NumPy, Pandas and Matplotlib A-Z™ for Machine Learning

NumPy, Pandas and Matplotlib A-Z& for Machine Learning, available at $64.99, has an average rating of 4.15, with 447 lectures, based on 87 reviews, and has 562 subscribers.

You will learn about Go from absolute beginner to become a confident Python NumPy, Pandas and Matplotlib user Dare to get the most out of Python NumPy, Pandas and Matplotlib Go deeper to understand complex topics in Python NumPy, Pandas and data visualisation Learn Python NumPy, Pandas and Matplotlib through several exercises and solutions Acquire the required Python NumPy, Pandas and Matplotlib knowledge you need to excel in Data Science, Machine Learning, Ai and Deep Learning Be trained by expert This course is ideal for individuals who are All levels of students It is particularly useful for All levels of students.

Enroll now: NumPy, Pandas and Matplotlib A-Z& for Machine Learning

Summary

Title: NumPy, Pandas and Matplotlib A-Z& for Machine Learning

Price: $64.99

Average Rating: 4.15

Number of Lectures: 447

Number of Published Lectures: 447

Number of Curriculum Items: 447

Number of Published Curriculum Objects: 447

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Go from absolute beginner to become a confident Python NumPy, Pandas and Matplotlib user

Dare to get the most out of Python NumPy, Pandas and Matplotlib

Go deeper to understand complex topics in Python NumPy, Pandas and data visualisation

Learn Python NumPy, Pandas and Matplotlib through several exercises and solutions

Acquire the required Python NumPy, Pandas and Matplotlib knowledge you need to excel in Data Science, Machine Learning, Ai and Deep Learning

Be trained by expert

Who Should Attend

All levels of students

Target Audiences

All levels of students

Welcome to NumPy, Pandas and Matplotlib A-Z&: for Machine Learning

NumPy is a leading scientific computing library in Python while Pandas is for data manipulation and analysis. Also, learn to use Matplotlib for data visualization. Whether you are trying to go into Data Science, dive into machine learning, or deep learning, NumPy and Pandas are the top Modules in Python you should understand to make the journey smooth for you. In this course, we are going to start from the basics of Python NumPy and Pandas to the advanced NumPy and Pandas. This course will give you a solid understanding of NumPy, Pandas, and their functions.

At the end of the course, you should be able to write complex arrays for real-life projects, manipulate and analyze real-world data using Pandas.

WHO IS THIS COURSE FOR? 

√ This course is for you if you want to learn NumPy, Pandas, and Matplotlib for the first time or get a deeper knowledge of NumPy and Pandas to increase your productivity with deep and Machine learning.

√ This course is for you if you are coming from other programming languages and want to learn Python NumPy and Pandas fast and know it really well.

√ This course is for you if you are tired of NumPy,  Pandas, and Matplotlib courses that are too brief, too simple, or too complicated.

√ This course is for you if you want to build real-world applications using NumPy or Panda and visualize them with Matplotlib.

√ This course is for you if you have to get the prerequisite knowledge to understanding Data Science and Machine Learning using NumPy and Pandas.

√ This course is for you if you want to master the in-and-out of NumPy, Pandas, and data visualization.

√ This course is for you if you want to learn NumPy and Pandas by doing exciting real-life challenges that will distinguish you from the crowd.

√ This course is for you if plan to pass an interview soon.

Course Curriculum

Chapter 1: NumPy – Setups

Lecture 1: Course Syllabus Walkthrough

Lecture 2: Installing Jupiter Notebook

Lecture 3: Installing of NumPy

Lecture 4: Importing NumPy

Chapter 2: NumPy – Introduction

Lecture 1: What is NumPy

Lecture 2: What is Arrray

Lecture 3: Types of Array

Lecture 4: What is Dimension

Lecture 5: Exploring – Row Before Column – Why?

Lecture 6: Identifying an Array

Lecture 7: Scalar vs Vector vs Matrix vs Tensor

Chapter 3: NumPy – Creating Arrays

Lecture 1: First Time Creating an Array

Lecture 2: Creating an Array from a Tuple

Lecture 3: Creating a Zero Dimensional Array

Lecture 4: Avoiding Errors of Multiple Arguments

Lecture 5: Creating a 1-D Array

Lecture 6: Creating a 2-D Array

Lecture 7: Creating a 3-D Array

Chapter 4: NumPy – Data Type

Lecture 1: Understanding NumPy Data Type

Lecture 2: Forcing a Data Type of an Array

Chapter 5: NumPy – Challenges and Solution – Creating Arrays

Lecture 1: The Challenges

Lecture 2: The Challenges – text

Lecture 3: Solution to Challenge 1a

Lecture 4: Solution to Challenge 1b

Lecture 5: Solution to Challenge 1c

Lecture 6: Solution to Challenge 1d

Lecture 7: Solution to Challenge 1e

Lecture 8: Solution to Challenge 2a

Lecture 9: Solution to Challenge 2b

Lecture 10: Solution to Challenge 2c

Lecture 11: Solution to Challenge 2d

Lecture 12: Solution to Challenge 2e

Lecture 13: Solution to Challenge 2f

Chapter 6: NumPy – Creating Arrays – (Others)

Lecture 1: Array of Zeros

Lecture 2: Arrays of Ones

Lecture 3: Empty Arrays

Lecture 4: How to use arange()

Lecture 5: How to use linspace()

Lecture 6: How to use reshape()

Chapter 7: NumPy – Attributes of an Array

Lecture 1: How to find the attributes of an Array – (ndim, shape, size, dtype, itemsize)

Chapter 8: NumPy – Challenges and Solutions – Creating Arrays (More)

Lecture 1: The Challenges

Lecture 2: The Challenges – Text

Lecture 3: Solution to Challenge 1a

Lecture 4: Solution to Challenge 1b

Lecture 5: Solution to Challenge 1c

Lecture 6: Solution to Challenge 2a

Lecture 7: Solution to Challenge 2b

Lecture 8: Solution to Challenge 2c

Lecture 9: Solution to Challenge 2d

Lecture 10: Solution to Challenge 2e

Lecture 11: Solution to Challenge 2f

Lecture 12: Solution to Challenge #3

Lecture 13: Solution to Challenge #4

Chapter 9: NumPy – Array Sorting and Concatenation

Lecture 1: Array Sorting

Lecture 2: Array Concatenation

Chapter 10: NumPy – 1-D Array Indexing and Slicing

Lecture 1: Understanding how indexing and Slicing work on 1-D Arrays

Chapter 11: NumPy – Challenges and Solution – 1-D Array Indexing & Slicing

Lecture 1: The Challenges

Lecture 2: The Challenges – Text

Lecture 3: Solution to Challenge 1a

Lecture 4: Solution to Challenge 1b

Lecture 5: Solution to Challenge 1c

Lecture 6: Solution to Challenge 1d

Lecture 7: Solution to Challenge 1e

Lecture 8: Solution to Challenge 1f

Lecture 9: Solution to Challenge 1g

Lecture 10: Solution to Challenge 1h

Lecture 11: Solution to Challenge 1i

Lecture 12: Solution to Challenge 1j

Lecture 13: Solution to Challenge 1k

Lecture 14: Solution to Challenge 1l

Lecture 15: Solution to Challenge 1m

Chapter 12: NumPy – Creating an Array from Existing Array

Lecture 1: With Less Than, Greater Than or Equal To

Lecture 2: Even and Odd Numbers

Lecture 3: Two Conditions

Chapter 13: NumPy – Challenges and Solutions – Creating an Array from Existing Array

Lecture 1: The Challenges

Lecture 2: The Challenges – Text

Lecture 3: Solution to Challenge #1

Lecture 4: Solution to Challenge #2

Lecture 5: Solution to Challenge #3

Lecture 6: Solution to Challenge #4

Lecture 7: Solution to Challenge #5

Chapter 14: NumPy – 2-D Array Indexing and Slicing

Lecture 1: Selecting Elements of 2-D Array

Lecture 2: Slicing In 2-D Array

Chapter 15: NumPy – Challenges and Solution – 2-D Array Indexing & Slicing

Lecture 1: The Challenges

Lecture 2: The Challenges – Text

Instructors

Donatus Obomighie, PhD, MSc, PMP
Instructor & Engineer

Rating Distribution

1 stars: 3 votes

2 stars: 4 votes

3 stars: 15 votes

4 stars: 18 votes

5 stars: 47 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!