Learn how to use Model Builder to train models without having to code. This course is ideal for individuals who are This course is for entry-level machine learning enthusiasts, who have had some kind of theoretical introduction to machine learning, but who wants to put the theory into practice. or Machine learning enthusiasts who do not have a background in Statistics, Data Science or programming, but who want to see the complexities of machine learning in practice. or Machine learning enthusiasts who want to learn about complex concepts by seeing them in action, rather than by seeing a presentation. or Technical beginners who want to learn solid machine learning fundamentals before progressing onto more advanced courses where a detailed knowledge of statistics, calculus and programming may be required. It is particularly useful for This course is for entry-level machine learning enthusiasts, who have had some kind of theoretical introduction to machine learning, but who wants to put the theory into practice. or Machine learning enthusiasts who do not have a background in Statistics, Data Science or programming, but who want to see the complexities of machine learning in practice. or Machine learning enthusiasts who want to learn about complex concepts by seeing them in action, rather than by seeing a presentation. or Technical beginners who want to learn solid machine learning fundamentals before progressing onto more advanced courses where a detailed knowledge of statistics, calculus and programming may be required.

Enroll now: Practical AI and Machine Learning with Model Builder AutoML

Summary

Title: Practical AI and Machine Learning with Model Builder AutoML

Price: $19.99

Average Rating: 4.65

Number of Lectures: 39

Number of Published Lectures: 39

Number of Curriculum Items: 39

Number of Published Curriculum Objects: 39

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

• See an end-to-end, supervised machine learning process to tackle a regression problem, using Microsoft's Model Builder and ML .Net.
• Understand the tasks and activities that take place behind the scenes. From data preparation all the way to model training and evaluation.
• Understand data transformation, feature scaling, iterating through algorithms, evaluation metrics, overfitting, cross-validation and regularization.
• Understanding the impact of evaluation metrics on model performance, and how to check for overfitting.
• Understand the lasting fundamentals of machine learning that are independent of the tools or platforms one can use.
• Gain a deep understanding of machine learning concepts by seeing them in action, during a practical machine learning demonstration.
• Understand the importance of Exploratory Data Analysis (EDA) and the impact that the statistical distribution of the data has on model performance.
• Learn how to set up Visual Studio and to configure it to enable Model Builder, the graphical tool that will be used to demonstrate the machine learning process.
• Learn how to use Model Builder to train models without having to code.

Who Should Attend

• This course is for entry-level machine learning enthusiasts, who have had some kind of theoretical introduction to machine learning, but who wants to put the theory into practice.
• Machine learning enthusiasts who do not have a background in Statistics, Data Science or programming, but who want to see the complexities of machine learning in practice.
• Machine learning enthusiasts who want to learn about complex concepts by seeing them in action, rather than by seeing a presentation.
• Technical beginners who want to learn solid machine learning fundamentals before progressing onto more advanced courses where a detailed knowledge of statistics, calculus and programming may be required.

Target Audiences

• This course is for entry-level machine learning enthusiasts, who have had some kind of theoretical introduction to machine learning, but who wants to put the theory into practice.
• Machine learning enthusiasts who do not have a background in Statistics, Data Science or programming, but who want to see the complexities of machine learning in practice.
• Machine learning enthusiasts who want to learn about complex concepts by seeing them in action, rather than by seeing a presentation.
• Technical beginners who want to learn solid machine learning fundamentals before progressing onto more advanced courses where a detailed knowledge of statistics, calculus and programming may be required.