Supervised and Unsupervised Machine Learning Algorithms

What is supervised machine learning and how does it relate to unsupervised machine learning?

In this post you will discover supervised learning, unsupervised learning and semis-supervised learning. After reading this post you will know:

  • About the classification and regression supervised learning problems.
  • About the clustering and association unsupervised learning problems.
  • Example algorithms used for supervised and unsupervised problems.
  • A problem that sits in between supervised and unsupervised learning called semi-supervised learning.

Let’s get started.

Supervised and Unsupervised Machine Learning Algorithms

Supervised and Unsupervised Machine Learning Algorithms
Photo by US Department of Education, some rights reserved.

Supervised Machine Learning

The majority of practical machine learning uses supervised learning.

Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.

Y = f(X)

The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.

It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance.

Get your FREE Algorithms Mind Map

Machine Learning Algorithms Mind Map

Sample of the handy machine learning algorithms mind map.

I've created a handy mind map of 60+ algorithms organized by type.

Download it, print it and use it. 

Download For Free

Also get exclusive access to the machine learning algorithms email mini-course.



Supervised learning problems can be further grouped into regression and classification problems.

  • Classification: A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”.
  • Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.

Some common types of problems built on top of classification and regression include recommendation and time series prediction respectively.

Some popular examples of supervised machine learning algorithms are:

  • Linear regression for regression problems.
  • Random forest for classification and regression problems.
  • Support vector machines for classification problems.

Unsupervised Machine Learning

Unsupervised learning is where you only have input data (X) and no corresponding output variables.

The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.

These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.

Unsupervised learning problems can be further grouped into clustering and association problems.

  • Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
  • Association:  An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.

Some popular examples of unsupervised learning algorithms are:

  • k-means for clustering problems.
  • Apriori algorithm for association rule learning problems.

Semi-Supervised Machine Learning

Problems where you have a large amount of input data (X) and only some of the data is labeled (Y) are called semi-supervised learning problems.

These problems sit in between both supervised and unsupervised learning.

A good example is a photo archive where only some of the images are labeled, (e.g. dog, cat, person) and the majority are unlabeled.

Many real world machine learning problems fall into this area. This is because it can be expensive or time-consuming to label data as it may require access to domain experts. Whereas unlabeled data is cheap and easy to collect and store.

You can use unsupervised learning techniques to discover and learn the structure in the input variables.

You can also use supervised learning techniques to make best guess predictions for the unlabeled data, feed that data back into the supervised learning algorithm as training data and use the model to make predictions on new unseen data.


In this post you learned the difference between supervised, unsupervised and semi-supervised learning. You now know that:

  • Supervised: All data is labeled and the algorithms learn to predict the output from the input data.
  • Unsupervised: All data is unlabeled and the algorithms learn to inherent structure from the input data.
  • Semi-supervised: Some data is labeled but most of it is unlabeled and a mixture of supervised and unsupervised techniques can be used.

Do you have any questions about supervised, unsupervised or semi-supervised learning? Leave a comment and ask your question and I will do my best to answer it.

Frustrated With Machine Learning Math?

Mater Machine Learning Algorithms

See How Algorithms Work in Minutes

…with just arithmetic and simple examples

Discover how in my new Ebook: Master Machine Learning Algorithms

It covers explanations and examples of 10 top algorithms, like:
Linear Regression, k-Nearest Neighbors, Support Vector Machines and much more…

Finally, Pull Back the Curtain on
Machine Learning Algorithms

Skip the Academics. Just Results.

Click to learn more.

160 Responses to Supervised and Unsupervised Machine Learning Algorithms

  1. Omot August 20, 2016 at 2:32 pm #

    Thanks for this post. That was helpful. My question is how does one determine the correct algorithm to use for a particular problem in supervised learning? Also,can a network trained by unsupervised learning be tested with new set of data (testing data) or its just for the purpose of grouping?

    • Jason Brownlee August 21, 2016 at 6:15 am #

      Hi Omot, it is a good idea to try a suite of standard algorithms on your problem and discover what algorithm performs best.

      Normally, an unsupervised method is applied to all data available in order to learn something about that data and the broader problem. You could say cluster a “training” dataset and later see what clusters new data is closest to if you wanted to avoid re-clustering the data.

      • SABARISH V April 7, 2018 at 2:44 pm #

        sir, does k-means clustering can be implemented in MATLAB to predict the data for unsupervised learning.

      • Tarun September 7, 2018 at 8:49 am #

        Which of the following is a supervised learning problem?
        A) Grouping people in a social network.
        B) Predicting credit approval based on historical data
        C) Predicting rainfall based on historical data
        D) all of the above

    • angel November 22, 2016 at 9:58 am #

      I need help in solving a problem. I have utilized all resources available and the school can’t find a tutor in this subject. My question is this: What is the best method to choose if you want to train an algorithm that can discriminate between patients with hypertension and patients with hypertension and diabetes. Please help me understand!

    • Seho Kim October 16, 2018 at 11:14 am #

      very informing article that tells differences between supervised and unsupervised learning!

  2. Pragya Poonia August 23, 2016 at 1:08 pm #

    This content is really helpful. Can you give some examples of all these techniques with best description?? or a brief introduction of Reinforcement learning with example??

  3. Naveen October 10, 2016 at 8:16 pm #

    Hi Jason,

    Thank you for summary on types of ML algorithms
    How can one use clustering or unsupervised learning for prediction on a new data. I have clustered the input data into clusters using hierarchical clustering, Now I want to check the membership of new data with the identified clusters. How is it possible. Is there an algorithm available in R?

    • Jason Brownlee October 11, 2016 at 7:23 am #

      Hi Naveen, generally I don’t use unsupervised methods much as I don’t get much value from them in practice.

      You can use the cluster number, cluster centroid or other details as an input for modeling.

  4. Tashrif October 25, 2016 at 9:03 am #

    Could you please give me a real world example of supervised, unsupervised, and semi supervised learning?

    • Jason Brownlee October 26, 2016 at 8:25 am #

      Hi Tashrif,

      Supervised would be when you have a ton of labeled pictures of dogs and cats and you want to automatically label new pictures of dogs and cats.

      Unsupervised would be when you want to see how the pictures structurally relate to each other by color or scene or whatever.

      Semi-supervised is where you have a ton of pictures and only some are labelled and you want to use the unlabeled and the labelled to help you in turn label new pictures in the future.

  5. Frank M November 12, 2016 at 7:38 am #

    This was a really good read, so thanks for writing and publishing it.

    Question for you. I have constructed a Random Forest model, so I’m using supervised learning, and I’m being asked to run an unlabeled data set through it. But I won’t have the actual results of this model, so I can’t determine accuracy on it until I have the actual result of it.

    So my question is… how can I run a set of data through a ML model if I don’t have labels for it?

    For further clarity and context, I’m running a random forest model to predict a binary classification label. I get the first few data points relatively quickly, but the label takes 30 days to become clear.

    Maybe none of this makes sense, but I appreciate any direction you could possibly give.

    Many thanks,


    • Jason Brownlee November 14, 2016 at 7:30 am #

      Thanks Frank. Great question.

      You will need to collect historical data to develop and evaluate your model.

      Once created, it sounds like you will need to wait 30 days before you can evaluate the ongoing performance of the model’s predictions.

  6. Ann November 17, 2016 at 8:29 pm #

    Hi Jason,
    Have done a program to classify if a customer(client) will subscribe for term deposit or not..
    dataset used: bank dataset from uci machine learning repository
    algorithm used: 1. random forest algorithm with CART to generate decision trees and 2.random forest algorithm with HAC4.5 to generate decision trees.

    my question is how do i determine the accuracy of 1 and 2 and find the best one???

    am really new to this field..please ignore my stupidity
    thanks in advance

  7. Nihad Almahrooq December 1, 2016 at 6:17 pm #

    Hi Jason, greater work you are making I wish you the best you deserving it.

    My question: I want to use ML to solve problems of network infrastructure data information. You know missing, typo, discrepancy. Fundamentals in knowledge and expertise are essential though need some ML direction and research more. Can you provide or shed light off that? And how? If you prefer we can communicate directly at

    Thanks and please forgive me if the approach seems awkward as startup and recently joint your connections it’s may be rushing!

    • Jason Brownlee December 2, 2016 at 8:14 am #

      Hi Nihad, that is an interesting application.

      Machine learning might not be the best approach for fixing typos and such. Nevertheless, the first step would be to collect a dataset and try to deeply understand the types of examples the algorithm would have to learn.

      This post might help you dive deeper into your problem:

      I hope this helps as a start, best of luck.

  8. Nischay December 24, 2016 at 8:11 am #

    Splendid work! A helpful measure for my semester exams. Thanks!!

  9. Sam January 1, 2017 at 4:11 am #

    hello Jason, greater work you are making I wish you the best you deserving it.
    I want to find an online algorithm to cluster scientific workflow data to minimize run time and system overhead so it can map these workflow tasks to a distributed resources like clouds .The clustered data should be mapped to these available resources in a balanced way that guarantees no resource is over utilized while other resource is idle.

    I came a cross a horizontal clustering ,vertical clustering but these technique are static and user should determine the number of clusters and number of tasks in each cluster in advance …

    • Jason Brownlee January 1, 2017 at 5:25 am #

      Hi Sam,

      Thanks for your support.

      Off-the-cuff, this sounds like a dynamic programming or constraint satisfaction problem rather than machine learning.

  10. Marcus January 6, 2017 at 6:55 am #

    Hi Jason, this post is really helpful for my Cognitive Neural Network revision!

    I have a question of a historical nature, relating to how supervised learning algorithms evolved:
    Some early supervised learning methods allowed the threshold to be adjusted during learning. Why is that not necessary with the newer supervised learning algorithms?

    Is this because they (e.g. the Delta Rule) adjust the weights on a running basis to minimize error, which supersedes the need for threshold adjustment? Or is there something more subtle going on in the newer algorithms that eliminates the need for threshold adjustment? Thank you in advance for any insight you can provide on this.

    • Jason Brownlee January 6, 2017 at 9:14 am #

      I don’t think I have enough context Marcus. It sounds like you may be referring specifically to stochastic gradient descent.

      I’m not really an algorithm historian, I’d refer you to the seminal papers on the topic.

  11. David Lehmann February 17, 2017 at 3:52 am #

    Hi Jason – Thanks so much for the informative post. I think I am missing something basic. Once a model is trained with labeled data (supervised), how does additional unlabeled data help improve the model? For example, how do newly uploaded pictures (presumably unlabeled) to Google Photos help further improve the model (assuming it does so)? Or how does new voice data (again unlabeled) help make a machine learning-based voice recognition system better? i understand conceptually how labeled data could drive a model but unclear how it helps if you don’t really know what the data represents.

    Thanks! Dave

    • Jason Brownlee February 17, 2017 at 10:01 am #

      Great question Dave.

      Generally, we can use unlabelled data to help initialize large models, like deep neural networks.

      More specifically, we can label unlabelled data, have it corroborate the prediction if needed, and use that as input to update or retrain a model to make be better for future predictions.

      Does that help?

      • Dave Lehmann February 18, 2017 at 2:50 am #

        yes thanks. So the data ultimately needs to be labeled to be useful in improving the model? Keeping with the Google Photos use case, all the millions of photos uploaded everyday then doesn’t help the model unless someone manually labels them and then runs those through the training? Guess I was hoping there was some way intelligence could be discerned from the unlabeled data (unsupervised) to improve on the original model but that does not appear to be the case right? thanks again for the help – Dave

        • Jason Brownlee February 18, 2017 at 8:43 am #

          There very well may be, I’m just not across it.

          • Amit Mukherjee July 12, 2018 at 5:45 pm #

            For a business which uses machine learning, would it be correct to think that there are employees who manually label unlabeled data to overcome the problem raised by Dave? The amount of unlabeled data in such cases would be much smaller than all the photos in Google Photos.

          • Jason Brownlee July 13, 2018 at 7:35 am #

            It is a good approach, e.g. to use local or remote labor to prepare/label a first-cut dataset.

  12. Rohit Thakur March 20, 2017 at 10:48 pm #

    Can you write a blog post on Reinforcement Learning explaining how does it work, in context of Robotics ?

  13. Hansa April 12, 2017 at 8:05 pm #

    Hi Jason,

    I am trying to solve machine learning problem for Incidents in Health & safety industry.
    I want to recommend the corrective or preventive actions based on the Incident happening at given site.
    I am trying to understand which algorithm works best for this.
    Could you please share your thoughts.


  14. Aigerim April 18, 2017 at 11:08 pm #

    I need help in solving a problem. I am writing thesis about Unsupervised Learning of Morphology of Turkish language. It is my first thesis about this area. My question is this: I have to write math model of morphology and I am trying to understand which algorithm works best for this. Could you please give me same important information. Please help me understand!

  15. lilya April 23, 2017 at 7:06 pm #

    Hi Jason,

    please I need help in solving my problem which is : i want to do supervised clustering of regions ( classify regions having as response variable : frequence of accidents ( numeric response) and explanatory variables like : density of population , density of the trafic) i want to do this using Random forest is it possible ?

  16. Nuwan C April 27, 2017 at 12:14 pm #

    Hi Json,
    Thnc for the article and it is wonderful help for a beginner and I have a little clarification about the categorization.

    I saw some articles devide supervice learning and unsupervise and reinforcement.

    Is that same meaning of semi supervising and reinforcement gives?

  17. violet May 5, 2017 at 3:09 pm #

    Good one! Thanks a lot. Jason, you did great!It was so simplified. But I will love to have an insight as simplified as this on Linear regression algorithm in supervised machine. Thanks once more

  18. Anubhav May 23, 2017 at 8:00 pm #

    Good one! I an novice to ML. So Timeseries based predictive model will fall under which category Supervised, Unsupervised or Sem-supervised? and why?

    • Jason Brownlee May 24, 2017 at 4:54 am #

      Time series forecasting is supervised learning.

      • Fred October 31, 2018 at 5:26 am #

        What are 10 difficulties or problems faced anyone want to get data mining about in this topic “Prediction of Portuguese students’ performance on mathematics class in high schools”?

        • Jason Brownlee October 31, 2018 at 6:31 am #

          Sounds like a homework question, I recommend thinking through it yourself Fred.

  19. Balaji June 20, 2017 at 2:36 am #

    HI Jasan
    simple and easy to understand contents.
    I am an ML enthusiast looking for material that groups important and most used algorithms in to supervised and unsupervised.

    Supervised – Regression, Classification, Decision tree etc..
    Unsupervised – Cluster, etc..

    do you have ?

  20. Eashan Roy July 1, 2017 at 5:18 am #

    Given data on how 1000 medical patients respond to an experiment drug( such as effectiveness of treatment, side effects) discover whether there are different categories or types of patients in terms of how they respond to the drug and if so what these categories are.

    Is this supervised or unsupervised learning ?

    • Jason Brownlee July 1, 2017 at 6:38 am #

      Sounds like unsupervised to me.

      • David July 3, 2017 at 5:37 pm #

        I have over 1million sample input queries.. I want to classify into genuine or malicious query.. Every query consist of keywords but there are some specific keywords that may help identify malicious query or not. However not every of the possible malicious keyword may consider the whole query malicious… I’m not sure how to present my problem here but Let me ask this first… Is it possible to have 2 levels of classification(supervised) and 1 level of clustering(unsupervised) in solving a problem like this..?

  21. Sana August 2, 2017 at 12:22 am #

    Thanks Jason it is really helpful me in my semester exam

  22. Blessing August 12, 2017 at 11:59 am #

    Hi Jason, thank you for the post. I have a question. Does an unsupervised algorithm search for a final hypothesis and if so, what is the hypothesis used for. Are target functions involved in unsupervised learning? What does an unsupervised algorithm actually do?
    I understand supervised learning as an approach where training data is fed into an algorithm to learn the hypothesis that estimates the target function. However, for an unsupervised learning, for example, clustering, what does the clustering algorithm actually do? what does “concept learning” mean when it comes to unsupervised machine learning? I noticed that most books define concept learning with respect to supervised learning. Thank you

    • Jason Brownlee August 13, 2017 at 9:45 am #

      I don’t like unsupervised methods in general – I don’t find their results objective – I don’t think they are falsifiable therefore I can’t judge if they’re useful.

      They work by applying a methodology/process to data to get an outcome, then it is up to the practitioner to interpret the results – hopefully objectively.

      You’ll notice that I don’t cover unsupervised learning algorithms on my blog – this is the reason.

  23. abhi August 30, 2017 at 2:25 pm #

    Hi Jason,

    I am following your Tutorials from Last couple of weeks. Thanks for such awesome Tutorials for beginners.

    I have one problem for which I want to use ML algorithm. I tried Cats and Dogs for small dataset and I can predict correct output with Binary Cross entropy.

    Now To apply to my own dataset problem I want to classify images as Weather they are Cat or Dog or any other(if I provide Lion image). But all I get is only 0 & 1 for cat and dog class.

    Model.predict should give me different output if image is not cat or dog.
    Also , How Can I get % prediction that says. Yes this image is quite similar to cat/dot with test result accuracy as 80% or more. If I provide mountain/lion image then it should give me output as it is 10% or less than 50% so I can say it is not cat or dog but something other??

  24. Keval September 15, 2017 at 12:28 am #

    I am wondering where does a scoring model fit into this structure? I am trying to define my problem as an ML problem, however, I do not have any labeled data as I am just starting to work with the data. The output variable in my case is a score that is calculated based on select features from the dataset. How would you classify this problem and what techniques would you suggest exploring?

  25. Ares September 23, 2017 at 3:02 pm #

    Hii Jason .. Thank you for the post… I am new to Machine Learning…How should i start with Machine learning.. Should i study all the concepts first or should i code algorithms which i study simultaneously ??? Thanks

  26. Ahmed Fathy September 27, 2017 at 3:23 am #

    i have some of images about mango diseases. i want to make segmentation, feature extraction, classification … what is the best and common algorithms for this issue ??

  27. scott October 13, 2017 at 9:51 am #

    Hey there, Jason – Good high-level info. Truthfully, I found the grammar and spelling errors distracting. They make software for that. 😉

  28. SANDEEP S KUMAR November 4, 2017 at 12:14 pm #

    what are the examples of semi supervised learning algorithms

  29. Aditi Kadam November 6, 2017 at 5:57 am #

    Thank you! It was a great explanation

  30. Vinu Nair December 7, 2017 at 5:07 pm #

    Hi Jason,
    Good work.Could you please help me to find a algorithm for below mentioned problem .
    We have number of record groups which have been grouped manually . We needs to automate these grouping by analysis on this history data.

  31. Vinu Nair December 7, 2017 at 10:12 pm #

    Could you please share some algorithm for finding matching patterns

  32. Haneen December 20, 2017 at 9:52 pm #

    you are amazing, Thank you so match

  33. Ali December 21, 2017 at 10:44 pm #

    What is supervised and unsupervised learning? Which learning techniques could be better in particular machine learning domain? Which technique has limitations and why?

  34. Krishna December 27, 2017 at 3:31 pm #

    Very helpful to understand what is supervised and unsupervised learning. Its very better when you explain with real time applications lucidly.

  35. Pulkit Aggarwal December 28, 2017 at 11:00 pm #

    Hello, Sir Jason I’m new to Machine Learning and want to learn it from the scratch.Please guide me to do so.

  36. MUHAMMAD OMER January 12, 2018 at 10:16 pm #

    What kind of data we use reinforcement learning?
    guide me.

  37. Ritesh K. Patel (PhD) January 13, 2018 at 6:25 am #


  38. Glad January 18, 2018 at 8:23 am #

    Nice one, but I need more explanation on unsupervised learning please

    • Jason Brownlee January 18, 2018 at 10:15 am #

      What questions do you have about unsupervised learning exactly?

  39. Glad January 22, 2018 at 6:58 am #

    Examples of unsupervised machine learning

  40. Fernando January 24, 2018 at 9:35 pm #

    Hi Jason,

    My problem is related to NLP and sentiment analysis.

    I have a dataset with a few columns. One of them is a free text and another one is a sentiment score, from 1 (negative) to 10 (positive).

    I’m trying to apply a sentiment analysis to the text field and see how well it works comparing with the sentiment score field. For this purpose, I’ve run some off-the-self sentiment analysis tools, such as Polyglot, but they didn’t work very well. That’s why I’ve decided to address this as a classification problem (negative, neutral or positive).

    In order to do this, I’ve got 1, 2 and 3-grams and I’ve used them as features to train my model. I tried with SVM and also getting the most representative grams for each of these classes using z-score, but the results were worst than with Polyglot.

    Any suggestion?


  41. Satyam January 25, 2018 at 4:09 am #

    What are some widely used Python libraries for Supervised Learning?

  42. Mayur February 1, 2018 at 12:11 am #

    What will be the best algorithm to use for a Prediction insurance claim project?

  43. Sri February 7, 2018 at 1:13 pm #

    Hi Jason,

    you are awesome. Sorry if my question is meaningless. In simple what is relation between Big Data, Machine Learning, R, Python, Spark, Scala and Data Science?


    • Jason Brownlee February 8, 2018 at 8:21 am #

      You can probably look up definitions of those terms. Why are you asking exactly?

  44. Chibuzor February 9, 2018 at 2:43 am #

    Hello Jason,
    That was a good one, keep it up,
    Please, what is your advised for a corporation that wants to use machine learning for archiving big data, developing AI that will help detect accurately similar interpretation and transform same into a software program.
    Secondly, Beside these two areas, are there other areas you think AI will be helpful for industrialists. Let me know you take.


  45. Grant Morgan February 10, 2018 at 7:10 pm #

    Interesting read.

    Do you have a suggestion for where for a given input (image) choosing a particular point p gives a reward r. the goal is to maximize r. There may me multiple points that return the same maximum r value, so I don’t see standard a cnn training methods working. It does not matter which one is returned the reward is the same. Each trial is separate so reinforcement learning does not seem correct.

    • Jason Brownlee February 11, 2018 at 7:55 am #

      Sounds like a multimodal optimization problem. If you only need one result, one of a range of stochastic optimization algorithms can be used.

      If you need all points, then a multimodal optimizaiton could be used, like a niching genetic algorithm (I did my masters on these).

  46. Bilal Khan February 12, 2018 at 6:11 am #

    Very Helping Material i was preparing for my exams and i have completely understood the whole concept it was very smoothly explained JAZAKALLA (Means May GOD give you HIS blessing )

  47. Marwa February 20, 2018 at 4:59 pm #

    you can give me an explanation about the classes of unsupervised methods: by block, by pixel, by region which used in the segmentation.

    • Jason Brownlee February 21, 2018 at 6:37 am #

      Sorry, I don’t follow. Perhaps you can provide more context?

  48. nikhil February 26, 2018 at 4:03 pm #

    sir, can you tell real time example on supervised,unsupervised,semisupervised

    • Jason Brownlee February 27, 2018 at 6:24 am #

      Linear regression is supervised, clustering is unsupervised, autoencoders can be used in an semisupervised manner.

  49. Shivani March 12, 2018 at 4:06 pm #

    Sir, thank u for such a great information.
    But how can we use unsupervised learning for any type of clustering?

    • Jason Brownlee March 13, 2018 at 6:23 am #

      Sorry, I don’t have material on clustering. I may cover it in the future.

  50. Kristy March 13, 2018 at 4:06 am #

    Thanks for posting this. This is a great summary! Very straightforward explanations.

  51. Charalampos March 14, 2018 at 1:30 am #

    First of all very nice and helpfull report, and then my question.

    I have an unsupervised dataset with people and i want to find some paterns about their behaviour for future marketing. I am using clustering algorythms but then if i want to train a model for future predictions (for a new entry in the dataset, or for a new transaction of an already registered person in the dataset) should i use these clusters as classes to train the model as supervised classification? Or how can i do this? i am confused.

    Thank you in advance!!

    • Jason Brownlee March 14, 2018 at 6:28 am #

      Perhaps start with a clear idea of the outcomes you require and work backwards:

      • Charalampos March 14, 2018 at 7:14 pm #

        Thank you for your reply, but this couldnt help me too much..

        Some people, after a clustering method in a unsupervised model ex. k-means use the k-means prediction to predict the cluster that a new entry belong. But some other after finding the clusters, train a new classifier ex. as the problem is now supervised with the clusters as classes, And use this classifier to predict the class or the cluster of the new entry. I cant understand the difference bettween these two methods. I dont know if you understand my point but i would appreciate if you try to explain it to me..

        • Jason Brownlee March 15, 2018 at 6:27 am #

          Sorry, I don’t have material on clustering, I cannot give you good advice.

  52. pavan March 22, 2018 at 12:08 am #

    Thank You for the giving better explanation.

  53. Emran March 27, 2018 at 5:23 am #

    given that some students information such as(Name,Address,GPA-1,GPA-2, and Grade),,,,my job is to “divide students based on their grade”… my question is the this job is supervise or unsupervised learning? and which Machine learning algorithm is perfect to do this job…

    I thing it will be Unsupervised learning but i am confused about what algorithm perfect for this job….(is it clustering)… am i right sir?

  54. Harathi April 6, 2018 at 8:41 am #

    Hi Jason,

    I have documents with handwritten and machine printed texts. I want to localize the text in the document and find whether the text is handwritten or machine printed. If the text is handwritten, i have to give it to a handwritting recognition algorithm or if it is machine printed, I have to give it to tesseract ocr algorithm.

    Can you please suggest me how to do text localization and find whether the text is handwritten or machine printed..

    Thanks in advance,

    • Jason Brownlee April 6, 2018 at 3:47 pm #

      I would recommend looking into computer vision methods. I do not cover this area sorry.

  55. Saqi April 12, 2018 at 8:25 pm #

    hi, im new to machine learning im struck in the machine learning in training the data please help me with this, like Create a Keras neural network for anomaly detection,please can you fix the error i have tried several times no idea what is the problem

    stuck at task 3
    check in gist url
    features = train_both[:,:-1]
    labels = train_both[:,:-1]

    ths gist url:

    • Jason Brownlee April 13, 2018 at 6:39 am #

      I’m eager to help, but I don’t have the capacity to debug your code for you.

      Perhaps post on stackoverflow?

  56. Anfell May 23, 2018 at 3:00 am #

    Hi Jason, nice post btw.
    I was wondering what’s the difference and advantage/disadvantage of different Neural Network supervised learning methods like Hebb Rule, Perceptron, Delta Rule, Backpropagation, etc and what problems are best used for each of them.

    • Jason Brownlee May 23, 2018 at 6:31 am #

      We do not have a mapping of problems to algorithms in machine learning. The best we can do is empirically evaluate algorithms on a specific dataset to discover what works well/best.

  57. Moti June 4, 2018 at 3:14 am #

    I need a brief description in machine learning and how it is applied. Where and when it were required?

  58. Shreya Gupta June 6, 2018 at 11:53 pm #

    Amazing post.. Actual complete definitions are provided.. Thanks for it 🙂

  59. Nora July 3, 2018 at 8:27 am #

    Hi sir
    Thank you advance for your article, it’s very nice and helpful
    Iam new in machine learning and i would like to understand what is mean deep learning? Second, distance supervise wether like semisuperviser or not?

    Thanks advance

  60. Vinay July 9, 2018 at 5:41 pm #

    Hello Jason Brownlee,

    I was working on a health research project which would detect snore or not from input wav file. Can you please suggest which one i would prefer Supervised learning or Unsupervised learning or Semi-Supervised learning. i’m a iOS Developer and new to ML. Where do i start from?
    Your advise will help a lot in my project.

    Thanks in Advance!

  61. diwakar reddy July 11, 2018 at 3:32 pm #

    could you explain semi supervised machine learning a bit more with examples.

  62. yzdu August 11, 2018 at 9:18 pm #

    Dear prof Brownlee:

    From my understanding, method based on unsupervised leaning(no labels required) can’t compare with those based on supervised leaning(labels required) since their comparison premise is different. I f one wants to compare them, one should put them under the same problem scenarios,only this way, comparison is reasonable and fair,isn’i it? but provided that the problem scenarios are applictions without labels, they can’t compare with each other since supervised leaning methods need lables to train models,but now there are no labels to be trained, therefore I think it is unreasonable and infeasible to compare method based on unsupervised leaning with those based on supervised leaning,is it right? I want to know your views, thank you!

    • Jason Brownlee August 12, 2018 at 6:32 am #

      Yes, they are not comparable. They solve different problems.

  63. Navdeep Kapur August 22, 2018 at 2:20 am #

    Hi Jason,

    Your article was very informative and cleared lot of my concepts. I have lot of questions in my mind about Machine Learning. Is it possible you can guide me over Skype call and I am ready to pay.

  64. Lalam Rajesh September 27, 2018 at 3:59 pm #

    Why association rules are part of unsupervised learning?

    • Jason Brownlee September 28, 2018 at 6:05 am #

      There is no training/teaching component, the rules are extracted from the data.

  65. Fasih Ahmed September 27, 2018 at 8:29 pm #

    Hello Jason,

    Great explanation,
    i have a question , I am doing ML in JAVA ,can you suggest me how can i choose best algorithm for my data?
    as i am using numeric data (Temperature sensor) which method is best supervised or unsupervised ?
    Hope u got my point

  66. Noel gipson October 10, 2018 at 5:20 pm #

    Hello, I am Noel, I am new to machine learning with less experience. I want to make a machine learning model to predict the possibility of any attack or abnormal events/behavior to my system. the model should classify the situation based on the security level of it and give me the predictable cause and solution. What to do on this guys

  67. Akshay October 21, 2018 at 10:57 pm #

    I’m thankful to you for such a nice article!
    I would love to follow you and your articles further.

  68. Miled Basma Bentaiba October 27, 2018 at 5:13 am #

    I never understood what the semi-supervised machine learning is, until I read your post. The issue was whether we can have new labels after processing or we are based only on the first given labels. The example you gave made it all clear. So, the answer is, we don’t have all the labels, that’s why we join unlabeled data.

    Thank you for your great posts!

  69. Kate Weeks November 7, 2018 at 10:28 am #

    Hey Jason,

    Love your books and articles. Any chance you’ll give us a tutorial on K-Means clustering in the near future?


  70. Navya Mandava December 3, 2018 at 10:51 pm #

    Hi Jason ,

    Thanks for clarifying my dough’s between supervised and unsupervised machine learning. But one more dough’s , how can i justify or apply the correct algorithm for particular problem . Is their any easy way to find out best algorithm for problem we get. Could you please let me know ?

  71. Aditya Rana December 6, 2018 at 9:13 am #

    Thank you so much for all the time you put in for educating and replying to fellow learners. Thanks for being such an inspiration.

Leave a Reply