## Machine Learning Master Class – Learn Easier than you Think?

Hai, It’s me Sanjay Machine Learning Master Class Instructor, writing this Blog for you to deliver the values on Machine Learning.

## Introduction to A.I & Machine Learning

• What is Machine Learning (M.L)?
• What are the Types of ML?
• List of M.L Algorithm?
• What are the things you need to learn to become a Data scientist?
• Why do you need to Learn M.L?

## Supervised Learning – Classification & Regression

This Mind Map will give precise information about Supervised Learning algorithms

## Task – 1: Evaluating Machine Learning Algorithm using custom Dataset

As per the Session, you need to evaluate ML Model for Custom Dataset. You can use any dataset (numerical data in csv format) for the code attached below. TASK is to, You Need to Post the Screen Shot of the Validated Report with your Dataset Tile or Project Title in the Comment Box.

Note: 1. You can download dataset from Kaggle or You can create your own dataset for certain application
2. Don’t use Energy Meter Dataset Provided by us for TASK

## A.I Podcast – If 5Minute/ PerDay then A.I ?

You can Listen to the A.I Podcast to Learn A.I Terminology in Maximum 5 Minute / Day

## Join in A.I Community – Private Facebook Group

Join in our Community & Feel the Value

### This Post Has 18 Comments

1. 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

2. 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

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.

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!!

5. 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)

6. 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)

7. 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)

8. 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.

9. 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.

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)

mean and std

12. 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)

13. 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

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)

15. 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.

16. 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)

17. 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!

18. 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

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