Machine Learning with ML.Net for Absolute Beginners
4.2 (3 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,665 students enrolled

Machine Learning with ML.Net for Absolute Beginners

Use your dotnet skills for building Machine Learning apps using ML.Net
New
4.2 (3 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,665 students enrolled
Last updated 5/2020
English
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Current price: $20.99 Original price: $29.99 Discount: 30% off
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This course includes
  • 6 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Expand all 74 lectures 06:04:08
+ Introduction
6 lectures 20:42
What is Machine Learning?
06:45
ML v/s AI v/s DL
02:36
Setting up Environment
02:15
ML.Net SDK
02:25
+ Creating First Program
9 lectures 31:36
ML.Net Flow
01:46
ML Terminology
02:54
Section Summary
00:40
Create Regression
09:17
Evaluate Model: with same Dataset
03:12
Cross Validate Model
05:21
Algorithms & Hyperparameters
03:38
Section Summary
00:49
+ Data Load and Save
7 lectures 31:56
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
+ Model Save and Load
3 lectures 11:37
Section Introduction
01:37
Load Model
04:26
+ Binary Classification
5 lectures 35:25
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
4 lectures 21:16
Multiclass Classification
02:38
SdcaMaximumEntropy
05:24
OneVersusAll
07:20
LightGbm
05:54
+ Computer Vision
4 lectures 24:30
Computer Vision
02:43
Using Multiclass classification - 1
08:15
Using Multiclass classification - 2
06:07
Using TensorFlow
07:25
+ Other Training Tasks
5 lectures 32:12
Anomaly Detection
05:24
Ranking
05:49
Forecasting
06:25
Clustering
05:20
Recommendation
09:14
+ Transform - 1
8 lectures 53:33
Text: Featurize & Normalize
08:46
Text: Tokenize & Stopwords
08:36
Text: WordBags & Ngram
09:00
Conversion: Key & Value
06:27
Conversion: Vector
03:30
Conversion: Dictionary & Lookup
06:01
Section Summary
00:51
+ Transform - 2
11 lectures 01:02:21
Categorical: OneHotEncoding
04:43
Categorical: OneHotHashEncoding
02:59
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