## Video 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.

#### 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

#### Audience

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.

#### About The Author

**Abhilash Nelson:** Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than 8 years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.

## Publisher resources

## Table of contents

- Chapter 1 : Introduction to the Course
- Chapter 2 : Environment Setup – Preparing your Computer
- Chapter 3 : Essential Components Included in Anaconda
- Chapter 4 : Python Basics - Assignment
- Chapter 5 : Python Basics - Flow Control
- Chapter 6 : Python Basics - List and Tuples
- Chapter 7 : Python Basics - Dictionary and Functions
- Chapter 8 : NumPy Basics
- Chapter 9 : Matplotlib Basics
- Chapter 10 : Basics of Data for Machine Learning
- Chapter 11 : Central Data Tendency - Mean
- Chapter 12 : Central Data Tendency - Median and Mode
- Chapter 13 : Variance and Standard Deviation Manual Calculation
- Chapter 14 : Variance and Standard Deviation using Python
- Chapter 15 : Percentile Manual Calculation
- Chapter 16 : Percentile using Python
- Chapter 17 : Uniform Distribution
- Chapter 18 : Normal Distribution
- Chapter 19 : Manual Z-Score calculation
- Chapter 20 : Z-Score calculation using Python
- Chapter 21 : Multi Variable Dataset Scatter Plot
- Chapter 22 : Introduction to Linear Regression
- Chapter 23 : Manually Finding Linear Regression Correlation Coefficient
- Chapter 24 : Manually Finding Linear Regression Slope Equation
- Chapter 25 : Manually Predicting the Future Value Using Equation
- Chapter 26 : Linear Regression Using Python Introduction
- Chapter 27 : Linear Regression Using Python
- Chapter 28 : Strong and Weak Linear Regression
- Chapter 29 : Predicting Future Value Using Linear Regression in Python
- Chapter 30 : Polynomial Regression Introduction
- Chapter 31 : Polynomial Regression Visualization
- Chapter 32 : Polynomial Regression Prediction and R2 Value
- Chapter 33 : Polynomial Regression Finding SD Components
- Chapter 34 : Polynomial Regression Manual Method Equations
- Chapter 35 : Finding SD Components for abc
- Chapter 36 : Finding abc
- Chapter 37 : Polynomial Regression Equation and Prediction
- Chapter 38 : Polynomial Regression coefficient
- Chapter 39 : Multiple Regression Introduction
- Chapter 40 : Multiple Regression Using Python - Data Import as CSV
- Chapter 41 : Multiple Regression Using Python - Data Visualization
- Chapter 42 : Creating Multiple Regression Object and Prediction Using Python
- Chapter 43 : Manual Multiple Regression - Intro and Finding Means
- Chapter 44 : Manual Multiple Regression - Finding Components
- Chapter 45 : Manual Multiple Regression - Finding abc
- Chapter 46 : Manual Multiple Regression Equation Prediction and Coefficients
- Chapter 47 : Feature Scaling Introduction
- Chapter 48 : Standardization Scaling Using Python
- Chapter 49 : Standardization Scaling Using Manual Calculation

## Product information

- Title: Basic Statistics and Regression for Machine Learning in Python
- Author(s):
- Release date: October 2021
- Publisher(s): Packt Publishing
- ISBN: 9781803238487

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