value added course in electronics and communication...

,, KG REDDY College of Engineering

Technology

Value Added Course in Electronics and Communication Engineering with

Specialization in "Introduction to Machine Learning using

Python" Held On

10/11/2019 to 15/11/2019

Department of Electronics and Communication Engineering,

KG Reddy College of F ngineering &Technology Chilkur(Village), Moinabad(Mandal), Hyderabad RR Dist-501504

~,~,~..

Coordinator Prin ~ ~ a1 r,~>',~~1~~~~r

KG ?Eddy Colle~~e of ~ngir;~e~ir~~ ~ ~e~r~r~~~,~~ ~

KG REDDY College of Engineering &x Technology

SUMMARY REPORT ON INTRODUCTION TO MACHINE LEARNING USING PYTHON

About Course

The value added course on Introduction to Machine Learning using Python is concluded its work successfully by department of Electronics and Communication (ECE) in KG ready college of Engineering and technology (KGRCET), Hyderabad, Telangana. This course is a forum to bring together students to discuss innovative ideas and diverse topics of this course on next generation of information technologies. Department has taken a new step for students to improve the quality of study through this course and become most wide scale , extensive, spectacular event in computer science engineering. The six days course was held in two locations of the department (a) Department E-learning room for theory class and (b) Department laboratory for practical class.

Machine Learning using Python is very important aspects, Python like many other programming languages, has different versions. And sometimes when we create software, the software needs to run on a specific version of the language because our software expects a certain behaviour that is present in older versions but changes in newer versions. Likewise, we may need to use specific versions of the libraries for similar reasons. But Python 2.7 and even a more modern Flask app that runs on version 0.12 and Python 3.4.

Exploring data sets and developing deep understanding about the data is one of the most important skills every data scientist should possess. People estimate that time spent on these activities can go as high as the project time in some cases. Python has been gaining a lot of ground as preferred tool for data scientists lately, and for the right reasons. Ease of learning, powerful libraries with integration of C/C++, production readiness and integration with web stack are some of the main reasons for this move lately.

In this course, it will use NumPy, Matplotlib, Seaborn and Pandas to perform data exploration. These are powerful libraries to perform data exploration in Python. The idea is to create a ready reference for some of the regular operations required frequently. It can iPython Notebook to perform data exploration, and would recommend the same for its natural fit for exploratory analysis.

This course is absolutely practical oriented course which is helped to student for making their carrier through python in industry. The students of Final year 2nd semester have been benefited in many ways from this course. Tota131 students have joined in this course as their own interest and completed this course. The trainer taught to students very nice with real time example and sharing his knowledge to develop technical skill in industry.

Scope of the Course

The role of Python and Machine Learning environment is to be emphasized in computer science and engineering, to enhance and motivate the new technology for wide range of applications. Python can pretty much do the same tasks as R: data wrangling, engineering, feature selection web scrapping, app and so on. Python is a tool to deploy and implement machine learning at alarge-scale. Python codes are easier to maintain and more robust than R. Years ago; Python didn't have many data analysis and machine learning libraries. Recently, Python is catching up and provides cutting-edge API for machine learning or Artificial Intelligence. Most of the data science job can be done with five Python libraries:

KG REDDY Collegeo Engineeruig College of Engineeruig &t Technology

Numpy, Pandas, Scipy, Scikit-learn and Seaborn. Python, on the other hand, makes replicability and accessibility easier than R. In fact, if you need to use the results of your analysis in an application or website, Python is the best choice.

The bright future for Python learning students: 1. Python has been voted as most favorite programming language beating C, C++ and java

programming. Python programming is open source programming language and used to develop almost every kind of application.

2. Python is being used worldwide as a wide range of application development and system development programming language. Big brands and search engine giants are using python programming to make their task easier. Google, Yahoo, Quora, Facebook are using python programming to solve their complex programming problems.

3. Python programming is versatile, robust and comprehensive. Python is high-level programming language and easy to learn as well as it reduces the coding effort compare to other programming languages.

4. Python programming is used to write test scripts and tests mobile devices performance. It is one of the most versatile languages these days. Python programmers are most demandable in the IT industry these days and get paid more compared to another language programmer.

Python, though a newer entrant in the fray, has gained importance than other programming languages and holds a lot of promise for developers. Apart from being an open source programming language, it is also one of the most versatile programming languages. Developers use it extensively for application development and system development programming. Also, reduced coding effort and better test performance ensure better programming. Hence, python developers are very much in demand.

An important feature of this course is very useful in service carrier. The selected topics of this course helped to make project work. This permits also a rapid and broad dissemination of project and research work.

Objectives of the course

The objective of the course is to bring together experts from academic institute and training institute for sharing of knowledge, expertise and experience in emerging trends related to the computer science and engineering topics.

■ To understand why Python is a useful scripting language for developers. ■ To learn how to design and program Python applications. ■ To learn how to use lists, tuples, and dictionaries in Python programs. ■ To learn how to identify Python object types. ■ To learn how to use indexing and slicing to access data in Python programs. ■ To define the structure and components of a Python program. ■ To learn how to write loops and decision statements in Python. ■ To learn how to write functions and pass arguments in Python. ■ To learn how to build and package Python modules for reusability. ■ To learn how to read and write files in Python. • To learn how to design object-oriented programs with Python classes. ■ To learn how to use class inheritance in Python for reusability. ■ To learn how to use exception handling in Python applications for error handling.

KG REDDY College of Engineering Fu Technology

• Learn one of the most popular tool for data analytics • Learn Python Programming basics and essentials, along with machine learning for

conducting data analytics in Python • Hybrid Learning with Guided practice &Weekly Practice quiz questions on the app

along with the classroom Sessions • Hands-on application of the Tools • App based learning. Connect with Faculty on the App apart from the regular

classroom training. • Data modelling using Machine learning Techniques.

OUTCOMES

This course was not only shared the knowledge among students but also tied up with expert for upcoming course. It can use different python environment to help on manage workloads for data science, scientific computing, analytics, and large-scale data processing. It can check out on data analysis and machine learning to learn more about various tools available to use and projects that it can do.

1. Describe the Numbers, Math functions, Strings, List, Tuples and Dictionaries in Python 2. Express different Decision Making statements and Functions 3. Interpret Object oriented programming in Python 4. Understand and summarize different File handling operations 5. Explain how to design GUI Applications in Python and evaluate different database operations 6. Design and develop Client Server network applications using Python 7. Explore different packages of python like numpy, matplotlib, jupyter.

8. Import and apply the packages for application development..

9. Build simple linear and multiple linear regression applications

Summary of Participants

(a) Number of students attended this course: 33

KG REDDY Culiei;e of I uginecrin<~

Tecluu~k~~~~

Day-1 (1Q11-19)

Time: 09:00 AM to 1 I :00 AM

Inauguration of value added course

The first day of value added course started with welcoming and opening ceremony at the

KGRCET conference Hall. The following dignitaries were representatives of the value added

course who were addressed and pointed out the importance on course with short welcoming

speeches.

Welcome addressed by Dr. Pravin Khirsagar, HOD, ECE, KGRCET

About the value added course by Principal Dr. R. S. Jahagirdar, KGRCET.

Resourse Person: Arpit Yadav

Time: 11:10 AM to 04:15 PM The Boot Camp started with a discussion on basics of python programming and then

introduction to different machine learning packages used for different applications like

numerical calculations, data visualizations, algorithm training.

Mr. Arpit Yadav Giving Introduction about Bootcamp to Students

KG REDDY College of Engineering &z Technology

Day-2 (11-11-19)

Second day workshop continued with students working on numpy (numerical python)

package which is of great use to numerical calculations and pandas library. Students were

given examples and asked to solve similar other examples.

Day-3 (12-11-19)

Third day the learning continued with matplot lib package of python which is mainly used for

graphical visualizations. Students were given several hands on experiments to do which was

really great fetching to students. It helped them to explore things practically.

KG REDDY y Callegeo Engineering College of Engineering

6z Technology

Students doing the Practical Hands on Session

Day-4 (13-11-19)

Fourth day the program started with the trainer summarizing all the topics and packages

covered during three days. Trainer has given few sample projects to work on by using simple

linear regression and multiple linear regressions. Students worked on a data set called land prices.

Anaconda Navigator is a desktop graphical user interface (GIJI) included in Anaconda® distribution that allows you to launch applications and easily manage Gonda packages, environments and channels without using command-line commands. Navigator can search for packages on Anaconda Cloud or in a local Anaconda Repository. It is available for Windows, macOS and Linux. In order to run, many scientific packages depend on specific versions of other packages. Data scientists often use multiple versions of many packages, and use multiple environments to separate these different versions.

KG REDDY ~,, ,r Collegco Engineerin;; ,~ College of Engineeruin

&z Technology

Day-5 (14-11-19)

Setup:

1. Download Anaconda Package Manager

2. Download and install an IDE of your choice (I recommend PyCharm and will use it

through out this guide)

Step 1: Create a new enviroment and link it to your project

There are 2 ways of doing this

1.Create a new Anaconda Environment using Terminal then use that environment when setting

up a new project in PyCharm

To create a new environment from terminal use the following command:

Gonda create --name nameofyourEnvironment

Or to create a new enviroment with specific python version use

Gonda create -n nameofyourEnviroment python=3.6

More ways of creating environments can be found here.

Once the environment has been created, you can open up the IDE of your choice and select the

environment you just created.

For PyCharm this can be done by selecting File New Project. In the new project window click

the Existing Interpreter radio button (1) and then select browse

KG REDDY College of Engineering &t Technology

Day-6 (15-11-19)

Supervised Learning Project on Machine Learning .

Course Coordinator

KG REDDY C;ollc~~c of En<~ulccrin~

Tech.nolog}'

Ref No: KGRCET/CSE/2019-201 Date: 01/11/2019

CIRCULAR

All the students of IV Year I-semester B.Tech ECE are here by informed to enroll for the Value added course (Bootcamp) on "Introduction to Machine Learning Using Python", which is offered by KG Reddy college of Engineering and Technology from 10/11/2019 to 15/11/2019. The students are instructed to contact Dr. Hemanta Kumar Bhuyan for completing their registration before 05/11/2019.

s~~ HODrf. V , '~ ~,

L 'CS & COEz9wy~tli:,AT?~Jr ayEUGR3E9E~,li~:~s

.;.K ~ Rk,DDY ,,̂!~1.LE~~ OF ~NGst~~~• CiirS.541

Ciil~ ~,VR (`~1, ~4iNf,B~J, R.R.

Copy to:

l.Exam section

2. Library

Principal Pri; ~ cit~~l1

KG REddy College of Enoi,,~~~;ng u ~~:`.. .,i z.

R.~.C~is:., Teiat7~a^a.

,t

KG REDD~ C~llene of Fn; ,; et-i;?_

Technol~iGy

KG Reddy College of Engineering &Technology Chilkur, Moinabad, RR District

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

Value Added course, Syllabus KGRCET, Hyderabad

KGRVAC0507: INTRODUCTION TO MACHINE LEARNING USING PYTHON

B.Tech.,ECE, IV Year-I Sem

Course Objectives

(a) Understand Python is a useful scripting language for developers. (b) Use lists, tuples, and dictionaries in Python programs. (c) Use indexing and slicing to access data in Python programs. (d) Define the structure and components of a Python program. (e) Design loops and decision statements in Python. (f j Define functions and pass arguments in Python. (g) Build package Python modules for reusability. (h) Design object-oriented programs with Python classes. (i) Use class inheritance in Python for reusability. (j) Use exception handling in Python applications for error handling. (k) Demonstrate different packages of Python useful for data analysis etc.. (1) Express understanding and apply the knowledge of importing different packages (m) Create simple machine learning projects like cost estimation

Course Outcomes

At the end of the course, the student will be able to:

L-30

_ ~_~~ i_ acerecBted try NAAC

(a) Use if-else statements and switch-case statements to write programs in Python to tackle any decision-making scenario

(b) Create an entire Python project using objects and classes (c) Store and retrieve information using variables (d) Develop cost-effective robust applications using the latest Python trends and

technologies (e) Proficient in Debugging and Version Control (fi Build systems entire web development process using various tools (g) Create and use APIs to write back-end code (h) Explore different packages of python hke numpy, matplotlib, jupyter. (i) Import and apply the packages for application development G) Build simple linear and multiple linear regression applications

KG REDDY

Module 1

Introduction to Python Install and Import Python Packages Installation and Working with Python Understanding Python variables

Module 2

Python basic Operators Understanding python blocks Python basic Operators Understanding python blocks

Module 3

Python Data Types Declaring and using Numeric data types: int, float, Complex Using string data type and string operations

Module 4

Defining list and list slicing Use of Tuple data type Conditional blocks using if, else and elseif Simple for loops in python For loop using ranges, string

Module 5

list and dictionaries Organizing python codes using functions Organizing python projects into modules Importing own module as well as external modules, Understanding Packages,

Module 6

Powerful Lamda function in python Building blocks of python programs Understanding string in build methods List manipulation using in build methods, Dictionary manipulation, Programming using string

Module 7

Machine Learning

Types of Machine Learning

Module 8

Supervised Machine Learning

Unsupervised Machine Learning

Reinforcement Machine Learning

Semi supervised Machine Learning

Module 4

Regression Learning

Classification Learning

Signature of Chairman of BOS, ECE

KG REI~D~' Col iege of E n;:~,

Teclmolo~y

KG REDDY COLLEGE OF ENGINEERING &TECHNOLOGY Chilkur (Vill) Moinabad (Mdl) R R Dist

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

VALUE ADDED COURSES ON INTRODUCTION TO MACHINE LEARNING USING PYTHON

Schedule.

10-11-2019

Forenoon (10:00 to 1:00 pm):

• Introduction to Machine Learning • Role of Python in Data Analytics • Importance of python libraries

Afternoon (1:30 to4:30pm):

• Introduction to packages like numpy, pandas, Simple examples on numpy like working with arrays

• Exercises on the topics covered.

11-11-2019


• Introduction to Matplotlib. • Application areas of Matplotlib. • Practical examples. • Hands on practice of the topics covered.


• More examples practice on Matplotlib. • Introduction to Pandas. • Examples on pandas.

12-ll-2019


• Introduction to scipy. • Applications of scipy.

KG REDDY ~ Technc~Ic~y


• Practical examples on scipy. • Problem statement given to students to apply the concepts and come up with solution.

13-11-2019

Forenoon 10:00 to 1:00 pm)

• Introduction to machine learning • Role of machine learning in data analytics • Different algorithms of machine learning.


• Different datasets shared with students. • St<dents are explained haw to train algorithms using datasets. • Students are asked to do more examples on the topics.

14-11-2019

Forenoon (10:00 to 1:00 pm}:

• Practical session


• Practical examples session

15-11-2019


• Practical session


• Practical examples session

~~ KG RI=DDY' t:'r, ii,~c~ e>E i ,7t;laxcR.i'~ix',;^ ~.i I scl~rr«l,~ ;~•

Accredited liy NAAC DEPARTMENT OF ELECTRONICS AND COMMUNICATION

ENGINEERING

BOOT CAMP(DAY-1) TOPIC: Machine Learning using PYTHON Date: I C r t~ ~ 2" I~

Si. No. Roll number Name of the Student Year Signature

1 I ~ Q M 1 Acs 4 fl .L ' -Ta -

(3 ~ ~ u'►r1 K 1a,~,.ati ~~ 2 ~ 6 ~"w t~4a'~ c• ~ (- c ~ 3 16 ~ tu'I 1 R ~ tl. t~ fn ~ . M r rr c,~. ~,1 i r~~ Ka 1 ~ ~t !mot ~ r,~~ t~+ ~ia~ 4 ~ ~ ~ M 1•fl 0 ~{ o~ G , C P'i~^rrEv~Q, ~2~

.

g p ~

au a'~^rt c, ~ 16gMtfl©~ft~ ,~ _l9 t 6 '~ M l A 0 k 1.~ ~~ Yt a,~~k q ~ ,y

~ ~ cum_ to ,~Q M l -~}e~ L{ 2—S 1~ ~ h.~~~.Vct(~~ ~]~ 4 ~~E~~ ~~ 1 t 1(, ~ N{ I ~ o ~ 2-G ~ ~ 1L~~: -I~,~ ~11L ~ ~ Y -}f~-12 1 ~ ~M1 0~ GtI le ~,~~~~a~.~,i ~' S~~'~ 13 l.~ 4 l~,Sa~.~'~,,i ~ ~~,~i~~

14 i{ 3 ~ "~ ~~' ~~ 15 N~`~

16 1(~' I i A-o ~{ o '~ k . ,,,~ • 7 I<,,~}s L, .,~ TV ~.c -17 ~ ~-6 t S~i-o yr t a; • .~

~ i - - ,~

19 ~~ ~ t~ U~ o ~ L( ( 1 ' ~~ ,n c~'Y~, ~~ 20 FCC Q (Yl ~ ~-~ y `~_c~ t~ 'l ` ~ 21 1 6 ~ ~ l ~ ~ ~ 3 ~ ~ ~ t ~C~d'1ii ~

.~_

~ ~~ ~~ ~ ~_-

c~~̀ , ~ 23 ~ ~6QI~t [ X1rJU 1?3 24 ~ ~ ~ ~ O Lt ~' P~ ~d ~ P dc~'~ 2s t L ~t ~d ~t ~i ~- ~ - <S ~..dl.~ Cho ~ ` ~ ~r 26 Ih A r-t r~~ ~Z6 P~-~c~a~~~m~a -:4

28 l 6 ~ ~i ► LI D ~ ~ ~ K_~,~er~1~ t~.c~C L ~ c . 29 -r6c~ ~ t ~o ~► ~t ~"~ ~ ~~ V ~ ~

~~

30 G{-Dt ~-/►'(cL~o' kvmetf 1L ~I~ "

31 I~~ma#10 433 omI - ~~~~ ~ R r

c'~-~ -Y`' 32 ~~ (~t'~ t~ o ~E32 ~ 33~ ~Qt t~ M ~ ~ ~Is3 1 ~ i N ~r.u~+'~ ' .~

CO-ORDINATOR D t~EA~ c ENGtNEERINt~

~~~j~ot+tcs c ca~l~a~~~t ~nt1oN~ ~. ,..~otoo~ p ~ ~ r. i}tu

(J~~,l`,~llri. 1 ~

PRIN ~~:'f''Ff:j~)(1~

~i ~.!

KG REDDY C:.ollc~r of f:nginecrini;

Accredited lay NAAC

DEPARTMENT OF ELECTRONICS AND COMMUNICATION

ENGINEERING

BOOT CAMP(DAY-2)

TOPIC: Machine Learning using PYTHON Date: I 1 201

Si. No. Roll number

Name of the Student fear Signature

2 t~6~~ iA-a 40 ~ c~ S~•~ ~-~-~~ 3 ~ ~ g rn i ~ ~ r~ n~ ~t . r~ ~ t,~ a~ r~~~~ 4 I h~ M1tfln~~. ~~ ~~ ('~~~~ `~P~ ~ ~~~-~'~-

8 1(;~,M► ~ 4~~. ~~ u~iY~h~;(a ~ ~_ ~~ Sx~.~~~~~~ 9 ~ ~ a ~I ~ t o ~ G c. 2 w ~ ~ A-11 1 6 ~ M I ~ 42-`3 ~ • I~ e C ~`1~ {,~F ~V ~' 12 1-13 ~ ~ ~ g R Ini ~h e ~ ~.~~'~~ 13 4 .3 ~ T - M ~cl ~l 4L i `~

~v r-tct~l U.u~ .t

15 ~~~ ~• ~'~z~~ ~v ~~ Y16 ~ 6 6 1 I ~- o ~ o 'L ~ • /~ ~ ~ p.4~ a- ~, 1bt9~~

c17 ~~-~~S~D~-ib) ~~~~~ ~~-~ i s 1 6~ m~ L1oy ~3 ~ s-.fir ~~~~

~~~ ~ `~

21 ~ 6 rvt CA o l~ 3s ~ ~ ~~ 'V~ ~~ ~

23 ~~~ ~~I 0 18 ~~~~ ~/4j

~ ~~~ 24 ~ 6 M C I`~o 4 2, 8 ~e:S~ ph~)~•n ~ :~;~

26 tb ~ M I ~ ~ ~{- 2 ~ ~ • ~ M(,o~~ ~ 27 ~ 1 °l c~C~~ ~_ 28 Ll~ i - 'Gi~YUJ4cvn~ Y /`(nM• ~ ] 29 ti 3q c~v~ ~ ~ ~ _. 30 j ~~~~ ~ Icv~~ r-- ~ ',`'

32 t{ 3 2 ~ t1f ~ ' . ~ 3~ G ~ i ~ rwt~n ~ ~ ~

. ~iE.ERlF1•:: ~'~.~

PRINCIPAL l';-i;Tcinnl

`,.1 `, 7,

~G R~DLIY - C-;olle},e cif li.npinec~rixz~;;

kz "i crchntilcst;Y

Accreiiiteci liy NAAC


ENGINEERING

BOOT CAMP(DAY-3) TOPIC: Machine Learning using PYTHON Date: ~2~ 1 ~12-~~~

Si. No. Roll number

Name of the Student fear Signature

5 1 h ~ rn r ~ ~ ~P ~~ ~cc_ ~s--~~ 6 ~r r~ t

"1 I ~ ~,~ ,,,, Q ~f U1lL~~ R ~ `~1 v

7 16Q M~fl0~I2 C~- ~a~ c~th~ ~_ r •~' - s L~~, ~, r~ 4+ ~ k. ~r v~ ~ ~ ~ 'J

10 1~~~ t u 2 ~ ~-I ~ ~ ~ ~ ~ [t~~ ~V ~~,.,~ a~ 11

, ~ C~ QM 1--A--o t-( 2°I ~ • ICCS~--~-C-t ~ ~~l . ~~~~

12 iF~~t IMP C~ ~-~~ R < <gn~~ ~ ~c~l,~`~; 13 ~E~~ ff~ ~ `j-•~`~ T- ~~°I.C~In~~4~t ~ ~tie~l,tiwia 14 ~C~~1~C14

~

-~

1+~

6 ~«• S ~vCj~v~~-~~ ~U ~~~,%tvc~~.~~ 1 S

~'~P~-~ ~ ~ ~~Y~ ~U 'i:~:'~' ,

16

17 l ~~ ~ ~~4-0 ~{ t~~ ~~ ~ ~ ~~~a '~-~. ~ 18

19 ~ ~ Q rr-i 1 ~ c, ̀-~ t,t 1 ~ ~ „) Ga C n d vG~ ~ v 20 1 ~'~M ~ .~h ~ ~ o K. ~1~,~+~~ ~ 21 1 ~~Mt~QC~3~

~,~ ~- ~p~r°, Urxe `

22 i,6 ~ M, r ~ o ~ ~ ~ ~In ~h~n.~ ~ M `~23 ~ 6 ~ M 1 ~~Jt..( 13 L~~ ' ` t 24 ~ ~ 6 ej h'1 f'~ ©C F 2$ ~ • 1 Vct'ts~S ~ P ~~k~'ah

26 r ~ ~ r--i (,p~ ~ ~ 2 6 P'Scirra.r~ ~ i►v.~ ~~ • ~ —27 ~I 1°I c~vu'kcr~ ~' 28 ~ 2 ~

_ ~ N Urt,~

29 Gl ~ °~ GrvV~l/ ~^~~ 30 ~` I (~ ~' r.,t ~ kU•►-tom L 31 t.~ a,3 ~ ~~ "'~

33 ~ ~ 1 ~C ~ ~-t-at`1 t1V ~o`ru-~~

CO-ORDINATOR OD

~"~ 1 ENGt~ER~' f ~ sC3 h

C011H~1WiCA'Tttlt3 ~~~,~`i;G` ~FQ'i. off 1:~.~ r~k~~~ 4~ E~•,;;:EE„t!'~G ~4

" a, prtiti ~ ~ ̂ ~_:" 1kO1;iA~'~'~, Ct.R. p15`t.5ft'i

' CH;~ Kti;, ~til,

PRIN ~Z'ZZ7CZ~t7

i(v Gr,~{^,'~i L~t ~• ~ p ~I r','~J',.'1~gil~~ ~1 TC~il~~lt~7~

tt.Fc.i~i~f., ~~a~~7tsf:cjan~.

KG REDLY Cc~llc:;e of ]=n~~incc r,n~,~°v t cchnnlc,;~t~

Accredited E3~ NAAC: DEPARTMENT OF ELECTRONICS AND COMMUNICATION

ENGINEERING

BOOT CAMP(DAY-4)

TOPIC: Machine Learning using PYTHON Date: ~ 3 I I ~ 12d ~ `~

Si. No. Roll number

Name of the Student Year Signature

1 16 ~ ►~) ~~ o L Q • (~ rum ~M,..,,,~ ~ —~ ~,~

4 l ~ t~l,^

~ ~l D L~1

O/(~~

f'~ . r ~ ~a.e.%--GLA. `~2' i,i ~ ~L C'~,P,i~~~y~- /6

6 `"I ~V ~~I~1f UV1'~Q ~~ ~ w

g tb~mtflo~tiz ~'~ a c~~u' ~ ~ ~7~-

9 t >o p m ~ ~, y t ~ ~ ~, ~~ ~~ l 0 1~ ~►~~-~-~ k 2~

~ ,,~v tit ~I~~/~-~-~ I ~ ~jl~~

11 t 6 ~ l~ l -~-o u z 9 ~ • 1C c~.S~--f-(,~-i i — p~~~j~

~ C~~ l 2 ~ ~ ~ M t A p `~ ~ `~` ~ ~ ,S 0. ~1i`~4 ~ 13 ~ ~ ~ t~11~~ ~- 3 g ~ M a~ buvu r~ ~'~o~c~b~wu 14 ~ 1 ~ (~:~ ̂ ~ ~ f~~F- ► ~ 1<S . ,q,~ v-0.L a ~ L env ~, 15 4 ;~ ~ ~ ~ ~' U1 ~ 1~~Y `~~, _~` 16

~ ~6 11.9--0 ̀ -(0 L !C ' K~~P~•G.~ ~ , ~ -ti ~~ 17 1 X61 S- ~Fn ~ ~ ~ K ~ t, ~,!-~

~ 18 i 6~~ ~~~ ~C s~ y~~I~~~-P ~ ~~,~ ~ 19 16 ~ m ~ ~ ~ u~ , ~,,,,. ~YB ~~a ,

A ~ 21 l6~MIA61~3S ~~ ~~ Vc.~ ►~J :•,. `J~'

~~

24 (Ej er YYl ~ ~- C~ X42 ~ ~ /V h ('~ O ~~sZ~.h .~, ~" 25 p -26 16QMt~o►//r p 1

!'4~Ir,~1y?~y/~~~fMI~A ~ ~1

27 ~[

# 'I ~ I ' ~~~V~~.WN~'" 1 ~ ~ _ C n~

29 ~Zv7 ~] ~ . 3 0 ~C l ~ ' ~'~~~ L~vrvto~S~

t'~ ~1~

~~~id~`!"f —V ~~ 31 ~f 33 ~ Y~~

32 l(3~ ~ r ~^ yv 3 3 y 31 S r mlo~.a1 ~ riv ~ ~ ~,

CO-ORD>f?PQATOR H rf E':~ d

')fP't. t1P L"l~ :7R.",Nh~S d COi~dtfNICAT:;!"~S Fr►IGtiJEfit~?~ S.G. R~C~Y CCLI.=G~ 0~ EhGiF1~CR,13G ~ TECl1NOLQGY

C;~LKJt~; (`,';, ~SOfMABf1D, R.~c. DtST.501 5t}d

PRINC Prr'~3cf~nd

~!'` • dA,~ ~:1ilc^o n~ ;';~~'i$t'il;~~~ d !c:('I',l0(G~Y

KG REDLY i cchn<~i.,~y

Accredited liy NAAC


ENGINEERING

BOOT CAMP(DAY-5)

TOPIC: Machine Learning using PYTHON Date: ~ ~ ~ 1 1 12n ~~

Si. No. Roll number

Name of the Student Year Signature

1 ~ 6 Q~ I tto ~ v ~-- fS -T~.~~ ICti,~ ._n_ ~1 -~=

~ 4 1 ~6 ~ M i ~ 0 ~

o ~ ~ , ~-~a~`fG~- ~e-~a ~

i C+ 5 Lb ~, m r~rn a e~ P~, f • ~~~--~ 6 f -1 ~ b

kmen ~~~unid~a ~~ ~ ~- ~ ~~ ~ .,, ~, 7

fl 2 n C

~, 8 ~ b ~M ~~ 1 q-- ~' y, ~ ~ ~ ~-~ . 9 t~~rr~ab~rs k, ~~-r ~,

~~,~-~~ l o (~ ~ M 1 ~~ u ~ ~ ~ ~ ~. L 11 r ~ q M ~ ~ u a ~ ~ ~ ~~~-~-~ ~ ~ ~.-e-~~-

~~~~x~ lz ~~~~~~~o~.~~- R ~ s~~~~F~ 13 j61Q~1~C~`~- ~4 -~"- t 0̀1~~~vY► ~ f-~ct~l~u~i 14 I ~ ~ ~ ~ f~0'~- ~ ~ ~' ~~ Ct~~ i

~y. ~ -y~ ~~~i

15 l~ ̀~ (~~~.C~~~a1~ ~ti. _ ~' -1 ~

t7 ~ ~ ~'~ f ~ v ~b K , ~~-z10 E k~~ ~ 1 s ~ ~ ~ ~,,,~, ~ A ~~ ~ ~ c s • y e ~ 1.~, ~,,, ~ ~ r-rti, ,~

19 16 ~ ~ }1-~ ~ k I J' r~l~ea~cl yc` a v ,~

d~~-a~ ,~,~~

~~Y~.0~11/ 22 ~;6 t~~ (. A~b ,~ ~ ~ M ~ ~ ,rs~n~. 23 f~~M (1~dU l` ~ L~~ ~'t 24 1 68, iY11 x}-042 ~ P'~0.'~S'eS~P roKzs~+-~ ;..1~' 25 1 ~ ~M t~-o ~ ~ z ~l, ~ ~.~ lw. ~ ~ ~ 26 IG ~1`~( r ~~ 4 ~

~o Pt~~ ~~~..~ ~ `~

27 ~ ~ q rS~v~~o~'l -t-

29 '" v7 I ~n ; ~ 30 ~r

~

,~,~ 1titAt'le' kV _

~t V i 31 ~ ~3 ~, ~ ~-~—

CO-~DINATOR PRINCI~~1L

~'. ..;i~i;J!!i

KG REDDY `.a> Collct=,e of En~;incrrini:;

~ i -cciux~la~,Y

Accredited lay NAAC DEPARTMENT OF ELECTRONICS AND COMMUNICATION

ENGINEERING

BOOT CAMP(DAY-6)

TOPIC: Machine Learning using PYTHON Date: l'~~ < < ~~°►~~ Si. No.

Roll number Name of the Student fear Signature

1 ~. ~ Q 'x"11 A-e 4 Qti ~ ~ v~ ~~n r~ ah

~

1~ ~~

2 ~ 6 ~~~ ~~o~ c . ~~~ ,~~, ~,~p~~ 3 I Ca ~ Nt 1 A ~ cf-©~ T~. i~7c Nr h~~nK~z ~,V ~.M. +~7f l~x~'kn.

~ /6~mrfloUiz , C'Q~,~~i ~t Cam' _^~ _ ~,

10 ~ ~ ~-t 'LS cu ~ ~ 1 ti' ~,c~ 11 .6 ~ µ t~b~-12-~ ~ ~ K e-e~~i~ `~ ~, es~h~-1-~ 12 l 6 ~ NI ~ ~ t 4 ~~. R ~ S~l~~i ~ ' ~ cti~t~ -

14 ~6(~ t~\~ ~~ ~ It S • ~v 3n~~Qi 15 (~~?

~S~iv0.~Qi ~'. ~hp~~ ~ ~:

19 ~ -, ci ~ / ..,.dam'"

~~ ~~,,,.~->^_

21 ~ ~ ►~ ~A © 3s ~ 4 e- ~~, U ~tv 22 ~ ~ ,~ ~ ~~- ~ _r .

24 ~ ~ Q nil /`~-- ~ ~ ~ N ~ s~ (' 11 p~Sb~1~ ~~' 25 lC~t~~t~0u~t 2 V> ~~.~ ~,u~t. ~et,~ t~ 'V 26 ((„ Q M I ~ O 1.1 L~ ~'Sou~ 1ro~ ~ int~c I~'c~ ' ~

2s ~Z I Mcr~:la~►,t~ 29 Gl ~Ol

~~ ~~

3 0 q-rj I /~C~, ~ kv~ cs~ ~ ,. 31 t{33

~~~t • ~ ~~ 32 X13.2 ~~~ ' '

CO-

~-. ~NATOR =n~

Ql:~r. ref ~~FRO~ICs a c?ldtlt!?a,~~~ EN~giE£R;"~ F.G. ':~OCY':Cn r,:g~ OF ENGfNt`£RIiiG F.; TE~rfNO~ C~::1'

G ~ ' ,r I' Rjf~T t: [1 GA1 'iii..`•. :;il; E~~•IVf,`r . ; 1{.C. ~n~.L ~ 5Q1

PRINCIPAL I'r•incznnl

~~ Nc~J'~ CO~I~y Oi .. 'I~i:i~~'(iiiG ` T~_!1i1010 ; Crli:ifi.:f i~`i: .r , .. . . :~t .. ~!Vr).

KG REDDY COLLEGE OF ENGINEERING &TECHNOLOGY Chilkur (Vill) Moinabad (Mdl) R R Dist

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING VALUE ADDED COURSE ON INTRODUCTION TO MACHINE LEARNING USING PYTHON

FEEDBACK FORM

Title of the Course: Introduction to Machine Learning Using Python

Trainer/s Name: Arpit Yadav

Course Start Date: 10/11/2019 Course End Date: 15/11/2019

Venue: Rating Scale:5- Excellent, =1-Very good, 3-Good, 2-Average, l -Poor

Content 5 4 3 2 1 Programme Topics/Modules are relevant to my role The programme Objectives and Structure aze easily Understandable

,/

It is a good mix of Theory and Practice

Arrangements 5 4 3 2 1 Availability of Projection Equipment, Audio/Video Prior Communication about the programme sent to the Participands

~

Provision of Lab Facility /Internet Connectivity ~ Trainerl Name 5 4 3 2 1

Adhering to Time Schedules ~' Engagement with the Participants ~ Activity-based Teaching/Skilling ~

Trainerl Name (if applicable) 5 4 3 2 1 Adhering to Time Schedules Engagement with the Participants Activity-based Teaching/Skilling

Learning Outcome 5 4 3 2 1 It has enhanced my Knowledge It has empowered me to apply the learning in my work / It has enabled me to think Out-of-the-Box / Overall Programme Rating

Additional Comments (If Any) ~ ~ ~' U~ ~ ~ ~ ~ ~n~ cx .

Name: ~1~ (~ ~ ion 1i~. Roll No: ~ (~ (sL ~,.~ ( f} o ~, ~n~a u `^ ,

Signature:

t~ KG REDDY COLLEGE OF ENGINEERING &TECHNOLOGY

Chilkur (Vill) Moinabad (Mdl) R R Dist

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING VALUE ADDED COURSE ON INTRODUCTION TO MACHINE LEARNING USING PYTHON

FEEDBACK FORM Title of the Course: Introduction to Machine Learning Using Python

Trainer/s Name: Arpit Yadav

Course Start Date: 1OJ11/2019 Course End Date: 15/11/2019

Venue: Rating Scale:5- Excellent, -l-Very goon 3-Good, 2 Average, l -Poor

Content ~ 4 3 2 1 Programme Topics/Modules are relevant to my role The programme Objectives and Structure are easily Understandable

,~'

It is a good mix of Theory and Practice l!"

Arrangements 5 4 3 2 1 Availability of Projection Equipment, AudioNideo ~ Prior Communication about the programme sent to the Participantls Provision of Lab Facility (Internet Connectivity t,G

Trainerl Name 5 4 3 2 1 Adhering to Time Schedules Engagement with the Participants ~ Activity-based Teaching/Skilling ~/

Trainerl Name (if applicable) 5 4 3 2 1 Adhering to Time Schedules Engagement with the Participants Activity-based Teaching(Skilling

Learning Outcome 5 4 3 2 1 It has enhanced my Knowledge It has empowered me to apply the learning in my work It has enabled me to think Out-of-the-Box Overall Programme Rating

Additional Comments (If Any)

Name: ~- C~gi ~~v~c~ Roll No: ~G4 ~ C ~ ~r }~'

value added course in electronics and communication...

Documents