Machine Learning
237 resources created by 183 users
This Machine Learning page on GoConqr gathers community-made quizzes, mind maps, flashcards and notes, from logistic regression and decision trees to clustering, PCA and recommendation systems. It's built for university students revising a topic or filling gaps, and for teachers after ready-made material. There are over 230 resources here, including more than 20 quizzes, over 100 mind maps and more than 50 flashcard decks, almost all at university level. Pick a topic that matches your course and see what the community has made.
Top Machine Learning resources
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40 questionsKnowledge Check - Post Which of the following descriptions accurately describes Azure Machine Learning?
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7 questionsLogistic regression Logistic regression is a classification algorithm used to assign observations to a discrete set of classes.
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80 questionsMachine Learning Final Exam Prep Missing values can have meaning.
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Mind map Bayesian networks BN Datamining AI -
16 questionsMachine Learning for Data Science Where is machine learning used?
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13 questionsLinear Regression Regression analysis is a form of predictive modelling technique which investigates the relationship between...
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Mind map eXplainable AI Editor: Christoforos Spartalis Reference: ML interpretability: A survey on methods and metrics - Carvalho e...
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Mind map Machine Learning Machine Learning Mind Map based on the book "Hands-On Machine Learning with Scikit-Learn and TensorFlow".
Quizzes
See all 30 quizzes-
40 questionsKnowledge Check - Post Which of the following descriptions accurately describes Azure Machine Learning?
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7 questionsLogistic regression Logistic regression is a classification algorithm used to assign observations to a discrete set of classes.
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80 questionsMachine Learning Final Exam Prep Missing values can have meaning.
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16 questionsMachine Learning for Data Science Where is machine learning used?
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13 questionsLinear Regression Regression analysis is a form of predictive modelling technique which investigates the relationship between...
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2 questionsCollection of multiple-choice interview questions 1. Given the following train and eval loss curves, what actions would help?
Mind maps
See all 106 mind maps-
Bayesian networks BN Datamining AI
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eXplainable AI Editor: Christoforos Spartalis Reference: ML interpretability: A survey on methods and metrics - Carvalho e...
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Machine Learning Machine Learning Mind Map based on the book "Hands-On Machine Learning with Scikit-Learn and TensorFlow".
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COMP2611 - Uncertainty A mind map containing everything to do with uncertainty in respect to the Uni of Leeds COMP2611 Module.
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Machine Learning -
Machine Learning -
Computer science algorithm. This mind map i will go over the algorithm kinds and types.
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machine learn
Flashcards
See all 54 flashcardsNotes
See all 40 notes-
Dimensionality reduction and Visualization: What is Dimensionality reduction? We realize that we can visualize data in 2-D or 3-D using a simple scatter plot and for 4-D,5-D and even 6-D, we use pair plot to sense how data is. Note: If we have to visualize our ...
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Edx- Course intro- 1) identify the sequence pattern. 2) make prediction about the next based on previous computers are good at doing calculations. So our job is to define a way for computer to do calculation in correct way....
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week 3
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Machine learning: Supervision Machine Learning: Supervision Machine learning algorithms can be broadly classified in terms of the amount of human supervision they require. The categories are Supervised Unsupervised Semisupervised lear...
More Databases and data science topics
About Machine Learning
GoConqr OriginalWhat this unit page covers
The resources here were made by GoConqr users and they range widely. You will find notes on supervised and unsupervised learning, linear and multivariate regression, gradient descent and the normal equation. There are also mind maps on classification, clustering, Bayesian networks, regularization and semi-supervised learning.
Where to start
If you are new to the topic, begin with the flashcard deck called Machine Learning. It runs through supervised versus unsupervised learning, linear regression, the cost function, gradient descent and logistic regression. Then open the Machine Learning mind map, which links ideas such as classification, regression, generalisation error and the VC dimension.
For dimensionality reduction, read the notes on Principal Component Analysis. They cover the geometric intuition, the objective function and eigenvalues and eigenvectors. The notes on dimensionality reduction and visualization cover data matrices and feature normalisation, so they pair well with them.
Using quizzes and maps together
- Read a mind map such as Classification, which breaks down decision trees, nodes, branches, leaves and information gain.
- Take a quiz on the same idea, for example the Logistic regression quiz or Lecture 3 - Decision Tree.
- Note which answers you missed and go back to the matching map branch.
- Turn those gaps into flashcards, using decks like Naive Bayes or Regression as a model.
Beyond the basics
Some resources go further. The Rec sys flashcards explain embeddings, candidate generation, scoring and re-ranking. The UFLDL Tutorial mind map covers sparse autoencoders, PCA and whitening. The Knowledge Check - Post quiz asks about Azure Machine Learning, and the deck on MLOps looks at deployment. Teachers can pick from these to set as revision tasks.
Where it sits
This unit belongs to the strand Machine Learning and Artificial Intelligence, within Databases and data science. Choose one resource that matches your next lecture, work through it, and then add your own notes or cards beside it.
Frequently asked questions
Which resource should I open first?
Start with the flashcard deck called Machine Learning. It covers supervised versus unsupervised learning, linear regression, the cost function, gradient descent and logistic regression. After that, open the Machine Learning mind map to see how the ideas connect, then test yourself with a quiz.
What do the mind maps cover?
There are over 100 of them. Topics include classification and decision trees, clustering, DBSCAN, Bayesian networks, regularization, semi-supervised learning, recommendation systems and eXplainable AI. Some follow a single lecture, such as Lecture 7 on classification for targeting, so they are easy to match to your course.
Which quizzes are there for regression and classification?
You can try the Logistic regression quiz, the Linear Regression quiz and Lecture 3 - Decision Tree. There are also Lecture 2 - Naive Bayes, Neural Network, and test-style sets on clustering, regression analysis and logit regression. Each lets you check a topic before moving on.
Are the resources free, and who made them?
Every resource on this page is free to open, study and take, with no account needed. They were made by GoConqr users, not by GoConqr itself. A free account also saves your quiz scores and lets you make your own quizzes, mind maps, flashcards and notes.
Who is this page for?
It suits university students who want to revise a topic or fill gaps, and teachers looking for ready-made material. Nearly all the resources are at university level, with a small share aimed at professionals. Students can practise recall with flashcards and quizzes, and teachers can borrow maps and notes.
What if I cannot find my topic?
Try a nearby title first. Ideas such as PCA, overfitting, hyperparameters and cost functions appear in several notes, maps and decks under different names. If nothing fits, make your own mind map, flashcards or notes on the topic, which a free account lets you do.