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Data Science Prerequisites - NumPy, Matplotlib, and Pandas in Python
Data Science Prerequisites - NumPy, Matplotlib, and Pandas in Python

Data Science Prerequisites - NumPy, Matplotlib, and Pandas in Python: Get ready for AI, ML, and DL with NumPy, SciPy, Pandas, and Matplotlib stack

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€18.99 per month
Video Feb 2023 4hrs 21mins 1st Edition
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€82.99
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Profile Icon Lazy Programmer
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€18.99 per month
Video Feb 2023 4hrs 21mins 1st Edition
Video
€82.99
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Free Trial
Renews at €18.99p/m
Video
€82.99
Subscription
Free Trial
Renews at €18.99p/m

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Key benefits

  • Study basics of machine learning and understand how to use the NumPy stack for deep learning in data science
  • Learn how to use NumPy, Matplotlib, Pandas, and SciPy for critical tasks in data science and machine learning
  • Perform numerical computations, visualize data, load, and manipulate datasets using Pandas

Description

Welcome to the course where you will learn about the NumPy stack in Python, which is an important prerequisite for deep learning, machine learning, and data science. In this self-paced course, you will learn how to use NumPy, Matplotlib, Pandas, and SciPy to perform critical tasks related to data science and machine learning. This involves performing numerical computation and representing data, visualizing data with plots, loading in, and manipulating data using DataFrames, performing statistics and probability, and building machine learning models for classification and regression. In this course, we will first start with NumPy; we will understand the benefits of NumPy array and then we will look at some complicated matrix operations, such as products, inverses, determinants, and solving linear systems. Then we will cover Matplotlib. In this section, we will go over some common plots, namely the line chart, scatter plot, and histogram. We will also look at how to show images using Matplotlib. Next, we will talk about Pandas. We will look at how much easier it is to load a dataset using Pandas versus trying to do it manually. Then we will look at some data frame operations useful in machine learning, such as filtering by column, filtering by row, and the apply function. Later, you will learn about SciPy. In this section, you will learn how to do common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing. Finally, we will also cover some basics of machine learning that will help us start our deep learning journey. By the end of the course, we will be able to confidently use the NumPy stack in deep learning and data science.

Who is this book for?

This course is designed for anyone who is interested in data science and machine learning, who knows Python and wants to take the next step into Python libraries for data science, or who is interested in acquiring tools to implement machine learning algorithms. One must have decent Python programming skills and a basic understanding of linear algebra and probability for this course.

What you will learn

  • Understand supervised machine learning with real-world examples
  • Understand and code using the NumPy stack
  • Make use of NumPy, SciPy, Matplotlib, and Pandas to implement numerical algorithms
  • Understand the pros and cons of various machine learning models
  • Get a brief introduction to the classification and regression
  • Learn how to calculate the PDF and CDF under the normal distribution

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Feb 24, 2023
Length: 4hrs 21mins
Edition : 1st
Language : English
ISBN-13 : 9781803241616
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Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
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Product Details

Publication date : Feb 24, 2023
Length: 4hrs 21mins
Edition : 1st
Language : English
ISBN-13 : 9781803241616
Category :
Languages :
Concepts :
Tools :

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Table of Contents

6 Chapters
Welcome and Logistics Chevron down icon Chevron up icon
NumPy Chevron down icon Chevron up icon
Matplotlib Chevron down icon Chevron up icon
Pandas Chevron down icon Chevron up icon
SciPy Chevron down icon Chevron up icon
Machine Learning Basics Chevron down icon Chevron up icon
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