Machine Learning Tutorial Python -1: What is Machine Learning?

///Machine Learning Tutorial Python -1: What is Machine Learning?

Machine Learning Tutorial Python -1: What is Machine Learning?

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Introduction on machine learning to begin machine learning with python tutorial series. This video describes what is machine learning, deep learning, machine learning application in real life. In next tutorial we will start writing python code to solve a simple problem using machine learning

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#MachineLearning #PythonMachineLearning #MachineLearningTutorial #Python #PythonTutorial #PythonTraining #MachineLearningCource

Topics that are covered in this Machine Learning Video:
1:06 What is machine learning?
3:55 what is Deep Learning?
5:09 Machine Learning implementation in real life

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Machine Learning Tutorial Python – 2: Linear Regression Single Variable:

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By |2020-04-03T02:44:56+00:00April 3rd, 2020|Python Video Tutorials|47 Comments

47 Comments

  1. Mom's Spaghetti April 3, 2020 at 2:45 am - Reply

    Kaw and kar

  2. Anandachetan Elikapati April 3, 2020 at 2:45 am - Reply

    You've simplified the subject to a great extent where others are complicating it. Found minor mistakes in the flow but that don't hamper much. Thanks for your efforts and contribution.

  3. Bilal Chudhury April 3, 2020 at 2:45 am - Reply

    bring in hindi lang

  4. Syrym Zhakypbekov April 3, 2020 at 2:45 am - Reply

    I like it! Super! Keep Going!

  5. Gels April 3, 2020 at 2:45 am - Reply

    Hello sir πŸ™‚

    (I'm doing multi label text classification btw)
    I asked before that if it is better to clean the training data or the dataset before training a machine learning algorithm. So I did it and cleaned it however I'm getting an accuracy of 70% to 79% only and I'm not really satisfied with that soooo
    I'm just wondering how can I boost the accuracy? I think yeah , I need to add more data, I only have 2600 samples of my dataset which is obviously a small amount. Also is removing stopwords, punctuation marks or even characters with less than three of length already enough for cleaning? So that my model will have a good performance. Or should I include removing numbers? or any other things?

    Thank you so much for your answer πŸ™‚ You really help a lot of people , I've been reading other people's comments πŸ™‚ salute to you sir πŸ™‚

  6. Park 1954 April 3, 2020 at 2:45 am - Reply

    I like the way you simplify the topic hence requesting you -can you teach about backward elimination in multilinear regression
    use of OLS.summary()
    I am confused on this topic

  7. Ab Qyum April 3, 2020 at 2:45 am - Reply

    sir make a video on CNN,DNN,RNN,LSTM,deep-RNN etc

  8. Mars 1369 April 3, 2020 at 2:45 am - Reply

    5:27
    MARIJUANA?
    Deep learning with marijuana is Fun πŸ˜ŽπŸ˜‚

  9. Purple Salad April 3, 2020 at 2:45 am - Reply

    You are doing a great job in explaining , please keep up the good work.

  10. codebasics April 3, 2020 at 2:45 am - Reply

    Machine learning tutorials with exercises:

    https://www.youtube.com/watch?v=gmvvaobm7eQ&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw

    Step by step guide on how to learn data science for free: https://www.youtube.com/watch?v=Vn_mmOuQkSA
    Learn data analyst skills: https://www.youtube.com/watch?v=x6tnVOn4st4

    Pandas tutorials: https://www.youtube.com/playlist?list=PLeo1K3hjS3uuASpe-1LjfG5f14Bnozjwy
    Switch career to data science from non-technical background: https://www.youtube.com/watch?v=P8BuKf9crA8
    Complete python tutorials list: https://www.youtube.com/playlist?list=PLeo1K3hjS3usILfyvQlvUBokXkHPSve6S

  11. keshav dk April 3, 2020 at 2:45 am - Reply

    Thank you very much for this video. I am non Computer science student.

    Recently i worked as a Internet of things technology for start up (Bangalore). I was working as Firmware engineer.

    There i got an opportunity to explore Python language for sending data from sensor to cloud using API's .Also got to know about Cloud technology and other computer science terms like client ,server,github . I started learning Python myself from internet resource(free courses).

    I am beginner to machine learning.
    Now i got a requirement on machine learning. Blood deferral data set means when the blood bank go to the blood bank, there will be various reasons they get rejected.

    You have to find out which is the maximum condition that most of the donors are rejected.

    There is 1 and 0 in that. There is a column various reasons column and whenever there is 1 means that donor is deferred of that reason. So I have to find out for which reason most of the donors are deferred.

    How can we achieve this?

  12. Rashedul Alam April 3, 2020 at 2:45 am - Reply

    Sir you are awesome

  13. Fitz Keb April 3, 2020 at 2:45 am - Reply

    I can not see the link for the exercise

  14. Subramanian Chenniappan April 3, 2020 at 2:45 am - Reply

    Great videos. I am a java developer

  15. RΔ—al Star April 3, 2020 at 2:45 am - Reply

    What's the prerequisite knowledge needed to study machine learning??

  16. D Kishore April 3, 2020 at 2:45 am - Reply

    i have a one big challenging doubt in my mind Really please help me
    why do we find best accuracy model , what is the use with that ?
    after finidng the best model what is the next process in organization

    ex: Walmart wants to open new stores in NewYork, walmart has online customers shipping raw data (like customer location , name, phone number, sales ), so how can they find best locations in NewYork to open stores based on raw-data. what will be the Output here ?

    you can please mail me with your explanation : dkishore090@gmail.com

  17. Matt Chase April 3, 2020 at 2:45 am - Reply

    5:20 Nice

  18. Lilita Alem April 3, 2020 at 2:45 am - Reply

    Linear Regression (dummy variables): I appreciate your tutorial, I'm a beginner for Python ML. str conversion is one of the issues I'm facing. I watched your Linear Regression tutorial and I have converted one of my columns by using your dummies conversion method. The converted dummies are 10 and one minus 9. The problem is it couldn't predict for me. It produced the following error message: ValueError: shapes (1,3) and (11,) not aligned: 3 (dim 1) != 11 (dim 0). For details, look at the following converted data and error messages. You can contact me on email associated with this comment. I look forward to hearing from you.

    test_score oral_score salary eleven five fourteen seven six ten

    0 8 9 45000 0 1 0 0 0 0

    1 8 6 45000 0 0 0 0 1 0

    2 6 7 65000 0 1 0 0 0 0

    3 10 10 65000 0 0 0 0 0 0

    4 9 6 70000 0 0 0 1 0 0

    5 8 10 62000 0 0 0 0 0 0

    6 6 7 72000 0 0 0 0 0 1

    7 7 8 82000 1 0 0 0 0 0

    8 9 7 82000 0 0 0 0 0 0

    9 8 8 83000 0 0 0 0 0 0

    10 9 9 85000 0 0 1 0 0 0

    11 8 6 82000 0 0 0 0 0 1

    12 9 5 82000 0 0 0 0 0 1

    three twelve two

    0 0 0 0

    1 0 0 0

    2 0 0 0

    3 0 0 1

    4 0 0 0

    5 1 0 0

    6 0 0 0

    7 0 0 0

    8 0 1 0

    9 0 0 0

    10 0 0 0

    11 0 0 0

    12 0 0 0

    X = final.drop('salary', axis = 'columns')

    y = final.salary

    #because of small data, no need to use train_test_split, but just train on the whole data.

    from sklearn.linear_model import LinearRegression

    model = LinearRegression()

    model.fit(X, y)

    LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)

    model.predict([[8,9,1]])

    —————————————————————————

    ValueError Traceback (most recent call last)

    <ipython-input-15-196b62f15502> in <module>

    —-> 1 model.predict([[8,9,1]])

    C:ProgramDataAnaconda3libsite-packagessklearnlinear_modelbase.py in predict(self, X)

    219 Returns predicted values.

    220 """

    –> 221 return self._decision_function(X)

    222

    223 _preprocess_data = staticmethod(_preprocess_data)

    C:ProgramDataAnaconda3libsite-packagessklearnlinear_modelbase.py in _decision_function(self, X)

    204 X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])

    205 return safe_sparse_dot(X, self.coef_.T,

    –> 206 dense_output=True) + self.intercept_

    207

    208 def predict(self, X):

    C:ProgramDataAnaconda3libsite-packagessklearnutilsextmath.py in safe_sparse_dot(a, b, dense_output)

    140 return ret

    141 else:

    –> 142 return np.dot(a, b)

    143

    144

    ValueError: shapes (1,3) and (11,) not aligned: 3 (dim 1) != 11 (dim 0)

    ​

  19. PramoD sanghavi April 3, 2020 at 2:45 am - Reply

    nice information and very easy explained each and every concept sir thanks a lot…. !!!

  20. Bb rocks April 3, 2020 at 2:45 am - Reply

    sir, any chances we are getting a playlist on statistics required for data science?

  21. Vivek Chaudhary April 3, 2020 at 2:45 am - Reply

    Hey you are doing very good job. God bless you buddy.

  22. Amit Seth April 3, 2020 at 2:45 am - Reply

    sir, I need your help. kindly make video "How do I set up Jupyter/IPython Notebook for Django?"

  23. Simran Chawla April 3, 2020 at 2:45 am - Reply

    Sir, the videos you have made are really beneficial. I requested you to make more videos on data science like NLP, Deep Learning. It could be really helpful to us. Thank you πŸ™‚

  24. Skk Kks April 3, 2020 at 2:45 am - Reply

    So eagerly awaited course,Thank you for making it possible.

  25. sory diallo April 3, 2020 at 2:45 am - Reply

    Hello, do you have a course video on deep learning keras ?

  26. Sajjad Ahmed April 3, 2020 at 2:45 am - Reply
  27. Shiva Prasad Reddy April 3, 2020 at 2:45 am - Reply

    Very Nice tutorial

  28. zero study April 3, 2020 at 2:45 am - Reply

    This is Epic!. i;'ve watched almost all video on this channel and this channel Is Pure gold.
    Low subcribe?. may be cause there's not many programmer in this world hehee IMAO

  29. Avanish Patel April 3, 2020 at 2:45 am - Reply

    What is the best book for ML?

  30. Aman Gambhir April 3, 2020 at 2:45 am - Reply

    Please make a series on Scipy, Scikit-learn and pyspark

  31. Ganesh Sundar April 3, 2020 at 2:45 am - Reply

    Awesome, Good Show!!!

  32. abhijit biswas April 3, 2020 at 2:45 am - Reply

    please make videos on support vector machines and when do we use it. similarly when do we SVM, PCA and Decision Tree & Random Forest?

  33. Brandon N April 3, 2020 at 2:45 am - Reply

    Love this series, very helpful. You should consider making this into a course you can sell on Udemy. I'd definitely buy it.

  34. Vishwa Kovilampati April 3, 2020 at 2:45 am - Reply

    Got clarity on "what is machine learning" first, with clear explanations thank you

  35. Matthew Parker April 3, 2020 at 2:45 am - Reply

    Wow I just discovered your videos and I am so excited to dive into Python for Data Science. Been working with SAS but need to learn Open Source. Thank you for all you do.

  36. shikha kumar April 3, 2020 at 2:45 am - Reply

    hi . Can you please share some videos on Exploratory Data analysis (using python). It'll be very helpful. Thanks in advance.

  37. Utkarsh Srivastava April 3, 2020 at 2:45 am - Reply

    Please make a series on Beautiful Soup and Web crawling.And btw you have got an exceptionally great way of teaching.

  38. Amit Saroj April 3, 2020 at 2:45 am - Reply

    please make a series on data wrangling

  39. Marimuthu S April 3, 2020 at 2:45 am - Reply

    So YouTube got I am learning machine learning with machines learning

  40. Harsh Chowdhary April 3, 2020 at 2:45 am - Reply

    reat video and teaching method. You have an art of keeping things simple but still teach advanced concepts. I get a very good and quick overview and understanding from your videos. Thanks a lot.

  41. Rajyalakshmi Kallagunta April 3, 2020 at 2:45 am - Reply

    can u plz give diffrence between machine data and human genarated data

  42. Increadible Angel April 3, 2020 at 2:45 am - Reply

    nice

  43. Prince Kumar Singh April 3, 2020 at 2:45 am - Reply

    Aaaannnddd Subscribed !

  44. Muhammad K April 3, 2020 at 2:45 am - Reply

    Very good video, I have been looking to learn machine learning for a while now but I couldn't find good resources until now! Thanks, keep it up.

  45. Vinod Kinoni April 3, 2020 at 2:45 am - Reply

    welcome

  46. Shritam Kumar Mund April 3, 2020 at 2:45 am - Reply

    Great timing!
    Just completed Python intermediate and advance, and here is my favorite Python teacher with a new series of Machine learning.
    So much excited for this series.
    Happy Pythoning:)

  47. My4DGlasses April 3, 2020 at 2:45 am - Reply

    Oh what a lovely series!
    Thank you so much πŸ™‚
    How regularly will you upload videos for this series?

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