Machine Learning With Python | Machine Learning Tutorial | Python Machine Learning | Simplilearn

///Machine Learning With Python | Machine Learning Tutorial | Python Machine Learning | Simplilearn

Machine Learning With Python | Machine Learning Tutorial | Python Machine Learning | Simplilearn

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This Machine Learning with Python tutorial gives an introduction to Machine Learning and how to implement Machine Learning algorithms in Python. By the end of this video, you will be able to understand Machine Learning workflow, steps to download Anaconda, types of Machine Learning and hands-on in Python for Linear Regression and K-Means clustering algorithms. Below are the topics covered in this Machine Learning tutorial:

1. Why Machine Learning? ( 01:09 )
2. Applications of Machine Learning ( 01:50 )
3. How does Machine Learning work? ( 03:33 )
4. Machine Learning Workflow ( 04:53 )
5. Steps to download Anaconda ( 06:13 )
6. Types of Machine Learning ( 09:53 )
7. Linear Regression Demo ( 13:51 )
8. K-Means Clustering Demo ( 26:02 )
9. Use Case – Weather Analysis ( 39:27 )

What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

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Why learn Machine Learning?

Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.

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3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems

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We recommend this Machine Learning training course for the following professionals in particular:

1. Developers aspiring to be a data scientist or Machine Learning engineer
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By |2019-06-29T20:16:45+00:00June 29th, 2019|Python Video Tutorials|38 Comments

38 Comments

  1. Aqira June 29, 2019 at 8:16 pm - Reply

    is data mining any related to machine learning? I got assigned to do data mining my college final test. And having a lil bit of a trouble to get the references of data mining (since it always redirects me to data science / data analysis)

  2. Kevin VH June 29, 2019 at 8:16 pm - Reply

    To much babble with not enough in depth explanation. Video is more like a do this instead of this is how you do this.

  3. Night Owl June 29, 2019 at 8:16 pm - Reply

    Weather Datasheet: https://github.com/jupyter/docker-demo-images/blob/master/datasets/cluster/xclara.csv

    you can copy and paste the data into an excel file and save as a csv

  4. Zohaib Siddiqui June 29, 2019 at 8:16 pm - Reply

    This world is full of beautiful people like you. Thank you for the awesome tutorial

  5. phuc hung Vu June 29, 2019 at 8:16 pm - Reply

    hello, very nice lecture for ML beginner. Can you send me the weather dataset at hwngvux98@gmail.com. Thanks very much, keep up the good work

  6. Rishabh June 29, 2019 at 8:16 pm - Reply

    13:37 GRAPH IS IN RUPEES.

  7. pradyumn singh June 29, 2019 at 8:16 pm - Reply

    Can anyone send me the data at pradyumnsingh15@gmail.com of this lecture Machine Learning with Python.

  8. Сарвар Ганиевич June 29, 2019 at 8:16 pm - Reply

    Thanks for video! Can you send me weather dataset please?

  9. Tóth Ádám June 29, 2019 at 8:16 pm - Reply

    Hello ! Very good and informative video. Could you please send me the databases? gosupalanta@gmail.com ! Thank you ! I'll sub!!!

  10. Timalo Kuhamba June 29, 2019 at 8:16 pm - Reply

    I'm starting to learn machine learning/scikit/tensorflow. Thanks for your tutorial. Please send me the dataset and code. Thanks! (kuhamba@yahoo.com)

  11. Yahya Murad June 29, 2019 at 8:16 pm - Reply

    Awesome Video !

    I have one question though, do I
    have to use Anaconda or can I use
    PyCharm.

  12. elyakimlev June 29, 2019 at 8:16 pm - Reply

    Coming from Andrew Ng's course on ML in Octave, this Python library seems sooo high level. Saves a lot of time doing a lot of math. But I'm glad I know what it's doing behind the scenes.

  13. Nur Aisyah June 29, 2019 at 8:16 pm - Reply

    Hi. did anyone know how's to create a transliteration machine learning that can solved homograph disambiguation using python?

  14. Serge Daney June 29, 2019 at 8:16 pm - Reply

    that was great. Thanks a lot for sharing!

  15. ThePornoslav June 29, 2019 at 8:16 pm - Reply

    I just subscribed. Thank you so so so much. The very best ML with python tutorial.

  16. Mr Arca9 June 29, 2019 at 8:16 pm - Reply

    Because for some reason programmers never put code in the description to make copy/paste easier.

    LinearRegression (No Labels):
    START
    import matplotlib.pyplot as plt

    import numpy as np

    from sklearn import datasets, linear_model

    def graph(x_range, formula):

    x = np.array(x_range)

    y = eval(formula)

    plt.plot(x,y)

    house_price = [245,312,279,308,199,219,405,324,319,255]

    size = [1400,1600,1700,1875,1100,1550,2350,2450,1425,1700]

    #Make Y Axis

    size2 = np.array(size).reshape((-1,1))

    regr = linear_model.LinearRegression()

    regr.fit(size2, house_price)

    print("Co:n", regr.coef_)

    print("inter:n",regr.intercept_)

    graph(range(1000,2700),'regr.coef_*x + regr.intercept_')

    plt.scatter(size,house_price, color='black')

    plt.show
    END

    Cluster K-Means:
    START
    import matplotlib.pyplot as plt

    import numpy as np

    import matplotlib.pyplot as plt

    from matplotlib import style

    style.use("ggplot")

    from sklearn.cluster import KMeans

    x = [1,5,1.5,8,1, 9]

    y = [2,8,1.8,8,0.6,11]

    plt.scatter(x,y)

    plt.show()

    #Make 2d array from data

    X = np.column_stack((x,y))

    kmeans = KMeans(n_clusters=2)

    kmeans.fit(X)

    centroids = kmeans.cluster_centers_

    labels = kmeans.labels_

    colors = ["g.","r.","c.","y."]

    for i in range (len(X)):

    plt.plot(X[i][0], X[i][1], colors[labels[i]], markersize = 10)

    plt.scatter(centroids[:,0], centroids[:,1], marker = "x", s=150, linewidths = 5, zorder =10)

    plt.show()
    END

    Will edit as i complete more

  17. Komal Singh June 29, 2019 at 8:16 pm - Reply

    can you send me data set for practice
    email id:- komal.singh.thestar@gmail.com

  18. big gucker June 29, 2019 at 8:16 pm - Reply

    I would like the dataset in the final demo! Can you email it to me at trollol1337@yahoo.com? thanks!

  19. ayush salik June 29, 2019 at 8:16 pm - Reply

    Hey, can you please forward the dataset to asalik13@gmail.com? This is great stuff, thanks a lot.

  20. Emanuele Collaro June 29, 2019 at 8:16 pm - Reply

    Awesome video, can you send me the datasets? e.collaro@yahoo.it

  21. mokshitha sai June 29, 2019 at 8:16 pm - Reply

    Thanks for the video….helped a lot!!! can u mail the dataset to mokshithasai08@gmail.com

  22. Gagan Arora June 29, 2019 at 8:16 pm - Reply

    Nice one. Please send me dataset at gaganarora0101@gmail.com

  23. Shubh Choubey June 29, 2019 at 8:16 pm - Reply

    Can you guys provide me datasets used in this video??

  24. Rock Rock June 29, 2019 at 8:16 pm - Reply

    Hello sir Great video. Very informative and helpful Would you mind sending me the weather analysis dataset? prixitkaushik07@gmail.com Thanks in advance 🙂

  25. LOSHIJO123 June 29, 2019 at 8:16 pm - Reply

    jupyter notebook didn't open up. using anaconda command prompt need help ?

  26. FilaBlaster June 29, 2019 at 8:16 pm - Reply

    can you pls sent me dataset to fila.zg@hotmail.com

  27. Xiaohong Yan June 29, 2019 at 8:16 pm - Reply

    Great video. Could you share the dataset to xiaohongyan2015@gmail.com? Thx

  28. Mango Phones June 29, 2019 at 8:16 pm - Reply

    Honestly you can do all this in excel, with forecast.ets formula.. and you can add further complex formulas so it looks pro and to impress your boss 😁

  29. usama rafea June 29, 2019 at 8:16 pm - Reply

    Excellent explanation. would you please send me the data set to usamarafea@yahoo.com

  30. Israel Barth Rubio June 29, 2019 at 8:16 pm - Reply

    Could you send me the used dataset? barthisrael@gmail.com. Thanks in advance!

  31. Radu Nițescu June 29, 2019 at 8:16 pm - Reply

    Very good video. Keep up doing such great work. Could you send me the dataset to try myself? email is: radu.nitescu35@gmail.com

  32. Knights June 29, 2019 at 8:16 pm - Reply

    Hey Richard, thank you for your videos, they are awesome!
    What would you recommend for "predictive maintenance"? Using Python/Java or MatLab?
    Since the job of predicting something requires analyzing a lot of data, i would say MatLab.
    But what do you think?

  33. Duo Al Familie June 29, 2019 at 8:16 pm - Reply

    I need the dataset for your sample program. Please send to my email id: prayogi_mesin@yahoo.com, Thanks for your video, great.

  34. sinelk berhane June 29, 2019 at 8:16 pm - Reply

    Thanks for the video it was easy to follow. It would help a lot if the datasets were public please make it available…. Thank you

  35. Shanshan Bau June 29, 2019 at 8:16 pm - Reply

    Thank you for the great presentation. Could you please share the datasets for this presentation to shanshan_bau@yahoo.com. Thanks!
    Looking forward to more presentations in ML.

  36. Omnya Abdalgadir June 29, 2019 at 8:16 pm - Reply

    Please can you share dataset my email id.. mon.alabaid@gmail.com

  37. Edgar Hernandez June 29, 2019 at 8:16 pm - Reply

    4:17 colors are wrong…….. sorry had to say something

  38. CorporateGamer June 29, 2019 at 8:16 pm - Reply

    Your presentation is top notch, thank you for your contributions!

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