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Development Programming Languages Machine Learning

Machine Learning with ML.Net for Absolute Beginners

Use your dotnet skills for building Machine Learning apps using ML.Net
Rating: 2.9 out of 52.9 (21 ratings)
2,738 students
Created by Nilay Mehta, Tutorials Team
Last updated 5/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll 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

Course content

14 sections • 74 lectures • 6h 4m total length

  • Preview02:50
  • What is Machine Learning?
    06:45
  • ML v/s AI v/s DL
    02:36
  • Preview03:51
  • Setting up Environment
    02:15
  • ML.Net SDK
    02:25

  • ML.Net Flow
    01:46
  • ML Terminology
    02:54
  • Section Summary
    00:40
  • Create Regression
    09:17
  • Preview03:59
  • Evaluate Model: with same Dataset
    03:12
  • Cross Validate Model
    05:21
  • Algorithms & Hyperparameters
    03:38
  • Section Summary
    00:49

  • Preview08:09
  • Load data from Multiple TextFile
    04:16
  • Load data from Binary
    02:34
  • Load data from Database
    03:25
  • Save data
    05:27
  • Filter data
    07:11
  • Section Summary
    00:54

  • Section Introduction
    01:37
  • Preview05:34
  • Load Model
    04:26

  • Binary Classification
    01:50
  • Logistic regression
    09:50
  • Sentiment Analysis - 1
    05:42
  • Sentiment Analysis - 2
    07:18
  • Fast Tree & Fast Forest
    10:45

  • Multiclass Classification
    02:38
  • SdcaMaximumEntropy
    05:24
  • OneVersusAll
    07:20
  • LightGbm
    05:54

  • Computer Vision
    02:43
  • Using Multiclass classification - 1
    08:15
  • Using Multiclass classification - 2
    06:07
  • Using TensorFlow
    07:25

  • Anomaly Detection
    05:24
  • Ranking
    05:49
  • Forecasting
    06:25
  • Clustering
    05:20
  • Recommendation
    09:14

  • Text: Featurize & Normalize
    08:46
  • Text: Tokenize & Stopwords
    08:36
  • Text: WordBags & Ngram
    09:00
  • Preview10:22
  • Conversion: Key & Value
    06:27
  • Conversion: Vector
    03:30
  • Conversion: Dictionary & Lookup
    06:01
  • Section Summary
    00:51

  • Categorical: OneHotEncoding
    04:43
  • Categorical: OneHotHashEncoding
    02:59
  • Preview04:20
  • Select & Drop Columns
    05:04
  • Custom Mapping
    07:25
  • FeatureSelection
    07:08
  • Missing Values
    08:15
  • Expression & Normalization
    08:43
  • TimeSeries: ChangePoint
    05:04
  • TimeSeries: Anomaly & Spike
    07:26
  • Section Summary
    01:14

Requirements

  • Basic C# development
  • Basic concept of Machine Learning
  • Visual Studio 2019

Description

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.

Who this course is for:

  • 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

Instructors

Nilay Mehta
Passionate Software Engineer and Instructor
Nilay Mehta
  • 3.6 Instructor Rating
  • 586 Reviews
  • 13,745 Students
  • 9 Courses

Hey, My name is Nilay Mehta! I am an experienced .Net developer, having the Microsoft certificate of Programming with C#.Net.

I have a Master of Computer Applications and Bachelor of Computer Application degrees. Starting out in the IT industry about 3 years ago. I've worked with a range of development tools from PHP, C#, ASP.NET, and ASP.Net core.

I am a passionate software engineer who loves learning new technologies, and from the past 3 years, I'm enjoying sharing that knowledge through blogs and courses.

Tutorials Team
Start learning today and curve your future.
Tutorials Team
  • 3.6 Instructor Rating
  • 586 Reviews
  • 13,745 Students
  • 9 Courses

Hey there! I'm Nilay Mehta from Tutorials Team.

TutorialsTeam is group of people who committed to developer learning solutions since 2016.

Initially its start with 2 colleagues Nilay & Ravi and later joined by other partners.

Our Udemy courses continue to be bringing you comprehensive yet concise video courses straight from our experts.

Feel free to contact us on tutorialsteam.nnr@gmail.com.

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