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Book Overview & Buying
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Table Of Contents
Basic Statistics and Regression for Machine Learning in Python
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Basic Statistics and Regression for Machine Learning in Python
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Overview of this book
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
Table of Contents (49 chapters)
Introduction to the Course
Environment Setup – Preparing your Computer
Essential Components Included in Anaconda
Python Basics - Assignment
Python Basics - Flow Control
Python Basics - List and Tuples
Python Basics - Dictionary and Functions
NumPy Basics
Matplotlib Basics
Basics of Data for Machine Learning
Central Data Tendency - Mean
Central Data Tendency - Median and Mode
Variance and Standard Deviation Manual Calculation
Variance and Standard Deviation using Python
Percentile Manual Calculation
Percentile using Python
Uniform Distribution
Normal Distribution
Manual Z-Score calculation
Z-Score calculation using Python
Multi Variable Dataset Scatter Plot
Introduction to Linear Regression
Manually Finding Linear Regression Correlation Coefficient
Manually Finding Linear Regression Slope Equation
Manually Predicting the Future Value Using Equation
Linear Regression Using Python Introduction
Linear Regression Using Python
Strong and Weak Linear Regression
Predicting Future Value Using Linear Regression in Python
Polynomial Regression Introduction
Polynomial Regression Visualization
Polynomial Regression Prediction and R2 Value
Polynomial Regression Finding SD Components
Polynomial Regression Manual Method Equations
Finding SD Components for abc
Finding abc
Polynomial Regression Equation and Prediction
Polynomial Regression coefficient
Multiple Regression Introduction
Multiple Regression Using Python - Data Import as CSV
Multiple Regression Using Python - Data Visualization
Creating Multiple Regression Object and Prediction Using Python
Manual Multiple Regression - Intro and Finding Means
Manual Multiple Regression - Finding Components
Manual Multiple Regression - Finding abc
Manual Multiple Regression Equation Prediction and Coefficients
Feature Scaling Introduction
Standardization Scaling Using Python
Standardization Scaling Using Manual Calculation
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