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Machine Learning Master Class – 30 Days Challenge

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FREE Machine Learning Master Class (PYTHON) 30 Day Hackathon with Full Hands-on Build What’s Next!!!

The Objective of this machine learning master class is to facilitate the participants to get cognizance of the concepts dealt with for substantial utilization of the same into studying, teaching, Research work, and Upgrading. 📚

What you will Learn?

✅Day-1: Overview A.I | Machine Learning – DOWNLOAD YOUR DAY 1 LECTURE NOTES

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✅Day-2: Introduction to Python | How to write code in Google Colab, Jupyter Notebook, Pycharm & IDLE

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SUPERVISED LEARNING – CLASSIFICATION & REGRESSION
✅Day-3: Advertisement Sale prediction from an existing customer using

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LOGISTIC REGRESSION
✅Day-4: Salary Estimation using K-NEAREST NEIGHBOR

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✅Day-5: Character Recognition using SUPPORT VECTOR MACHINE

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✅Day-6: Titanic Survival Prediction using NAIVE BAYES

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✅Day-7: Leaf Detection using DECISION TREE

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✅Day-8: Handwritten digit recognition using RANDOM FOREST

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✅Day-9: Evaluating Classification model Performance using CONFUSION

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MATRIX, CAP CURVE ANALYSIS & ACCURACY PARADOX
✅Day-10: Classification Model Selection for Breast Cancer classification

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✅Day-11: House Price Prediction using LINEAR REGRESSION Single Variable

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✅Day-12: Exam Mark Prediction using LINEAR REGRESSION Multiple Variable

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✅Day-13: Predicting the Previous salary of the New Employee using
POLYNOMIAL REGRESSION
✅Day-14: Stock price prediction using SUPPORT VECTOR REGRESSION
✅Day-15: Height Prediction from the Age using DECISION TREE REGRESSION
✅Day-16: Car price prediction using RANDOM FOREST
✅Day-17: Evaluating Regression model performance using R-SQUARED
INTUITION & ADJUSTED R-SQUARED INTUITION
✅Day-18: Regression Model Selection for Engine Energy prediction.

UNSUPERVISED LEARNING – CLUSTERING
✅Day-19: Identifying the Pattern of the Customer spent using K-MEANS
CLUSTERING
✅Day-20: Customer Spending analysis using HIERARCHICAL CLUSTERING
✅Day-21: Leaf types data visualization using PRINCIPLE COMPONENT
ANALYSIS
✅Day-22: Finding Similar Movie based on ranking using SINGULAR VALUE
DECOMPOSITION

UNSUPERVISED LEARNING – ASSOCIATION
✅Day-23: Market Basket Analysis using APIRIORI
✅Day-24: Market Basket Optimization/Analysis using ECLAT

REINFORCEMENT LEARNING
✅Day-25: Web Ads. Click through Rate optimization using UPPER BOUND
CONFIDENCE

Natural Language Processing
✅Day-26: Sentimental Analysis using Natural Language Processing

Day-27: Breast cancer Tumor prediction using XGBOOST

DEEP LEARNING
✅Day-28: Pima-Indians Diabetes Classification
✅Day-29: Covid-19 Detection using CNN

✅Day-30: A.I Snake Game using REINFORCEMENT LEARNING

Learn Faster & Easier than You Think 🚀 🔥


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Post Your Positive Comments Below

This Post Has 33 Comments

  1. shan

    hi

  2. Cherub Weeratunge

    Day 15 Hackathon

    print(model.predict(([[99]]))[0])

    Result: 11.0

  3. Suueesh Kumar J

    DAY 12 HACKATHON
    a=[[120,10000,0,7,6,733,0]]
    Predictedmodel = model.predict(a)
    print(Predictedmodel)

    OUTPUT : [156869.35117057]

  4. UTKARSH LOLUSARE

    datasample=[(160,3951,1,6,5,621,0)]
    modelresult=model.predict(datasample)
    print(modelresult)

    Output : [164500.65369712]

  5. KEERTHIKA KUMAR

    a=[[ 14, 8000,0,6,8,1404,0,0,1,0,0,0]]
    PredictedmodelResult = model.predict(a)
    print(PredictedmodelResult)

    OUTPUT : [252799.81347602]

  6. praveen t

    input :
    b=[[60,8450,0,7,5,856,0]]
    PredictedmodelResult = model.predict(b)
    print(PredictedmodelResult)

    output
    [204111.05057904]

  7. Nikunj Doshi

    Day 12 Practice
    datasample=[(30,6000,1,7,8,1150,5)]
    modelresult=model.predict(datasample)
    print(modelresult)

    Output : [221291.70673548]

  8. Cherub Weeratunge

    import pandas as pd #useful for loading the dataset
    import numpy as np #to perform array
    from matplotlib import pyplot
    from sklearn.linear_model import LinearRegression

    from google.colab import files
    uploaded = files.upload()

    dataset = pd.read_csv(‘practiceDataset.csv’)

    print(dataset.shape)
    print(dataset.head(5))

    dataset[‘Street’] = dataset[‘Street’].map({‘Pave’: 0, ‘Grvl’: 1}).astype(int)
    print(dataset.head)

    dataset[‘Heating1’] = dataset[‘Heating’].map({‘GasA’: 1, ‘GasW’: 0, ‘Wall’: 0, ‘Grav’: 0, ‘OthW’: 0, ‘Floor’: 0}).astype(int)
    dataset[‘Heating2’] = dataset[‘Heating’].map({‘GasA’: 0, ‘GasW’: 1, ‘Wall’: 0, ‘Grav’: 0, ‘OthW’: 0, ‘Floor’: 0}).astype(int)
    dataset[‘Heating3’] = dataset[‘Heating’].map({‘GasA’: 0, ‘GasW’: 0, ‘Wall’: 1, ‘Grav’: 0, ‘OthW’: 0, ‘Floor’: 0}).astype(int)
    dataset[‘Heating4’] = dataset[‘Heating’].map({‘GasA’: 0, ‘GasW’: 0, ‘Wall’: 0, ‘Grav’: 1, ‘OthW’: 0, ‘Floor’: 0}).astype(int)
    dataset[‘Heating5’] = dataset[‘Heating’].map({‘GasA’: 0, ‘GasW’: 0, ‘Wall’: 0, ‘Grav’: 0, ‘OthW’: 1, ‘Floor’: 0}).astype(int)
    dataset[‘Heating6’] = dataset[‘Heating’].map({‘GasA’: 0, ‘GasW’: 0, ‘Wall’: 0, ‘Grav’: 0, ‘OthW’: 0, ‘Floor’: 1}).astype(int)
    print(dataset.head)

    X = dataset.iloc[:,[1,4,5,17,18,38,-6,-5,-4,-3,-2,-1]].values
    print(X.shape)
    X[1]

    Y = dataset.iloc[:, -7].values
    Y

    model = LinearRegression()
    model.fit(X,Y)

    a=[[ 20, 9600,0,6,8,1262,1,0,0,0,0,0]] #Data Format: MSSubClass, LOTArea, Street, Overall Quality, Overall Cond, TOTAL BSMT, Heating GasA, Heating GasW, Heating Wall, Heating Grav, Heating OthW, Heating Floor
    PredictedmodelResult = model.predict(a)
    print(PredictedmodelResult)

  9. Cherub Weeratunge

    a=[[ 20, 9600,0,6,8,1262,1,0,0,0,0,0]] #Data Format: MSSubClass, LOTArea, Street, Overall Quality, Overall Cond, TOTAL BSMT, Heating GasA, Heating GasW, Heating Wall, Heating Grav, Heating OthW, Heating Floor
    PredictedmodelResult = model.predict(a)
    print(PredictedmodelResult)

    [190130.66855158]

  10. Ayisha banu.M

  11. Dr. Syam Sundar Pillalamarri

    I tried to download day 1 lecture notes. it asks for my email. i tried giving my 2 mail IDs but didnot receive any mail. really you are sharing the material of is just a fake?

    1. Pantech

      Please Check ur SPAM Folder sir in ur mail

  12. Radha Krishna Chilukalapalli

    Hello boss Sanjay,

    Very nice class boss. But I request you to share the notes and study material in WhatsApp group and telegram groups. Also your dedication and zeal is marvelous.

    Hope you get my point. By this you will be donating your knowledge to poor people who can not afford.

    Alot more to go and teach.

    With Regards
    Radha Krishna chilukalapalli

  13. Utkarsh

    Good morning ,sanjay sir 😃

    Mind blowing class ,but sanjay sir apni speed bhut tej thi ,kafi bate skip bhi hui ,but I am so excited your next class ….. Paython 😍😍

    Sanjay sir class j notes kha milenge …

  14. Sridharan. B

    Learning something new with this ML master class

  15. mahesh

    LD: 0.986047 (0.023716)
    KNN: 0.964839 (0.021656)
    CART: 0.932060 (0.031854)
    NB: 0.943798 (0.031560)
    SVM: 0.979014 (0.021946)
    LDA: 0.953101 (0.017949)
    i tried it sanjay , thanks for teaching us

  16. Rohit Pokhriyal

    LR: 0.985000 (0.018371)
    LDA: 0.952310 (0.019946)
    KNN: 0.952278 (0.009290)
    CART: 0.917089 (0.006093)
    NB: 0.942278 (0.023063)
    SVM: 0.974968 (0.022343)
    Thank you Sanjay!

  17. Rajeev

    LR: 0.955592 (0.030253)
    LDA: 0.953212 (0.034535)
    KNN: 0.929734 (0.040287)
    CART: 0.910853 (0.031071)
    NB: 0.932115 (0.035155)
    SVM: 0.610410 (0.007053)

  18. KRISHNA VAMSI

    Always great to interact with these techie legends. Education and sharing knowledge! that’s what makes us humans. Doing great job to encourage upcoming generations. I can imagine a smile if a person gains some knowledge through your concepts. God bless and be happy and wealthy.

  19. Adikaram

    LR: 0.953816 (0.020818)
    LDA: 0.953816 (0.020818)
    KNN: 0.953816 (0.020818)
    CART: 0.953816 (0.020818)
    NB: 0.953816 (0.020818)
    SVM: 0.953816 (0.020818)

  20. Shubham

    LR : 94.48076923076924% mean , (2.906957635672559%) std
    CART : 92.23717948717947% mean , (5.290885225501307%) std
    KNN : 92.21794871794872% mean , (3.4258847602528215%) std
    NB : 93.97435897435898% mean , (3.3839840125954543%) std
    SVM : 62.82051282051283% mean , (0.6410256410256432%) std
    LDA : 94.9871794871795% mean , (2.958289926619907%) std

  21. Minal Gavhane

    LR: 0.947285 (0.027702)
    LDA: 0.953130 (0.029324)
    KNN: 0.927677 (0.021649)
    CART: 0.915950 (0.043777)
    NB: 0.931523 (0.033237)
    SVM: 0.626961 (0.004410)

  22. Aatir Nadim

    mean and std

  23. Aatir Nadim

    LR: 0.947343 (0.032476)
    LDA: 0.956087 (0.013680)
    KNN: 0.927488 (0.038094)
    CART: 0.927488 (0.024244)
    NB: 0.940773 (0.029156)
    SVM: 0.637440 (0.007005)

  24. Shankar

    Accuracy of LR , standard Deviation of LR
    0.94845 0.03586
    Accuracy of LDA , standard Deviation of LDA
    0.95775 0.03096
    Accuracy of DT , standard Deviation of DT
    0.95089 0.02186
    Accuracy of KNN , standard Deviation of KNN
    0.91573 0.04917
    Accuracy of GNB , standard Deviation of GNB
    0.93909 0.03623
    Accuracy of RFC , standard Deviation of RFC
    0.95548 0.02198
    Accuracy of SVM , standard Deviation of SVM
    0.92254 0.04443

    Thank you so much for your intense work, your sessions are really awesome. sanjay and jeevarajan sir are very interactive.

  25. Shankar

    Accuracy of LR , standard Deviation of LR
    0.94845 0.03586
    Accuracy of LDA , standard Deviation of LDA
    0.95775 0.03096
    Accuracy of DT , standard Deviation of DT
    0.95089 0.02186
    Accuracy of KNN , standard Deviation of KNN
    0.91573 0.04917
    Accuracy of GNB , standard Deviation of GNB
    0.93909 0.03623
    Accuracy of RFC , standard Deviation of RFC
    0.95548 0.02198
    Accuracy of SVM , standard Deviation of SVM
    0.92254 0.04443

    This is so great I think many people will benefit learning from you. sanjay and jeevarajan sir are very interactive. I hope you reach your stipulated goals sooner and Thank you very much for your intense work.

  26. Dhanshree

    LR: 0.950831 (0.036608)
    LDR: 0.957863 (0.020150)
    DTC: 0.927187 (0.032254)
    KNN: 0.927187 (0.046122)
    GNB: 0.939037 (0.033207)
    SVM: 0.901495 (0.032657)

  27. John William

    LR: 0.975942 (0.018042)
    LDA: 0.953816 (0.020818)
    KNN: 0.962754 (0.027579)
    CART: 0.951739 (0.033565)
    NB: 0.932029 (0.031328)
    SVM: 0.969324 (0.029562)

  28. SHIRISHA BOJU

    for yesterday’s assignment using breast cancer data finding machine learning algorithms outputs for the given data is:
    LR: 0.981285 (0.025173)
    LDA: 0.957863 (0.020150)
    KNN: 0.964839 (0.018995)
    NB: 0.941417 (0.027918)
    CART: 0.915615 (0.045433)
    SVM: 0.979014 (0.021946)

  29. Pradnya

    I’m very thankful to you Sanjay Sir. You are doing a great job. Your teaching style is so frankly. And due to this, now I got confidence in python programming as well as Machine Learning. Thank you so much!!

  30. Pradnya

    LR: 94.844961% mean, (3.586181)% std
    LDA: 95.775194% mean, (3.095733)% std
    KNN: 91.572536% mean, (4.917043)% std
    CART: 95.310078% mean, (2.086987)% std
    NB: 93.909192% mean, (3.623226)% std
    SVM: 92.253599% mean, (4.443324)% std

    In SVM model, I have used the gamma parameter ‘scale’ instead of ‘auto’ , because by using ‘auto’ , the accuracy was too less.

  31. ROHAN

    LR : 94.48076923076924% mean , (2.906957635672559%) std
    CART : 92.23717948717947% mean , (5.290885225501307%) std
    KNN : 92.21794871794872% mean , (3.4258847602528215%) std
    NB : 93.97435897435898% mean , (3.3839840125954543%) std
    SVM : 62.82051282051283% mean , (0.6410256410256432%) std
    LDA : 94.9871794871795% mean , (2.958289926619907%) std

  32. Deva Uma Devi

    Really very much interested topics you are covering, but while listening audio is resounding and not clear.
    The way of introducing the concepts and summary in terms of mindmaps are excellent

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