Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Basic Statistics and Regression for Machine Learning in Python
Basic Statistics and Regression for Machine Learning in Python

Basic Statistics and Regression for Machine Learning in Python: A quick and easy guide on statistical regression for machine learning

Arrow left icon
Profile Icon Abhilash Nelson
Arrow right icon
$19.99 per month
Video Oct 2021 5hrs 5mins 1st Edition
Video
$49.99
Subscription
Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Abhilash Nelson
Arrow right icon
$19.99 per month
Video Oct 2021 5hrs 5mins 1st Edition
Video
$49.99
Subscription
Free Trial
Renews at $19.99p/m
Video
$49.99
Subscription
Free Trial
Renews at $19.99p/m

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
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.
Subscribe now
View plans & pricing

Key benefits

  • A comprehensive course that includes Python coding, visualization, loops, variables, and functions
  • Manual calculation and then using Python functions/codes to understand the difference
  • Beginner to advanced mathematics and statistical concepts that cover machine learning algorithms

Description

This course is for ML enthusiasts who want to understand basic statistics and regression for machine learning. The course starts with setting up the environment and understanding the basics of Python language and different libraries. Next, you’ll see the basics of machine learning and different types of data. After that, you’ll learn a statistics technique called Central Tendency Analysis. Post this, you’ll focus on statistical techniques such as variance and standard deviation. Several techniques and mathematical concepts such as percentile, normal distribution, uniform distribution, finding z-score, linear regression, polynomial linear regression, and multiple regression with the help of manual calculation and Python functions are introduced as the course progresses. The dataset will get more complex as you proceed ahead; you’ll use a CSV file to save the dataset. You’ll see the traditional and complex method of finding the coefficient of regression and then explore ways to solve it easily with some Python functions. Finally, you’ll learn a technique called data normalization or standardization, which will improve the performance of the algorithms very much compared to a non-scaled dataset. By the end of this course, you’ll gain a solid foundation in machine learning and statistical regression using Python. All the code files and related files are available on the GitHub repository at https://github.com/PacktPublishing/Basic-Statistics-and-Regression-for-Machine-Learning-in-Python

Who is this book for?

This course is for beginners and individuals who want to learn mathematics for machine learning. You need not have any prior experience or knowledge in coding; just be ready with your learning mindset at the highest level. Individuals interested in learning what’s actually happening behind the scenes of Python functions and algorithms (at least in a shallow layman’s way) will be highly benefitted. Basic computer knowledge and an interest to learn mathematics for machine learning is the only prerequisite for this course.

What you will learn

  • Set up the environment
  • Learn central tendency analysis
  • Learn statistical models and analysis
  • Learn regression models and analysis
  • Use NumPy, matplotlib, and scikit-learn libraries
  • Learn the data normalization or standardization technique

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 26, 2021
Length: 5hrs 5mins
Edition : 1st
Language : English
ISBN-13 : 9781803238487
Category :
Languages :
Tools :

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
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.
Subscribe now
View plans & pricing

Product Details

Publication date : Oct 26, 2021
Length: 5hrs 5mins
Edition : 1st
Language : English
ISBN-13 : 9781803238487
Category :
Languages :
Tools :

Packt Subscriptions

See our plans and pricing
Modal Close icon
$19.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
$199.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts
$279.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total $ 170.97
Basic Statistics and Regression for Machine Learning in Python
$49.99
Machine Learning for Time-Series with Python
$54.99
Practical Discrete Mathematics
$65.99
Total $ 170.97 Stars icon
Banner background image

Table of Contents

49 Chapters
Introduction to the Course Chevron down icon Chevron up icon
Environment Setup – Preparing your Computer Chevron down icon Chevron up icon
Essential Components Included in Anaconda Chevron down icon Chevron up icon
Python Basics - Assignment Chevron down icon Chevron up icon
Python Basics - Flow Control Chevron down icon Chevron up icon
Python Basics - List and Tuples Chevron down icon Chevron up icon
Python Basics - Dictionary and Functions Chevron down icon Chevron up icon
NumPy Basics Chevron down icon Chevron up icon
Matplotlib Basics Chevron down icon Chevron up icon
Basics of Data for Machine Learning Chevron down icon Chevron up icon
Central Data Tendency - Mean Chevron down icon Chevron up icon
Central Data Tendency - Median and Mode Chevron down icon Chevron up icon
Variance and Standard Deviation Manual Calculation Chevron down icon Chevron up icon
Variance and Standard Deviation using Python Chevron down icon Chevron up icon
Percentile Manual Calculation Chevron down icon Chevron up icon
Percentile using Python Chevron down icon Chevron up icon
Uniform Distribution Chevron down icon Chevron up icon
Normal Distribution Chevron down icon Chevron up icon
Manual Z-Score calculation Chevron down icon Chevron up icon
Z-Score calculation using Python Chevron down icon Chevron up icon
Multi Variable Dataset Scatter Plot Chevron down icon Chevron up icon
Introduction to Linear Regression Chevron down icon Chevron up icon
Manually Finding Linear Regression Correlation Coefficient Chevron down icon Chevron up icon
Manually Finding Linear Regression Slope Equation Chevron down icon Chevron up icon
Manually Predicting the Future Value Using Equation Chevron down icon Chevron up icon
Linear Regression Using Python Introduction Chevron down icon Chevron up icon
Linear Regression Using Python Chevron down icon Chevron up icon
Strong and Weak Linear Regression Chevron down icon Chevron up icon
Predicting Future Value Using Linear Regression in Python Chevron down icon Chevron up icon
Polynomial Regression Introduction Chevron down icon Chevron up icon
Polynomial Regression Visualization Chevron down icon Chevron up icon
Polynomial Regression Prediction and R2 Value Chevron down icon Chevron up icon
Polynomial Regression Finding SD Components Chevron down icon Chevron up icon
Polynomial Regression Manual Method Equations Chevron down icon Chevron up icon
Finding SD Components for abc Chevron down icon Chevron up icon
Finding abc Chevron down icon Chevron up icon
Polynomial Regression Equation and Prediction Chevron down icon Chevron up icon
Polynomial Regression coefficient Chevron down icon Chevron up icon
Multiple Regression Introduction Chevron down icon Chevron up icon
Multiple Regression Using Python - Data Import as CSV Chevron down icon Chevron up icon
Multiple Regression Using Python - Data Visualization Chevron down icon Chevron up icon
Creating Multiple Regression Object and Prediction Using Python Chevron down icon Chevron up icon
Manual Multiple Regression - Intro and Finding Means Chevron down icon Chevron up icon
Manual Multiple Regression - Finding Components Chevron down icon Chevron up icon
Manual Multiple Regression - Finding abc Chevron down icon Chevron up icon
Manual Multiple Regression Equation Prediction and Coefficients Chevron down icon Chevron up icon
Feature Scaling Introduction Chevron down icon Chevron up icon
Standardization Scaling Using Python Chevron down icon Chevron up icon
Standardization Scaling Using Manual Calculation Chevron down icon Chevron up icon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is included in a Packt subscription? Chevron down icon Chevron up icon

A subscription provides you with full access to view all Packt and licnesed content online, this includes exclusive access to Early Access titles. Depending on the tier chosen you can also earn credits and discounts to use for owning content

How can I cancel my subscription? Chevron down icon Chevron up icon

To cancel your subscription with us simply go to the account page - found in the top right of the page or at https://subscription.packtpub.com/my-account/subscription - From here you will see the ‘cancel subscription’ button in the grey box with your subscription information in.

What are credits? Chevron down icon Chevron up icon

Credits can be earned from reading 40 section of any title within the payment cycle - a month starting from the day of subscription payment. You also earn a Credit every month if you subscribe to our annual or 18 month plans. Credits can be used to buy books DRM free, the same way that you would pay for a book. Your credits can be found in the subscription homepage - subscription.packtpub.com - clicking on ‘the my’ library dropdown and selecting ‘credits’.

What happens if an Early Access Course is cancelled? Chevron down icon Chevron up icon

Projects are rarely cancelled, but sometimes it's unavoidable. If an Early Access course is cancelled or excessively delayed, you can exchange your purchase for another course. For further details, please contact us here.

Where can I send feedback about an Early Access title? Chevron down icon Chevron up icon

If you have any feedback about the product you're reading, or Early Access in general, then please fill out a contact form here and we'll make sure the feedback gets to the right team. 

Can I download the code files for Early Access titles? Chevron down icon Chevron up icon

We try to ensure that all books in Early Access have code available to use, download, and fork on GitHub. This helps us be more agile in the development of the book, and helps keep the often changing code base of new versions and new technologies as up to date as possible. Unfortunately, however, there will be rare cases when it is not possible for us to have downloadable code samples available until publication.

When we publish the book, the code files will also be available to download from the Packt website.

How accurate is the publication date? Chevron down icon Chevron up icon

The publication date is as accurate as we can be at any point in the project. Unfortunately, delays can happen. Often those delays are out of our control, such as changes to the technology code base or delays in the tech release. We do our best to give you an accurate estimate of the publication date at any given time, and as more chapters are delivered, the more accurate the delivery date will become.

How will I know when new chapters are ready? Chevron down icon Chevron up icon

We'll let you know every time there has been an update to a course that you've bought in Early Access. You'll get an email to let you know there has been a new chapter, or a change to a previous chapter. The new chapters are automatically added to your account, so you can also check back there any time you're ready and download or read them online.

I am a Packt subscriber, do I get Early Access? Chevron down icon Chevron up icon

Yes, all Early Access content is fully available through your subscription. You will need to have a paid for or active trial subscription in order to access all titles.

How is Early Access delivered? Chevron down icon Chevron up icon

Early Access is currently only available as a PDF or through our online reader. As we make changes or add new chapters, the files in your Packt account will be updated so you can download them again or view them online immediately.

How do I buy Early Access content? Chevron down icon Chevron up icon

Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.

What is Early Access? Chevron down icon Chevron up icon

Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.