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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

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Profile Icon Abhilash Nelson
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€18.99 per month
Video Oct 2021 5hrs 5mins 1st Edition
Video
€37.99
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Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Abhilash Nelson
Arrow right icon
€18.99 per month
Video Oct 2021 5hrs 5mins 1st Edition
Video
€37.99
Subscription
Free Trial
Renews at €18.99p/m
Video
€37.99
Subscription
Free Trial
Renews at €18.99p/m

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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

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

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

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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
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