CYBORG TUTORIALS

Search This Blog

Kerala state government jobs

August 14, 2022

Get link
Facebook
Twitter
Pinterest
Email
Other Apps

Get link
Facebook
Twitter
Pinterest
Email
Other Apps

Comments

Viewers please click the below google advertisements for our website revenue.

Donate

Followers

All Companies Placement Papers

IBM Free Certification Courses

Free online courses: Data Science and Cognitive Science Courses Earn IBM Badge & Certificate For Free 📢 Certificate By : IBM Cognitive Classes Fees : ₹0 Level : Beginner To Advance Course (1) IBM Blockchain Foundation Course Double click to enroll now (2) IBM Machine Learning with Python Double click to enroll now > (3) IBM Python For Data Science Free Course With Certification Double click to enroll now (4) IBM Deep Learning Free Course With Certification Double click to enroll now (5) IBM SQL & Relational Database Management Free Course Double click to enroll now Free Data science courses for beginners in udemy: 1. Python for data Science Double click to enroll now 2. Python for data science - Great Learning Double click to enroll now 3. Introduction to data science Double click to enroll now

Free Courses in Machine Learning

Free Courses in Machine Learning Analytics Vidhya 1. Introduction to AI & ML Double click to enroll now 2. Pandas for Data Analysis in Python Double click to enroll now 3. Fundamentals of Regression Analysis Double click to enroll now 4. K-Nearest Neighbors (KNN) Algorithm in Python and R Double click to enroll now 5. Naive Bayes from Scratch Double click to enroll now

IBM Machine Learning with Python | Free Course | Cognitive Class

IBM Machine Learning with Python | Free Course | Cognitive Class Earn IBM Machine Learning with Python Badge Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning. Why earn this badge? The badge earner has demonstrated an understanding of Supervised vs. Unsupervised Learning, how Statistical Modelling relates to Machine Learning, and how to build and evaluate machine learning models. Course Syllabus Module 1 – Introduction to Machine Learning Applications of Machine Learning Supervised vs Unsupervised Learning Python libraries suitable for Machine Learning Module 2 – Regression Linear Regression Non-linear Regression Model evaluation methods Module 3 – Classification K-Nearest Neighbour Decision Trees Logistic Regression Support Vector Machines Model Evaluation Module 4 – Unsupervised Le

Pandas Practice Code (Set - 1)

2 /6 / 2 2 , 1 1 :4 2 A M P a n d a s P r a ctice Co d e - S e t 1 - Ju p y te r No te b o o k loca lh o st: 8 8 8 8 /n o t e b o o k s/P a n d a s P ract ice Cod e - S e t 1 . i p y n b 1 /4 In [1 ] :  In [3 ] :  Se r ie s ([ ] , d ty p e: fl o at 6 4) <i p yt h on - in p ut - 1- c 46 5 11 a d4 f ca > :1 2 : D ep r e ca t io n Wa r ni n g: Th e d e fa u lt dt y pe f or em p ty Se r ie s w i ll be 'o b je c t' in s te a d o f ' f lo a t6 4 ' i n a f u tu r e v er s io n . Sp e ci f y a d t yp e e x pl i ci t ly to si l en c e t h is wa r ni n g. m y _s e ri e s = p d .S e ri e s( ) 0 1 0 1 2 0 2 3 0 3 4 0 4 5 0 dt y pe : i n t3 2 # A P a nd a s s er i es is a d at a s t ru c tu r e t h at st o re s d a ta in th e f o rm # o f a c o lu m n. A s er i es is no r ma l ly us e d t o s t or e i n fo r ma t io n # a bo u t a p a rt i cu l ar at t ri b ut e i n y o ur d at a

Numpy tutorials Part 2

Click here for part 1 👇 Numpy tutorials part 1 Numpy tutorials part 2 mean, variance and standard deviation 15. mean >>>import numpy >>>my_array = numpy.array([ [1, 2], [3, 4] ]) >>>print numpy.mean(my_array, axis = 0) #Output : [ 2. 3.] >>>print numpy.mean(my_array, axis = 1) #Output : [ 1.5 3.5] >>>print numpy.mean(my_array, axis = None) #Output : 2.5 >>>print numpy.mean(my_array) #Output : 2.5 16. var >>>import numpy >>>my_array = numpy.array([ [1, 2], [3, 4] ]) >>>print numpy.var(my_array, axis = 0) #Output : [ 1. 1.] >>>print numpy.var(my_array, axis = 1) #Output : [ 0.25 0.25] >>>print numpy.var(my_array, axis = None) #Output : 1.25 >>>print numpy.var(my_array) #Output : 1.25 17. std >>>import numpy >>>my_array = numpy.array([ [1, 2], [3, 4] ]) >>>print numpy.std(my_ar

Numpy tutorials - Part 1

1. Array to Numpy import numpy a = numpy.array([1,2,3,4,5]) print(a[1]) output: 2 2.Using shape to change array dimensions import numpy change_array = numpy.array([1,2,3,4,5,6]) change_array.shape = (3, 2) print change_array #Output [[1 2] [3 4] [5 6]] 3.reshape import numpy my_array = numpy.array([1,2,3,4,5,6]) print numpy.reshape(my_array,(3,2)) #Output [[1 2] [3 4] [5 6]] 4. Transpose import numpy my_array = numpy.array([[1,2,3], [4,5,6]]) print numpy.transpose(my_array) #Output [[1 4] [2 5] [3 6]] 5. Flatten import numpy my_array = numpy.array([[1,2,3], [4,5,6]]) print my_array.flatten() #Output [1 2 3 4 5 6] 6. Concatenation import numpy array_1 = numpy.array([1,2,3]) array_2 = numpy.array([4,5,6]) array_3 = numpy.array([7,8,9]) print numpy.concatenate((array_1, array_2, array_3)) #Output [1 2 3 4 5 6 7 8 9] import numpy array_1 = numpy.array([[1,2,3],[0,0,0]]) array_2 = numpy.array([[0,0,0],[7,8,9]]) print numpy.concate

Powered by Blogger

Kerala state government jobs

Comments

Post a Comment