x
I T R i d g e

NumPy Training

  • Beginner & Advanced level Classes.
  • NumPy Programming With Practical Experience.
  • Presented by a 10+ Year of NumPy Certified Expert.
  • Affordable Fees and Curriculum Created by Industry Experts.
  • Over 12,402 Students Have Received Training, With Over 350 Potential Recruits.
  • The Upcoming NumPy Training Batch to Begin This Week –  Enroll Your Name Now!
This is How I.T. Ridge Students Prepare for Better Jobs
PLACED IMAGE I.T. Ridge
What is NumPy in Python?

NumPy is an open source library available in Python, which helps in mathematical, scientific, engineering, and data science programming. It is a very useful library to perform mathematical and statistical operations in Python. It works perfectly for multi-dimensional arrays and matrix multiplication. It is easy to integrate with C/C++ and Fortran. For any scientific project, NumPy is the tool to know. It has been built to work with the N-dimensional array, linear algebra, random number, Fourier transform, etc.

Syllabus of Numpy Training
Module 1: Introduction
  • Lesson 1 - Introduction to Numpy
  • Lesson 2 - Python NumPy
  • Lesson 3 - NumPy array in Python
  • Lesson 4 - Basics of NumPy Arrays
  • Lesson 5 - Python Lists VS Numpy Arrays
  • Lesson 6 - Numpy – ndarray
  • Lesson 7 - Data type Object (dtype) in NumPy Python
Module 2: Creating NumPy Array
  • Lesson 1 - Numpy – Array Creation
  • Lesson 2 - The Arange Method
  • Lesson 3 - The Zero Method
  • Lesson 4 - Create a Numpy array filled with all ones
  • Lesson 5 - The linspace Method
  • Lesson 6 - The eye Method
  • Lesson 7 - Numpy Meshgrid function
  • Lesson 8 - Creating a one-dimensional NumPy array
  • Lesson 9 - How to create an empty and a full NumPy array?
  • Lesson 10 - Create a Numpy array filled with all zeros
  • Lesson 11 - Create a Numpy array filled with all ones
  • Lesson 12 - How to generate 2-D Gaussian array using NumPy?
  • Lesson 13 - How to create a vector in Python using NumPy
  • Lesson 14 - Create the record array from list of individual records
Module 3: NumPy Array Manipulation
  • Lesson 1 - Copy and View in NumPy Array
  • Lesson 2 - How to Copy NumPy array into another array?
  • Lesson 3 - Appending values at the end of an NumPy array
  • Lesson 4 - How to swap columns of a given NumPy array?
  • Lesson 5 - Insert a new axis within a NumPy array
  • Lesson 6 - Stack the sequence of NumPy array horizontally
  • Lesson 7 - Stack the sequence of NumPy array vertically
  • Lesson 8 - Joining NumPy Array
  • Lesson 9 - Combining a one and a two-dimensional NumPy Array
  • Lesson 10 - Concatenate two arrays – np.ma.concatenate()
  • Lesson 11 - Combined array index by index
  • Lesson 12 - Splitting Arrays in NumPy
  • Lesson 13 - Compare two NumPy arrays
  • Lesson 14 - Find the union of two NumPy arrays
  • Lesson 15 - Find unique rows in a NumPy array
  • Lesson 16 - Get the unique values from an array
  • Lesson 17 - Trim the leading and/or trailing zeros from a 1-D array
Module 4: Matrix in NumPy
  • Lesson 1 - Matrix manipulation in Python
  • Lesson 2 - numpy matrix operations | empty() function
  • Lesson 3 - numpy matrix operations | zeros() function
  • Lesson 4 - numpy matrix operations | ones() function
  • Lesson 5 - numpy matrix operations | eye() function
  • Lesson 6 - numpy matrix operations | identity() function
  • Lesson 7 - Adding and Subtracting Matrices in Python
  • Lesson 8 - Matrix Multiplication in NumPy
  • Lesson 9 - Dot product of two arrays
  • Lesson 10 - NumPy | Vector Multiplication
  • Lesson 11 - How to calculate dot product of two vectors in Python?
  • Lesson 12 - Multiplication of two Matrices in Single line using Numpy in Python
  • Lesson 13 - Get the eigen values of a matrix
  • Lesson 14 - Calculate the determinant of a matrix using NumPy
  • Lesson 15 - Find the transpose of the matrix
  • Lesson 16 - Find the variance of a matrix
  • Lesson 17 - Compute the inverse of a matrix using NumPy
Module 5: Operations on NumPy Array
  • Lesson 1 - Numpy – Binary Operations
  • Lesson 2 - Numpy – Mathematical Function
  • Lesson 3 - Numpy – String Operations
Module 6: Reshaping NumPy Array
  • Lesson 1 - Reshape NumPy Array
  • Lesson 2 - Resize the shape of the given matrix
  • Lesson 3 - Reshape the shape of the given matrix
  • Lesson 4 - Get the Shape of NumPy Array
  • Lesson 5 - Change the dimension of a NumPy array
  • Lesson 6 - Change shape and size of array in-place
  • Lesson 7 - Flatten a Matrix in Python using NumPy
  • Lesson 8 - Flatten a matrix – matrix.ravel()
  • Lesson 9 - Move axes of an array to new positions
  • Lesson 10 - Interchange two axes of an array
  • Lesson 11 - Swap the axes a matrix
  • Lesson 12 - Split an array into multiple sub-arrays vertically
  • Lesson 13 - Split an array into multiple sub-arrays horizontally
  • Lesson 14 - Give a new shape to the masked array without changing its data
  • Lesson 15 - Squeeze the size of a matrix
Module 7: Indexing NumPy Array
  • Lesson 1 - Basic Slicing and Advanced Indexing in NumPy Python
  • Lesson 2 - Get selected slices of an array along mentioned axis
  • Lesson 3 - Accessing Data Along Multiple Dimensions Arrays in Python Numpy
  • Lesson 4 - How to access different rows of a multidimensional NumPy array?
  • Lesson 5 - Get the indices for the lower-triangle of an (n, m) array
Module 8: Arithmetic operations on NumPy Array
  • Lesson 1 - Broadcasting with NumPy Arrays
  • Lesson 2 - Estimation of Variable
  • Lesson 3 - Python: Operations on Numpy Arrays
  • Lesson 4 - How to use the NumPy sum function?
  • Lesson 5 - Divide the NumPy array element wise
  • Lesson 6 - Computes the inner product of two arrays
  • Lesson 7 - Absolute Deviation and Absolute Mean Deviation using NumPy
  • Lesson 8 - Find the standard deviation a matrix
  • Lesson 9 - Calculate the GCD of the NumPy array
Module 9: Linear Algebra in NumPy Array
  • Lesson 1 - Numpy | Linear Algebra
  • Lesson 2 - Get the QR factorization of a given NumPy array
  • Lesson 3 - How to get the magnitude of a vector in NumPy?
  • Lesson 4 - Compute the eigenvalues and right eigenvectors of a given square array using NumPy?
Module 10: NumPy and Random Data
  • Lesson 1 - Random sampling in numpy | ranf() function
  • Lesson 2 - Random sampling in numpy | random() function
  • Lesson 3 - Random sampling in numpy | random_sample() function
  • Lesson 4 - Random sampling in numpy | sample() function
  • Lesson 5 - Random sampling in numpy | random_integers() function
  • Lesson 6 - Random sampling in numpy | randint() function
  • Lesson 7 - Get random elements from NumPy – random.choice()
  • Lesson 8 - How to choose elements from the list with different probability using NumPy?
  • Lesson 9 - How to get weighted random choice in Python?
  • Lesson 10 - How to get the random positioning of different integer values?
  • Lesson 11 - Get Random Elements form geometric distribution
  • Lesson 12 - Get Random samples of a sequence of permutation
Module 11: Sorting and Searching in NumPy Array
  • Lesson 1 - Searching in a NumPy array
  • Lesson 2 - How to sort a Numpy Array
  • Lesson 3 - Numpy – Sorting, Searching and Counting
  • Lesson 4 - Variations in different Sorting techniques in Python
  • Lesson 5 - Sort a complex array
  • Lesson 6 - Get the minimum value of masked array
  • Lesson 7 - Sort the values in a matrix
  • Lesson 8 - Sort the elements in the given matrix having one or more dimension
Module 12: Universal Functions
  • Lesson 1 - Numpy ufunc | Universal functions
  • Lesson 2 - Create your own universal function in NumPy
Module 13: Working With Images
  • Lesson 1 - Create a white image using NumPy in Python
  • Lesson 2 - Convert a NumPy array to an image
  • Lesson 3 - How to Convert images to NumPy array?
  • Lesson 4 - Convert an image to NumPy array and save it to CSV file using Python?
Module 14: Projects and Applications with NumPy
  • Lesson 1 - Print checkerboard pattern of nxn using numpy
  • Lesson 2 - Implementation of neural network from scratch using NumPy
  • Lesson 3 - Analyzing selling price of used cars using Python
Module 14: Python Numpy Exercises
  • Lesson 1 - Python NumPy – Practice Exercises, Questions, and Solutions
  • Lesson 2 - Python MCQ (Multiple Choice Questions) with Answers
Upcoming Batches
26-Feb-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

Request Demo
21-Feb-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

Request Demo
24-Feb-2024
Sat,Sun

Weekend Regular

(10:00 AM - 01:30 PM)

(Class 3hr - 3:30Hrs) / Per Session

Request Demo
24-Feb-2024
Sat,Sun

Weekend Fasttrack

(09:00 AM - 02:00 PM)

(Class 4:30Hr - 5:00Hrs) / Per Session

Request Demo

Key Features

  • ➤ 40 Hours Course Duration
  • ➤ Industry Expert Faculties
  • ➤ 100% Job Oriented Training

.

  • ➤ Free Demo Class Available
  • ➤ Completed 500+ Batches
  • ➤ Certification Guidance

Summary NumPy

👉 Python is an open source library available in Python, which helps in mathematical, scientific, engineering, and data science programming.

👉 numpy.zeros() or np.zeros Python function is used to create a matrix full of zeroes.

👉 numpy.ones() in Python can be used when you initialize the weights during the first iteration in TensorFlow and other statistic tasks.

👉 Python NumPy Reshape function is used to shape an array without changing its data.

👉 Python NumPy Flatten function is used to return a copy of the array in one-dimension.

👉 Numpy.hstack is a function in Python that is used to horizontally stack sequences of input arrays in order to make a single array.

👉 Numpy.vstack is a function in Python which is used to vertically stack sequences of input arrays in order to make a single array.

👉 numpy.arange() is an inbuilt numpy function that returns an ndarray object containing evenly spaced values within a defined interval.

👉 Numpy.dot product is a powerful library for matrix computation.

👉 The Numpy matmul() function is used to return the matrix product of 2 arrays.

Get Certified By NumPy & Industry Recognized I.T. Ridge Certificate

All significant economic firms have accredited I.T. Ridge Credential. Following the completion of theoretical and practical workshops, we offer training either to freshmen and corporate trainees alike. The practice of testing application software produced for handheld mobile devices for functionality, usability, and consistency is known as mobile application testing. Tests of mobile applications might be automated or manual.

FAQ's

Call now: +1469-990-1010 and know the exciting offers available for you!

I.T. Ridge is the Legend in offering placement to the students. Please visit our Placed Students List on our website

We have strong relationship with over 700+ Top MNCs like SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc.

More than 3500+ students placed in last year in India & Globally

I.T. Ridge conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.

85% percent placement record

Our Placement Cell support you till you get placed in better MNC

I.T. Ridge is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (NIE) Singapore

No worries. I.T. Ridge assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.

All the instructors at I.T. Ridge are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by I.T. Ridge for providing an awesome learning experience.

↞ Go To Top