The 7 Steps of Machine Learning (AI Adventures)

detecting skin cancer to sorting cucumbers
to detecting escalators in need of repair,
machine learning has granted computer systems
entirely new abilities. But how does it really
work under the hood? Let’s walk through
a basic example and use it as an excuse to talk
about the process of getting answers from your data
using machine learning. Welcome to Cloud AI Adventures. My name is Yufeng Guo. On this show, we’ll
explore the art, science, and tools of machine learning. Let’s pretend that
we’ve been asked to create a system that answers
the question of whether a drink is wine or beer. This question answering
system that we build is called a model,
and this model is created via a
process called training. In machine learning,
the goal of training is to create an accurate model
that answers our questions correctly most of the time. But in order to
train the model, we need to collect
data to train on. This is where we will begin. Our data will be collected
from glasses of wine and beer. There are many aspects of drinks
that we could collect data on– everything from the amount of
foam to the shape of the glass. But for our purposes, we’ll
just pick two simple ones– the color as a wavelength of
light and the alcohol content as a percentage. The hope is that we can
split our two types of drinks along these two factors alone. We’ll call these our
features from now on– color and alcohol. The first step to
our process will be to run out to the
local grocery store, buy up a bunch of
different drinks, and get some equipment to do our
measurements– a spectrometer for measuring the
color and a hydrometer to measure the alcohol content. It appears that our grocery
store has an electronics hardware section as well. Once our equipment and then
booze– we got it all set up– it’s time for our first
real step of machine learning– gathering that data. This step is very important
because the quality and quantity of
data that you gather will directly determine how good
your predictive model can be. In this case, the
data we collect will be the color and alcohol
content of each drink. This will yield us a table
of color, alcohol content, and whether it’s beer or wine. This will be our training data. So a few hours of
measurements later, we’ve gathered our training data
and had a few drinks, perhaps. And now it’s time for our next
step of machine learning– data preparation– where we load our data
into a suitable place and prepare it for use in our
machine learning training. We’ll first put all our
data together then randomize the ordering. We wouldn’t want the
order of our data to affect how we
learn since that’s not part of determining whether
a drink is beer or wine. In other words, we want to
make a determination of what a drink is independent of what
drink came before or after it in the sequence. This is also a good time to do
any pertinent visualizations of your data, helping
you see if there is any relevant relationships
between different variables as well as show you if there
are any data imbalances. For instance, if we collected
way more data points about beer than wine, the model we
train will be heavily biased toward guessing that virtually
everything that it sees is beer since it would be
right most of the time. However, in the real
world, the model may see beer and wine
in equal amount, which would mean that it would
be guessing beer wrong half the time. We also need to split
the data into two parts. The first part used
in training our model will be the majority
of our dataset. The second part will be used
for evaluating our train model’s performance. We don’t want to use the same
data that the model was trained on for evaluation since
then it would just be able to memorize
the questions, just as you wouldn’t want to
use the questions from your math homework on the math exam. Sometimes the data we
collected needs other forms of adjusting and
manipulation– things like duplication, normalization,
error correction, and others. These would all happen at
the data preparation step. In our case, we don’t have any
further data preparation needs, so let’s move on forward. The next step in our
workflow is choosing a model. There are many models that
researchers and data scientists have created over the years. Some are very well suited
for image data, others for sequences, such as text or
music, some for numerical data, and others for text-based data. In our case, we have just two
features– color and alcohol percentage. We can use a small
linear model, which is a fairly simple one
that will get the job done. Now we move on to what
is often considered the bulk of machine learning– the training. In this step, we’ll use our
data to incrementally improve our model’s ability to
predict whether a given drink is wine or beer. In some ways, this
is similar to someone first learning to drive. At first, they don’t know
how any of the pedals, knobs, and switches work or when they
should be pressed or used. However, after lots of
practice and correcting for their mistakes, a
licensed driver emerges. Moreover, after a
year of driving, they’ve become quite
adept at driving. The act of driving and
reacting to real-world data has adapted their driving
abilities, honing their skills. We will do this on a much
smaller scale with our drinks. In particular, the formula
for a straight line is y equals mx plus b,
where x is the input, m is the slope of the
line, b is the y-intercept, and y is the value of the
line at that position x. The values we have available
to us to adjust or train are just m and b, where the
m is that slope and b is the y-intercept. There is no other way to
affect the position of the line since the only other variables
are x, our input, and y, our output. In machine learning,
there are many m’s since there may
be many features. The collection of
these values is usually formed into a matrix
that is denoted w for the weights matrix. Similarly, for b, we
arranged them together, and that’s called the biases. The training process involves
initializing some random values for w and b and
attempting to predict the outputs with those values. As you might imagine, it
does pretty poorly at first, but we can compare our model’s
predictions with the output that it should have produced
and adjust the values in w and b such that we will have
more accurate predictions on the next time around. So this process then repeats. Each iteration or cycle of
updating the weights and biases is called one training step. So let’s look at what
that means more concretely for our dataset. When we first
start the training, it’s like we drew a random
line through the data. Then as each step of
the training progresses, the line moves
step by step closer to the ideal separation
of the wine and beer. Once training is
complete, it’s time to see if the model is any good. Using evaluation, this is
where that dataset that we set aside earlier comes into play. Evaluation allows
us to test our model against data that has never
been used for training. This metric allows us to
see how the model might perform against data
that it has not yet seen. This is meant to be
representative of how the model might perform
in the real world. A good rule of thumb I use for
a training-evaluation split is somewhere on the order
of 80%-20% or 70%-30%. Much of this depends on the size
of the original source dataset. If you have a lot
of data, perhaps you don’t need as big of a fraction
for the evaluation dataset. Once you’ve done
evaluation, it’s possible that you want to see
if you can further improve your training in any way. We can do this by tuning
some of our parameters. There were a few
that we implicitly assumed when we
did our training, and now is a good time
to go back and test those assumptions,
try other values. One example of a
parameter we can tune is how many times we run
through the training set during training. We can actually show
the data multiple times. So by doing that,
we will potentially lead to higher accuracies. Another parameter
is learning rate. This defines how far
we shift the line during each step based
on the information from the previous training step. These values all play a role
in how accurate our model can become and how long
the training takes. For more complex models,
initial conditions can play a significant
role as well in determining the outcome of training. Differences can
be seen depending on whether a model
starts off training with values initialized at
zeros versus some distribution of the values and what
that distribution is. As you can see, there
are many considerations at this phase of training,
and it’s important that you define what makes
a model good enough for you. Otherwise, we might find
ourselves tweaking parameters for a very long time. Now, these parameters
are typically referred to as hyperparameters. The adjustment or tuning
of these hyperparameters still remains a bit more
of an art than a science, and it’s an experimental
process that heavily depends on the specifics
of your dataset, model, and training process. Once you’re happy with your
training and hyperparameters, guided by the
evaluation step, it’s finally time to use your
model to do something useful. Machine learning is using
data to answer questions, so prediction or inference
is that step where we finally get to answer some questions. This is the point of all of this
work where the value of machine learning is realized. We can finally use our model
to predict whether a given drink is wine or beer, given its
color and alcohol percentage. The power of machine
learning is that we were able to determine how
to differentiate between wine and beer using our model rather
than using human judgment and manual rules. You can extrapolate the
ideas presented today to other problem
domains as well, where the same principles apply– gathering data, preparing
that data, choosing a model, training it and evaluating
it, doing your hyperparameter training, and
finally, prediction. If you’re looking
for more ways to play with training and
parameters, check out the TensorFlow Playground. It’s a completely browser-based
machine learning sandbox, where you can try
different parameters and run training
against mock datasets. And don’t worry, you
can’t break the site. Of course, we will encounter
more steps and nuances in future episodes,
but this serves as a good foundational
framework to help us think through the problem,
giving us a common language to think about each step
and go deeper in the future. Next time on AI
Adventures, we’ll build our first real machine
learning model, using code– no more drawing lines
and going over algebra. [MUSIC PLAYING]


  1. #trendyideas

    July 30, 2018 at 4:51 pm

    guys any recommendations from where should I start learning ML

  2. Susmita Sanyal

    August 3, 2018 at 2:43 pm

    @10:03 Are you challenging us Yufeng?

  3. BEPEC - Career Transition Simplified

    August 8, 2018 at 6:10 am

    Great presentation, loved it.

  4. BEPEC - Career Transition Simplified

    August 9, 2018 at 9:44 am

    Thank you for the video, can i expect video on data science?

  5. Nebu

    August 11, 2018 at 12:12 am

    @03:01 Why would it be biased to detect beer more often than wine? Would that not mean that it is just better at finding out if something is beer than it is at finding out wether something is wine?

  6. tangobayus

    August 16, 2018 at 8:36 pm

    Choosing a Machine Learning Project:

  7. Josh

    August 19, 2018 at 10:34 pm

    This was a damn good video. Correct me if i'm wrong, but it seems like essentially what your doing is teaching the computer how to use the scientific method. I'm sure that's a very over simplified explanation, but as I was watching this it started to seem very familiar.

  8. Hannah Humphreys

    August 21, 2018 at 3:24 am

    Mathematics for Machine Learning

    > Linear Algebra
    > Multivariate Calculus

    #Maths #Basics #MachineLearning #DataScience

  9. Hannah Humphreys

    August 26, 2018 at 7:28 pm

    Exploring ​and ​Preparing ​your ​Data with BigQuery

    > Introduction ​to ​Data ​on Google ​Cloud ​Platform
    > Big ​Data ​Tools ​Overview
    > Exploring ​your ​Data ​with SQL
    > Google ​BigQuery ​Pricing
    > Cleaning ​and ​Transforming your ​Data

    #GoogleCloud #BigQuery

  10. Hannah Humphreys

    August 30, 2018 at 6:10 pm

    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

  11. SuBsCribe f0r nO resan plese

    September 2, 2018 at 11:11 am

    Computers are amazing and INTERESTING!

  12. dark ashes

    September 3, 2018 at 3:30 am

    google should get there AI to recreate its own algorithm to out perform its existing algorithm to learning and strength it mean while when it's recreating its own AI algorithm's people could teach it what we rely want and need this would give us AI of the 2080s

  13. Simon Kalu

    September 3, 2018 at 10:48 am

    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

  14. Yuzhen Wang

    September 4, 2018 at 1:08 am

    郭san 是怎么去到google的哦 好厉害

  15. simarjit kaur

    September 14, 2018 at 4:21 am

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

  16. BungyStudios

    September 17, 2018 at 3:46 am

    I'm so triggered. A linear equation is:
    y=mx+c not +b

  17. Brady Lange

    September 19, 2018 at 9:10 pm

    That was interesting

  18. Ádám Jakab

    September 20, 2018 at 10:18 am

    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?

  19. regortaz

    September 24, 2018 at 7:04 pm

    Sorting cucumbers. The most important function of machine learning.

  20. Daggie Blanqx

    September 24, 2018 at 7:54 pm

    Wow ! Thank you

  21. Sergo Pedro Khoch

    September 26, 2018 at 5:40 pm

    what is all that hand movement ?

  22. Pavankumar reddy S

    September 29, 2018 at 8:15 am


  23. Joanna Spiska

    October 1, 2018 at 11:28 am

    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 🙂

  24. AgarWorstPlayer

    October 8, 2018 at 2:08 pm

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

  25. Abdullah Aghazadah

    October 10, 2018 at 7:04 pm

    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 🙂

  26. Rukshar Alam

    October 13, 2018 at 1:52 pm

    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!!

  27. Monu Kumar Modi

    October 17, 2018 at 8:16 pm

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

  28. Noorudheen km

    October 21, 2018 at 9:23 pm

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

  29. Joanna Spiska

    October 23, 2018 at 8:43 am

    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?

  30. Sam Gib

    October 24, 2018 at 9:27 pm

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

  31. Shalini Priya

    October 26, 2018 at 12:37 pm

    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

  32. Aditya Gupta

    October 28, 2018 at 11:46 am

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

  33. Alex Pavtoulov

    October 30, 2018 at 6:45 pm

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

  34. L. A.

    November 5, 2018 at 5:21 am

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

  35. Ajmal Hussain

    November 5, 2018 at 5:12 pm

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


    November 6, 2018 at 5:04 pm

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

  37. E. Camilo

    November 10, 2018 at 2:26 pm

    The data is collected form our phones, in this case.

  38. khalid khalifa

    November 17, 2018 at 8:25 pm

    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.

  39. hwu32 hwu32

    November 21, 2018 at 12:30 am

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

  40. Sushil Rangrej

    November 22, 2018 at 8:26 pm

    This is correct way of beginniners of programming

  41. TheHeartHome

    November 26, 2018 at 6:29 pm

    That was very clear thank you

  42. amogha bandrikalli

    December 4, 2018 at 3:22 pm

    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 .

  43. Mason Deterding

    December 4, 2018 at 6:14 pm

    why alcohol for the example?

  44. Hummingbird Journey

    December 6, 2018 at 3:54 am


  45. Abins Musthafa

    December 7, 2018 at 12:00 pm

    What is different between AI AND ML .

  46. yzchenwei

    December 12, 2018 at 5:38 am

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

  47. Jessie Wang

    December 14, 2018 at 11:08 am

    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…

  48. Reemi Essa

    December 26, 2018 at 7:06 am

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

  49. Geezer tataa

    December 26, 2018 at 11:05 am

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

  50. satyam sinha

    December 27, 2018 at 8:21 am

    Nice video god bless you

  51. Kamar Taylor

    December 30, 2018 at 3:07 am

    ugh..soooooo booorrriiing….

  52. mrcoolba

    December 30, 2018 at 11:14 pm

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

  53. arnav sood

    December 31, 2018 at 9:21 am

    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

  54. adeela saleem

    January 3, 2019 at 6:17 pm

    it is really interesting.

  55. Kamalshanth selvarajah

    January 4, 2019 at 12:54 pm

    7:05 Oh my Ghost…!!😱😱😱

  56. Vijay Kumar

    January 10, 2019 at 11:14 am

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

  57. learn robot

    January 19, 2019 at 12:55 am


  58. John Werkheiser

    January 22, 2019 at 11:24 pm

    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?

  59. buddha bless

    January 26, 2019 at 2:57 am



  60. Eliza Raffles

    January 29, 2019 at 12:58 pm

    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.

  61. Rudhisundar Beura

    February 5, 2019 at 7:14 am

    Thank you, Yufeng Guo!

  62. Jesse Collin

    February 15, 2019 at 3:03 am

    Can you please teach an AI bot to tell where the summoner spawns in diablo 2? (No one has ever found out and apparently its 100% random but I have always felt like there HAS to be some logic to it!

  63. the gaming dude

    February 18, 2019 at 12:49 pm

    This guy seems cool

  64. Derek Donahue

    February 19, 2019 at 2:03 am

    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?

  65. Lutek Fawelski

    February 28, 2019 at 9:25 am

    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 ?

  66. Honey Bie

    February 28, 2019 at 7:00 pm

    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.

  67. lego flame

    March 1, 2019 at 7:08 am

    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)

  68. Water P

    March 1, 2019 at 8:06 pm

    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

  69. AJ M

    March 2, 2019 at 3:16 pm

    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

  70. Nikhil Bahadure

    March 6, 2019 at 12:35 pm

    wow!!!great explaination….

  71. Luke Beacon

    March 9, 2019 at 5:04 am

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

  72. Uzair Ali

    March 13, 2019 at 7:45 pm

    y=mx+b ???? i always thought it was +c.

  73. Ed Rogers

    March 14, 2019 at 1:07 am

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

  74. Giacomo Ciarlini

    March 14, 2019 at 4:41 pm

    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 🙂

  75. Kwabena Kumah

    March 18, 2019 at 6:07 pm

    Interesting ideas

  76. Henry Windsor Rurikovich

    March 22, 2019 at 2:28 am

    That's awesome 😀

  77. Tatyana Kiryutina

    April 5, 2019 at 3:25 am


  78. Hobbyhorse Yang

    May 4, 2019 at 7:17 am

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

  79. spicytuna08

    May 8, 2019 at 6:51 am

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

  80. Arnav Kulshrestha

    May 12, 2019 at 6:02 pm

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

  81. Womp- womp

    May 12, 2019 at 10:00 pm

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

  82. Jephte C

    May 13, 2019 at 2:19 am

    Hot Dog. Not Hot Dog

  83. R S

    May 26, 2019 at 7:10 am

    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.


    May 26, 2019 at 4:54 pm

    How do I get the dataset for this??

  85. Ed Heinbockel

    May 28, 2019 at 1:48 pm

    Great info, thank you for sharing!

  86. Hosam Fikry

    June 9, 2019 at 12:34 pm

    You made it sound easy

  87. Сүхболд A.

    June 13, 2019 at 5:03 pm

    Reminds me of excel solver

  88. imad7x

    June 17, 2019 at 9:04 am

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

  89. spearlight knight

    June 27, 2019 at 1:58 am

    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

  90. Brandon Tseng

    July 10, 2019 at 3:08 am

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

  91. Aadya Goel

    July 17, 2019 at 10:18 am

    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?

  92. Bailey Ridley

    July 20, 2019 at 5:43 pm

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

  93. Berns Buenaobra

    August 11, 2019 at 7:15 pm

    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

  94. Naher Khulood

    August 16, 2019 at 5:25 pm

    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

  95. Snapigram-Social Network

    September 15, 2019 at 8:41 pm

    nice education

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