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Machine Learning with ML.Net for Absolute Beginners

Introduction

Intro to Course (2:50)
What is Machine Learning? (6:45)
ML v/s AI v/s DL (2:36)
What is ML.Net? (3:51)
Setting up Environment (2:15)
ML.Net SDK (2:25)
ML.Net Flow (1:46)
ML Terminology (2:54)
Section Summary (0:40)

Creating First Program

Create Regression (9:17)
Evaluate Model: with Test Dataset (3:59)
Evaluate Model: with same Dataset (5:21)
Cross Validate Model (3:12)
Algorithms & Hyperparameters (3:38)
Section Summary (0:49)

Data Load and Save

Load data from TextFile (8:09)
Load data from Multiple TextFile (4:16)
Load data from Binary (2:34)
Load data from Database (3:25)
Save data (5:27)
Filter data (7:11)
Section Summary (0:54)

Model Save and Load

Section Introduction (1:37)
Save Model (5:34)
Load Model (4:26)

Binary Classification

Binary Classification (1:50)
Logistic regression (9:50)
Sentiment Analysis - 1 (5:42)
Sentiment Analysis - 2 (7:18)
Fast Tree & Fast Forest (10:45)

Multiclass Classification

Multiclass Classification (2:38)
SdcaMaximumEntropy (5:24)
OneVersusAll (7:20)
LightGbm (5:54)

Computer Vision

Computer Vision (2:42)
Using Multiclass classification - 1 (8:15)
Using Multiclass classification - 2 (6:07)
Using TensorFlow (7:25)

Other Training Tasks

Anomaly Detection (5:24)
Ranking (5:49)
Forecasting (6:25)
Clustering (5:19)
Recommendation (9:13)

Transform - 1

Text: Featurize & Normalize (8:46)
Text: Tokenize & Stopwords (8:36)
Text: WordBags & Ngram (8:59)
Conversion: Key & Value (10:21)
Conversion: Convert & Hash (6:27)
Conversion: Vector (3:30)
Conversion: Dictionary & Lookup (6:01)
Section Summary (0:51)

Transform - 2

Categorical: OneHotEncoding (4:43)
Categorical: OneHotHashEncoding (2:59)
Copy & Concatenate Columns (4:20)
Select & Drop Columns (5:04)
Custom Mapping (7:25)
FeatureSelection (7:08)
Missing Values (8:15)
Expression & Normalization (8:43)
TimeSeries: ChangePoint (5:04)
TimeSeries: Anomaly & Spike (1:22)
Section Summary (1:14)

AutoML

ModelBuilder UI - 1 (5:36)
ModelBuilder UI - 2 (4:29)
ML.Net CLI - 1 (3:22)
ML.Net CLI - 2 (3:46)
Section Summary (0:43)

ONNX

What is ONNX? (2:03)
Save as ONNX model (2:45)
Use ONNX model (4:01)

Misc

Use Model in ASP.Net (6:40)
Evaluation metric (2:11)

Extra shoots

Conclusion (1:29)
Bonus Lecture (1:54)
Promo Video (3:23)

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ML v/s AI v/s DL

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