Data Science With C# And ML.NET by Mark Farragher

Data Science With C# And ML.NET

This course will introduce you to Data Science and AI and get you up to speed with Microsoft's new ML.NET Machine Learning library.

What my students are saying...

‘After hearing about neural networks for years without actually using them, I am proud to say I have successfully trained and used my first neural network – in C#. Thank you so much, Mark. Neural Networks are ridiculously awesome.’
Joel Dokmegang
'I found that participating on such a well-taught course was an awesome experience for me. I think Mark is a gifted teacher as well as amazing technically skilled. It was truly an enlightening experience'
Yoav Kaplan

What you'll learn...

  • The fundamentals of Data Science in C#
  • Supervised and Unsupervised Learning
  • The Microsoft ML.NET Machine Learning library
  • Regression and Classification
  • Decision Trees and Ensembles

  • Optimizing Machine Learning models
  • Unsupervised Data Clustering
  • Recommendation Systems
  • Building Data Science apps in C#
  • Completing real-world Case Studies

Course requirements

For this course you'll need:

  • A Windows, OS/X, or Linux computer
  • NET Core version 3
  • Visual Studio Code
  • At least 1 hour available per weekday

Course description

Are you a CTO, tech leader, or software developer wondering what the Machine Learning hype is all about? Would you like to start experimenting with Data Science Models in your organization?

Then this is the course for you!

In this course, you’re going to master the fundamentals of data science in C#. You will learn about loading and processing data, and analyzing data with Linear Regression, Binary and Multiclass Classification. You will build many data science apps with Decision Trees, Ensembles, Clustering Algorithms, and Recommendation Systems. You will also learn how to apply Data Science to real-world Case Studies.

After completing the course, you will be able to design and train your own C# data science models for a large number of problem domains. You will have built price and demand prediction models, medical classifiers for healthcare, a movie recommendation system, and many other practical data science applications.

What's included?

Video Icon 46 videos Text Icon 75 text files

Here's what's in the course...

Course Introduction
I'm pleased to meet you!
Welcome to the course
11 mins
Course prerequisites
10 mins
Installing NET Core 3.0
Installing Visual Studio Code
Introduction To Data Science
What is data science?
Loading And Processing Data
Introduction
2 mins
In this section...
Introducing numeric data
11 mins
Loading numeric data
Introducing string data
10 mins
Loading string data
Introducing geo data
16 mins
Loading Geo data
Quiz
Your assignment
6 mins
Assignment: Process California housing data
My answers
Recap
Supervised Learning
Introducing supervised learning
5 mins
Supervised learning
Regression
Introduction
2 mins
In this section...
Introducing linear regression
10 mins
Single linear regression
Introducing regression metrics
12 mins
RMSE, MSE, and MAE
Introducing gradient descent
13 mins
Gradient descent
Introducing multiple linear regression
13 mins
Multiple linear regression
Quiz
Your assignment
4 mins
Assignment: Predict taxi fares in New York
My answers
Recap
Case study
Predict house prices in Iowa
Binary Classification
Introduction
2 mins
In this section...
Introducing binary classification
15 mins
Binary classification
Introducing binary metrics
14 mins
Accuracy, Precision, and Recall
Introducing ROC and AUC
18 mins
ROC, AUC, and Bias
Quiz
Your assignment
6 mins
Assignment: Predict heart disease risk
My answers
Recap
Case study
Detect credit card fraud in Europe
Multiclass Classification
Introduction
3 mins
In this section...
Introducing multiclass classification
13 mins
Multiclass classification
Introducing multiclass metrics
15 mins
The confusion matrix
Micro and macro averages
Quiz
Your assignment
6 mins
Assignment: Recognize handwritten digits
My answers
Recap
Training And Evaluating Models
Introduction
3 mins
In this section...
Introducing overfitting
12 mins
Overfitting
Introducing partitioning
13 mins
Partitioning datasets
Minibatch training
Introducing K-fold cross validation
14 mins
K-Fold Cross Validation
Quiz
Your assignment
7 mins
Assignment: Detect spam messages
My answers
Recap
Case study
Flag toxic comments on Wikipedia
Decision Trees
Introduction
2 mins
In this section...
Introducing classification trees
15 mins
Classification trees
Introducing regression trees
9 mins
Regression trees
Quiz
Your assignment
4 mins
Assignment: Predict Titanic survivors
My answers
Recap
Case study
Detect diabetes in Pima indians
Ensemble Models
Introduction
3 mins
In this section...
Introducing ensemble models
9 mins
Ensemble models
Introducing bagging
8 mins
Bagging
Introducing boosting
8 mins
Boosting
Introducing stacking
10 mins
Stacking
Quiz
Assignment: Predict bike demand in Washington DC
My answers
Recap
Unsupervised Learning
Introducing unsupervised learning
8 mins
Unsupervised learning
Clustering
Introduction
2 mins
In this section...
Introducing clustering
16 mins
K-Means Clustering
Introducing clustering metrics
7 mins
The Davies Bouldin Index
Quiz
Assignment: Classify unlabeled Iris flowers
My answers
Recap
Recommendation Systems
Introduction
2 mins
In this section...
The challenge
Introducing PCA
15 mins
Introducing SVD
17 mins
Quiz
Your assignment
5 mins
Assignment: Recommend movies
My answers
Recap
In Conclusion
Course recap
11 mins
What you've learned