Machine Learning with ML.Net for Absolute Beginners

Machine Learning with ML.Net for Absolute Beginners, available at $39.99, has an average rating of 2.5, with 74 lectures, based on 55 reviews, and has 2938 subscribers.

You will learn about Create a Machine Learning app with C# Use TensorFlow or ONNX model with dotnet app Using Machine Learning model in ASP dotnet Use AutoML to generate ML dotnet model This course is ideal for individuals who are This is for newbies who want to learn Machine Learning or Developer who knows C# and want to use those skills for Machine Learning too or A person who wants to create a Machine Learning model with C# or Developer who want to create Machine Learning It is particularly useful for This is for newbies who want to learn Machine Learning or Developer who knows C# and want to use those skills for Machine Learning too or A person who wants to create a Machine Learning model with C# or Developer who want to create Machine Learning.

Enroll now: Machine Learning with ML.Net for Absolute Beginners

Summary

Title: Machine Learning with ML.Net for Absolute Beginners

Price: $39.99

Average Rating: 2.5

Number of Lectures: 74

Number of Published Lectures: 74

Number of Curriculum Items: 74

Number of Published Curriculum Objects: 74

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

Create a Machine Learning app with C#
Use TensorFlow or ONNX model with dotnet app
Using Machine Learning model in ASP dotnet
Use AutoML to generate ML dotnet model

Who Should Attend

This is for newbies who want to learn Machine Learning
Developer who knows C# and want to use those skills for Machine Learning too
A person who wants to create a Machine Learning model with C#
Developer who want to create Machine Learning

Target Audiences

This is for newbies who want to learn Machine Learning
Developer who knows C# and want to use those skills for Machine Learning too
A person who wants to create a Machine Learning model with C#
Developer who want to create Machine Learning

Note: This course is designed with ML.Net 1.5.0-preview2

Machine Learning is learning from experience and making predictions based on its experience.

In Machine Learning, we need to create a pipeline, and pass training data based on that Machine will learn how to react on data.

ML.NET gives you the ability to add machine learning to .NET applications.

We are going to use C# throughout this series, but F# also supported by ML.Net.

ML.Net officially publicly announced in Build 2019.

It is a free, open-source, and cross-platform.

It is available on both the dotnet core as well as the dotnet framework.

The course outline includes:

Introduction to Machine Learning. And understood how it’s different from Deep Learning and Artificial Intelligence.
Learn what is ML.Net and understood the structure of ML.Net SDK.
Create a first model for Regression. And perform a prediction on it.
Evaluate model and cross-validate with data.
Load data from various sources like file, database, and binary.
Filter out data from the data view.
Export created the model and load saved model for performing further operations.
Learn about binary classification and use it for creating a model with different trainers.
Perform sentimental analysis on text data to determine user’s intention is positive or negative.
Use the Multiclass classification for prediction.
Use the TensorFlow model for computer vision to determine which object represent by images.
Then we will see examples of using other trainers like Anomaly Detection, Ranking, Forecasting, Clustering, and Recommendation.
Perform Transformation on data related to Text, Conversion, Categorical, TimeSeries, etc.
Then see how we can perform AutoML using ModelBuilder UI and CLI.
Learn what is ONNX, and how we can create and use ONNX models.
Then see how we can use models to perform predictions from ASP.Net Core.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Intro to Course

Lecture 2: What is Machine Learning?

Lecture 3: ML v/s AI v/s DL

Lecture 4: What is ML.Net?

Lecture 5: Setting up Environment

Lecture 6: ML.Net SDK

Chapter 2: Creating First Program

Lecture 1: ML.Net Flow

Lecture 2: ML Terminology

Lecture 3: Section Summary

Lecture 4: Create Regression

Lecture 5: Evaluate Model: with Test Dataset

Lecture 6: Evaluate Model: with same Dataset

Lecture 7: Cross Validate Model

Lecture 8: Algorithms & Hyperparameters

Lecture 9: Section Summary

Chapter 3: Data Load and Save

Lecture 1: Load data from TextFile

Lecture 2: Load data from Multiple TextFile

Lecture 3: Load data from Binary

Lecture 4: Load data from Database

Lecture 5: Save data

Lecture 6: Filter data

Lecture 7: Section Summary

Chapter 4: Model Save and Load

Lecture 1: Section Introduction

Lecture 2: Save Model

Lecture 3: Load Model

Chapter 5: Binary Classification

Lecture 1: Binary Classification

Lecture 2: Logistic regression

Lecture 3: Sentiment Analysis – 1

Lecture 4: Sentiment Analysis – 2

Lecture 5: Fast Tree & Fast Forest

Chapter 6: Multiclass Classification

Lecture 1: Multiclass Classification

Lecture 2: SdcaMaximumEntropy

Lecture 3: OneVersusAll

Lecture 4: LightGbm

Chapter 7: Computer Vision

Lecture 1: Computer Vision

Lecture 2: Using Multiclass classification – 1

Lecture 3: Using Multiclass classification – 2

Lecture 4: Using TensorFlow

Chapter 8: Other Training Tasks

Lecture 1: Anomaly Detection

Lecture 2: Ranking

Lecture 3: Forecasting

Lecture 4: Clustering

Lecture 5: Recommendation

Chapter 9: Transform – 1

Lecture 1: Text: Featurize & Normalize

Lecture 2: Text: Tokenize & Stopwords

Lecture 3: Text: WordBags & Ngram

Lecture 4: Conversion: Convert & Hash

Lecture 5: Conversion: Key & Value

Lecture 6: Conversion: Vector

Lecture 7: Conversion: Dictionary & Lookup

Lecture 8: Section Summary

Chapter 10: Transform – 2

Lecture 1: Categorical: OneHotEncoding

Lecture 2: Categorical: OneHotHashEncoding

Lecture 3: Copy & Concatenate Columns

Lecture 4: Select & Drop Columns

Lecture 5: Custom Mapping

Lecture 6: FeatureSelection

Lecture 7: Missing Values

Lecture 8: Expression & Normalization

Lecture 9: TimeSeries: ChangePoint

Lecture 10: TimeSeries: Anomaly & Spike

Lecture 11: Section Summary

Chapter 11: AutoML

Lecture 1: ModelBuilder UI – 1

Lecture 2: ModelBuilder UI – 2

Lecture 3: ML.Net CLI – 1

Lecture 4: ML.Net CLI – 2

Lecture 5: Section Summary

Chapter 12: ONNX

Lecture 1: What is ONNX?

Lecture 2: Save as ONNX model

Lecture 3: Use ONNX model

Chapter 13: Misc

Lecture 1: Use Model in ASP.Net

Lecture 2: Evaluation metric

Chapter 14: Extra shoots

Lecture 1: Conclusion

Lecture 2: Bonus Lecture

Instructors

Nilay Mehta
Passionate Software Engineer and Instructor
Tutorials Team
Start learning today and curve your future.

Rating Distribution

1 stars: 15 votes
2 stars: 5 votes
3 stars: 12 votes
4 stars: 12 votes
5 stars: 11 votes

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