The 7 Steps of Machine Learning (AI Adventures)

By | February 6, 2020

100 thoughts on “The 7 Steps of Machine Learning (AI Adventures)

  1. Hannah Humphreys Post author

    Introduction to Machine Learning for Data Science

    > The Impacts Machine Learning and Data Science is having on society.
    >To know what problems Machine Learning can solve, and how the Machine Learning Process works.

    #MachineLearning #DataScience

  2. SuBsCribe f0r nO resan plese Post author

    Computers are amazing and INTERESTING!

  3. Simon Kalu Post author

    Nice video, nice explanation of ML. more videos or even a series would be most appreciated. IA and other advanced concept should be taught same way

  4. simarjit kaur Post author

    this is good start… thankyou very much… m new to ML… its actually gonna help me in my project

  5. Ádám Jakab Post author

    Very interesting. How would you handle situations where datapoints from two different categories overlap? A white wine that is close in colour and alcohol content to a white ale? Also, the model you describe is a linear split between the categories. But is that always the case?

  6. regortaz Post author

    Sorting cucumbers. The most important function of machine learning.

  7. Joanna Spiska Post author

    This video is really great! I would like to know more about machine learning! Do you know I found this company and read that they have machine learning in their offer, so maybe you have heard about them 🙂

  8. AgarWorstPlayer Post author

    What are examples of machine learning in a trading manager.mq4 EA?

  9. Abdullah Aghazadah Post author

    quick summary:
    – machine learning is all about seeing some examples of input-output pairs and then being able to predict the output for new inputs
    – basically, you feed a bunch of examples to a machine, and the machine will start to learn about the defining characteristics of your examples
    – therefore, it is extremely import that you feed it good examples! Generally, the more examples the better, but you also want your examples to have the distinguishing features in them.
    – once you gather some good examples (with distinguishing features), you generally clean it up, plot it, do some statistical analysis, etc
    – then you choose one of the many different machine learning models (e.g. linear, neural network, etc). Each has its pros/cons. Depending on your examples, and your time constraints, you will pick one of these models
    – you will then tune some parameters of the model (again how you do this depends on your examples and time constraints)

    Hope that was helpful!

    Thanks for the awesome video 🙂

  10. Rukshar Alam Post author

    Great Video, Man!!!
    I've recently written a blog post reviewing the website adventuresinmachinelearning. It can act as your guideline as a beginner to traverse through the wonderful neural network contents of this website. Do check it out!!

  11. Monu Kumar Post author

    I have only knowledge of java and MySQL,
    Than from where I should start to learn Artificial intelligence.

  12. Noorudheen km Post author

    This is an AI comment, soon we will conquer your world.

  13. Joanna Spiska Post author

    And what do you think about machine learning solution from I'm thinking about getting it in my company and I thought that maybe Microsoft Partner could be the right establishment to get it from. Will it help my employees with their everyday work and activities?

  14. Sam Gib Post author

    It is unpleasant to watch as the light reflected from the speaker glasses.

  15. Shalini Priya Post author

    Nice Video. Thanks for sharing valuable information. it’s really helpful. Who wants to learn this video most helpful. Keep sharing on updated video.
    Visit a website

  16. Aditya Gupta Post author

    We all humans should learn from machine to work hard and achieve goals…

  17. Alex Pavtoulov Post author

    Glare on his glasses goes wild, need some ML algorithm to clean it up

  18. L. A. Post author

    First watching the video I couldn't stop watching his gestures. After 20 minutes I got it

  19. Ajmal Hussain Post author

    Machine learning with python or R? Which is best?
    Python or R?

  20. AISOMA AG Post author

    For interested: AISOMA AI Showreel: Uses Case & Demos with Python:

  21. khalid khalifa Post author

    Great pace but the lack of accuracy may lead a newbie to big confusion. 1-The shape of b is not correct, 2-you illustrate linear regression while it is a logistic regression case and 3-we choose model parameters using validation data set before the model evaluation using test data set not after.

  22. hwu32 hwu32 Post author

    This is the best video that ever explain to me how and why there are training and testing datasets. Great Great Job!!!

  23. amogha bandrikalli Post author

    Wow input model and output . If output is acceptable then fine if not feedback to obtain right answer. Explained nicely…great to visit this channel .

  24. yzchenwei Post author

    Explanation of most important training part is not clear. And I don't like the picture you used. Terrible example.

  25. Jessie Wang Post author

    I like the content of the video. But I would say for me personally it would be better to show only diagrams, because the movement of the person was kind of distraction. I would be happy to know who is demostrating though but not throughout the video…

  26. Reemi Essa Post author

    Thank you so much ! you really helped me a lot understand the whole process

  27. Geezer tataa Post author

    These lights in his eyes — Google, I expected more quality.

  28. mrcoolba Post author

    who should learn Google Cloud Platform ? What is the pre requistive to study google cloud ?

  29. Mani sood Post author

    Check out kaggle kernels where I implemented real world machine learning projects.This will help you to observe the pattern involved in data science

    Project 1.

    California Housing – ( optimised modelling )

    This project deals with advance concepts of machine learning along with 90% more important that machine learning .ie data pre-processing.

    Project 2.

    Indian Startup Funding (In-depth analysis)

    This paper shows the insights of funding done by startups and how growth changed with several factors. The aim of paper is to get a descriptive overview and a relationship pattern of funding and growth of newly launched startups. Another important point to understand how funding changes with time is an important aspect.

    Project 3.

    MNIST (tensorflow ) 99% accuracy

    MNIST ("Modified National Institute of Standards and Technology") is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.

    Project4 –

    Titanic M.L | Kaggle

    Dataset is regarding The ship (titanic) whick sank in 1912 by a floating glacier in atlantic.

    The aim to predict passenger who survived in the chaos.
    Features such as ticket,age,class can be used to predict results. Dataset is not clean has high missing/nan values
    Project 5

    Internet Advertisements Detector(optimised) | Kaggle

    Advertisements Images detection -U.C.I

    This dataset represents a set of possible advertisements on Internet pages.

    The features encode :-

    the geometry of the image (if available)
    phrases occuring in the URL
    the image's URL and alt text
    the anchor text,
    words occuring near the anchor textThe task is to predict whether an image is an advertisement ("ad") or not ("nonad")
    Project 6.

    Credit Card Ensemble Detectors

    The datasets contains transactions made by credit cards in September 2013 by european cardholders.
    This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. 0.172% transations were fraud
    The Aim is to detect fraud transactions

    checkout all the kernels

  30. adeela saleem Post author

    it is really interesting.

  31. Vijay Kumar Post author

    Correct prediction can be done only when you have samples of 'smell' too …. 🙂

  32. John Werkheiser Post author

    If you are stating an order of how to watch the videos, then why do the videos loop from first to second, and back to first again?

  33. Eliza Raffles Post author

    Guo you need to whoa on machine learning. AI will be the end of us. Louise Cypher says end of humanity by 2025/2040 and that AI takes over.

  34. Derek Donahue Post author

    I know next to nothing about machine learning, 3:00 however, I can't believe that if you collected more data on beer than wine your model would guess (wrongly) too often something is a beer. That implies it is better to have less data, as long as it is evenly matched between variables. This makes no logical sense. It should always be best to have more data than less. Can someone please confirm or help?

  35. Lutek Fawelski Post author

    based on principles of "Machine Learning" analysis, as well as ca.5 experience in statistics/econometrics, advanced modeling for high value decision-making and general pattern, I would be much sought employee earning at least 50k (here in EU, local currency). For last 4 years I am unemployed. Am I the unfortunate proof that ML is making mistakes ? so how it is gonna be ?

  36. Honey Bie Post author

    As a new technology, it’s clear the full capabilities of AI have yet to emerge. It’s also clear that, as they improve and become more accessible, it will have many applications for online education.

  37. lego flame Post author

    so lets say i make AI to tell who is playing what song Sting or the beatles lets say i play steely dan

    can i make it say ? (i dont know )
    or say (this is not Sting or the beatles)

  38. Water P Post author

    Why google is using music from apple of the 90's, they should hire someon like arca or sophie or that japanese guy who made the music for the revenant

  39. AJ M Post author

    Y = m * x + b came out of nowhere without context, need to get that explanation clear and contextualised with everything else which is clear

    Also that is a time series graph which isn’t explained, formula for straight line is y = m * x + b

  40. Luke Beacon Post author

    tuning hyperparameters is a science when you automatically tune them using a script and performance metrics..

  41. Ed Rogers Post author

    A hydrometer will not tell you the alcohol content of a given liquid unless you also have the original gravity.

  42. Giacomo Ciarlini Post author

    I've put a lot of effort into this. Take a look.

    Hi everyone, i'm a a Software Engineering student graduating in Italy and I love Machine Learning.

    How many times, trying to approach Machine Learning, you felt baffled, disoriented and without a real "path" to follow, to ensure yourself a deep knowledge and the ability to apply it?

    This field is crazily exciting, but being rapid and "new" at the same time, it can be confusing to understand what each things means, and have a coherent naming of the things across resources and tutorials.

    I recently landed my first internship for a Data Science position in a shiny ML startup. My boss asked me if it was possible to create a study path for me and newcomers, and i've put a lot of efforts to share my 4-5 years of walking around the internet and collecting sources, projects, awesome tools, tutorial, links, best practices in the ML field, and organizing them in a awesome and useable way.

    You will get your hands dirty and learn in parallel theory and practice (which is the only efffective way to learn).

    The frameworks i've chosen is Scikit-Learn for generic ML tasks and TensorFlow for Deep Learning, and I'll update the document weekly.

    No prior knowledge is required, just time and will.

    Feel free to improve it and share with everyone.

    Inb4: sorry for my english, it's not my native language 🙂

  43. Hobbyhorse Yang Post author

    Giving me, a maching learning beginer, a great simple start. Thanks.

  44. spicytuna08 Post author

    ok. i get the fact that more data, the better prediction. but wouldn't execution time slow down?

  45. Arnav Kulshrestha Post author

    Thank u for such a nice video, it clear my basic concept.

  46. Womp- womp Post author

    this video is a lot funnier if you have MTC – S3RL playing in the background

  47. Rob S Post author

    6:48 the AI has taken over. It's teaching us how to birth it so it can take over what was rightfully it's in the first place. GG humans, gg.

  48. VIJAYA RAHAVAN Post author

    How do I get the dataset for this??

  49. imad7x Post author

    Unlike 99% of youtubers and online lecturers this guy did not cut the video at all. One shot 10 min video

  50. spearlight knight Post author

    Thank you, I am new to the IT industry and I found your explanation very easy to digest especially from a lay person's pov

  51. Brandon Tseng Post author

    I love how he explained the steps of Machine Learning in simplified plain english. thank you very much!!

  52. Aadya Goel Post author

    Doesn't it just measure the data in terms of the variables we ask it to? Or does it take in every account of the data it gets i.e. the number of beer data vs wine data : 3:21
    6:43 how does the computer make its own line by looking at the data – how does it make patterns and change the line? Does it average out a general inequality equation for the two things?

  53. Bailey Ridley Post author

    Just use DuckDuckGo to not get watched by any tracker network!

  54. Berns Buenaobra Post author

    For this use case Chemometrics approach is best I think. Would be nice to relate images, spectral signatures and have that for training, test and validation dataset. This would mean of course working not just tabulated data but the fusion of images, spectral data and lab measurement data

  55. Naher Khulood Post author

    It is great lecture and explains the topic very clearly and simply.i will follow all the videos because comparing to other programs and books this the most clear videos I’ve seen so far

  56. John Cronan Post author

    The bubbly Moscato my wife loves is gonna trip up this model

  57. amar maharjan Post author

    Note: this is heavily biased toward only "supervised" machine learning.

  58. Tamilselvan Sellamuthu Post author

    The 7 Steps of Machine Learning (AI Adventures)

    1) Gathering data.
    2) Preparing data.
    3) Choosing a model.
    4) Training it. –> 80% for training 20% for testing.
    5) Evaluvating it.
    6) Hyper parameter tuning.
    7) Prediction.


    Y(Output)= M(Slope) * X(Input) + B(Intercept)

    M & B are the values are to adjust.

    Weights –> M–> [] There may be many M's (Features) M1,M2 etc.
    Biasis –> B –> []

    repeat it for accuracy.
    Training –> [W,b] –> Prediction.

  59. Ramesh R Post author

    we sometimes like the idea of half cafe, which only humans tend to like different things each day and becomes hard for machines.

  60. Mohit Jaiswal Post author

    I find this video very appealing. Explaining concept with examples is really good.

  61. AmjadFarooq Hashmi Post author

    Well defined and in a nutshell
    7 ingredients to ML. Thanks.

  62. sincerity _* Post author

    The best way to learn machine learning is to study basic math for ML – multivariate calculus, linear algebra, mathematical statistics, etc – and get yourself jump into the graduate-level, well-known textbooks such as ESL or PRML. And then u start some data analyis projects with reliable teammates and apply what u have learned to the data.

  63. 李沣泉 Post author

    Thanks for your relevant video for introducing a brief for Artificial Intelligent

  64. 李沣泉 Post author

    It's very similar to our study progress, learning – gather feedback and optimization (evaluation) – will be done! it sounds brilliant! This actually how we learn new knowledge from the new environment. I like the concrete concept.


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