This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Machine Learning: Algorithms in the Real World
von
Spezialisierung Machine Learning: Algorithms in the Real WorldAlberta Machine Intelligence InstituteÜber diesen Kurs
33,905 kürzliche Aufrufe
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Ca. 9 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Chinesisch (vereinfacht), Italienisch, Vietnamesisch, Koreanisch, Deutsch, Russisch, Türkisch, Englisch, Spanisch
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Ca. 9 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Chinesisch (vereinfacht), Italienisch, Vietnamesisch, Koreanisch, Deutsch, Russisch, Türkisch, Englisch, Spanisch
von
Alberta Machine Intelligence Institute
The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based
Lehrplan - Was Sie in diesem Kurs lernen werden
4 Stunden zum Abschließen
Classification using Decision Trees and k-NN
Welcome to Supervised Learning, Tip to Tail! This week we'll go over the basics of supervised learning, particularly classification, as well as teach you about two classification algorithms: decision trees and k-NN. You'll get started programming on the platform through Jupyter notebooks and start to familiarize yourself with all the issues that arise when using machine learning for classification.
4 Stunden zum Abschließen
8 Videos (Gesamt 46 min), 4 Lektüren, 2 Quiz
8 Videos
What does a classifier actually do?5m
Classification in scikit-learn3m
What are decision trees?6m
Generalization and overfitting8m
Classification using k-nearest neighbours8m
Distance measures8m
Weekly summary2m
4 Lektüren
Math Review10m
Scikitlearn documentation for decision trees (Optional)10m
Scikitlearn documentation for random forests (Optional)10m
Scikitlearn documentation for k-nearest neighbours (Optional)10m
2 praktische Übungen
Supervised Learning Basics
Understanding Classification with Decision Trees and k-NN20m
2 Stunden zum Abschließen
Functions for Fun and Profit
Welcome to the second week of the course! In this week you'll learn all about regression algorithms, the other side of supervised learning. We'll introduce you to the idea of finding lines, optimization criteria, and all the associated issues. Through regression we'll see the interactions between model complexity and accuracy, and you'll get a first taste of how regression and classification might relate.
2 Stunden zum Abschließen
9 Videos (Gesamt 62 min), 1 Lektüre, 4 Quiz
9 Videos
Optimal line-fitting8m
Loss and Convexity7m
Gradient Descent9m
Nonlinear features and model complexity6m
Bias and variance tradeoff6m
Regularizers5m
Loss for Classification7m
Weekly summary4m
1 Lektüre
Scikitlearn documentation for linear regression (Optional)10m
4 praktische Übungen
Regression Basics
Understanding Model Complexity
From Regression to Classification2m
The Regression side of Supervised Learning20m
3 Stunden zum Abschließen
Regression for Classification: Support Vector Machines
This week we'll be diving straight in to using regression for classification. We'll describe all the fundamental pieces that make up the support vector machine algorithms, so that you can understand how many seemingly unrelated machine learning algorithms tie together. We'll introduce you to logistic regression, neural networks, and support vector machines, and show you how to implement two of those.
3 Stunden zum Abschließen
6 Videos (Gesamt 34 min), 1 Lektüre, 2 Quiz
6 Videos
Neural Networks9m
Hinge Loss6m
Basics of Support Vector Machines6m
Kernels6m
Weekly Summary1m
1 Lektüre
Scikitlearn documentation for SVMs (Optional)10m
2 praktische Übungen
Understanding Support Vector Machines
Regression-based Classification10m
1 Stunde zum Abschließen
Contrasting Models
Now at the tail end of the course, we're going to go over how to know how well your model is actually performing and what you can do to get even better performance from it. We'll review assessment questions particular to regression and classification, and introduce some other tools that really help you analyze your model performance. The topics covered this week aim to give you confidence in your models, so you're ready to unlock the power of machine learning for your business goals.
1 Stunde zum Abschließen
8 Videos (Gesamt 46 min), 1 Lektüre, 1 Quiz
8 Videos
Classification assessment6m
Learning Curves6m
Testing your models7m
Cross validation5m
Parameter tuning and grid search5m
Model Parameters6m
Weekly Summary1m
1 Lektüre
Some resources on model assessment (Optional)10m
1 praktische Übung
Contrasting Models
Bewertungen
Top-Bewertungen von MACHINE LEARNING ALGORITHMS: SUPERVISED LEARNING TIP TO TAIL
von DS6. Mai 2020
Excellent course for an overview of different ML algorithms. The course is made from a perspective of giving insights in process and not too many mathematical details.
von SK11. Apr. 2020
Excellent course. In which I had in-depth knowledge of all algorithms and the way she explained attracts to listen except for her spontaneity and speed in progressing.
von CW29. Sep. 2020
Great course, easy to grasp the main idea of how to assess and tune the performance of question-answering machines learned by machine learning algorithms through data
von RM8. Dez. 2020
I found the course to be enough detailed to get clarity on the basic concepts of Supervised learning algorithms. I hope to apply the learning from the course in work!
Über den Spezialisierung Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application.
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