Master Python With NumPy For Data Science Machine Learning

Master Python With NumPy For Data Science & Machine Learning, available at Free, has an average rating of 3.5, with 17 lectures, based on 675 reviews, and has 97538 subscribers.

You will learn about NumPy For Data Analysis NumPy For Data Science Numerical Computation Using Python How To Work With Nd-arrays How To Perform Matrix Computation This course is ideal for individuals who are Data Analyst Beginners or Business Analyst and AI Enthusiasts or Python Developers Beginners or Who Is Interested In ML, AI and Other Big Data Engineering It is particularly useful for Data Analyst Beginners or Business Analyst and AI Enthusiasts or Python Developers Beginners or Who Is Interested In ML, AI and Other Big Data Engineering.

Enroll now: Master Python With NumPy For Data Science & Machine Learning

Summary

Title: Master Python With NumPy For Data Science & Machine Learning

Price: Free

Average Rating: 3.5

Number of Lectures: 17

Number of Published Lectures: 17

Number of Curriculum Items: 17

Number of Published Curriculum Objects: 17

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

NumPy For Data Analysis

NumPy For Data Science

Numerical Computation Using Python

How To Work With Nd-arrays

How To Perform Matrix Computation

Who Should Attend

Data Analyst Beginners

Business Analyst and AI Enthusiasts

Python Developers Beginners

Who Is Interested In ML, AI and Other Big Data Engineering

Target Audiences

Data Analyst Beginners

Business Analyst and AI Enthusiasts

Python Developers Beginners

Who Is Interested In ML, AI and Other Big Data Engineering

Hi, welcome to the ‘NumPy For Data Science & Machine Learning’ course. This forms the basis for everything else. The central object in Numpy is the Numpy array, on which you can do various operations. We know that the matrix and arrays play an important role in numerical computation and data analysis. Pandas and other ML or AI tools need tabular or array-like data to work efficiently, so using NumPy in Pandas and ML packages can reduce the time and improve the performance of the data computation. NumPy based arrays are 10 to 100 times (even more than 100 times) faster than the Python Lists, hence if you are planning to work as a Data Analyst or Data Scientist or Big Data Engineer with Python, then you must be familiar with the NumPy as it offers a more convenient way to work with Matrix-like objects like Nd-arrays. And also we’re going to do a demo where we prove that using a Numpy vectorized operation is faster than normal Python lists.

So if you want to learn about the fastest python-based numerical multidimensional data processing framework, which is the foundation for many data science packages like pandas for data analysis, sklearn, scikit-learn for the machine learning algorithm, you are at the right place and right track. The course contents are listed in the “Course content” section of the course, please go through it.

I wish you all the very best and good luck with your future endeavors. Looking forward to seeing you inside the course.

Towards your success:

Pruthviraja L

Course Curriculum

Chapter 1: Introduction – Installation and Setup

Lecture 1: What Is NumPy

Lecture 2: How To Install And Setup NumPy & Pandas

Lecture 3: How To Work With The Jupyter Notebook

Chapter 2: NumPy Basics

Lecture 1: Numpy Initialization

Lecture 2: Creating An Ndarrays

Lecture 3: Data Types

Lecture 4: Pseudorandom Number Generation

Chapter 3: Indexing In NumPy

Lecture 1: Indexing And Slicing

Lecture 2: Boolean Indexing

Lecture 3: Fancy Indexing

Chapter 4: File Handling In NumPy

Lecture 1: How To Save And Load In Numpy

Chapter 5: Numerical Computation in NumPy

Lecture 1: Mathematical & Statistical Methods

Lecture 2: Arithmetic Operations

Lecture 3: Universal Functions In Numpy

Lecture 4: Conditional Logics In Numpy

Lecture 5: Sorting In Numpy

Chapter 6: Thank You and Bonus Section

Lecture 1: Congratulations!

Instructors

Pruthviraja L
Software Trainer and Lead Instructor – Team UpGraduate

Rating Distribution

1 stars: 43 votes

2 stars: 45 votes

3 stars: 161 votes

4 stars: 201 votes

5 stars: 225 votes

Frequently Asked Questions

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