Statistics And Regression Course for Machine Learning In Python
By the end of this course, you’ll gain a solid foundation in machine learning and statistical regression using Python
Updated on Oct, 2025
Language - English
Development ,Data Science,Python
Lectures -63
Duration -5 hours
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Course 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 course 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.
Goals
- 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.
Prerequisites
- Basic computer knowledge and an interest in learning mathematics for machine learning are the only prerequisites for this course.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to the Course
1 Lectures
-
Course Introduction and Table of Contents
10:16
10:16
Environment Setup – Preparing your Computer
2 Lectures
Essential Components Included in Anaconda
1 Lectures
Python Basics - Assignment
1 Lectures
Python Basics - Flow Control
2 Lectures
Python Basics - List and Tuples
1 Lectures
Python Basics - Dictionary and Functions
2 Lectures
NumPy Basics
2 Lectures
Matplotlib Basics
2 Lectures
Basics of Data for Machine Learning
1 Lectures
Central Data Tendency - Mean
1 Lectures
Central Data Tendency - Median and Mode
2 Lectures
Variance and Standard Deviation Manual Calculation
2 Lectures
Variance and Standard Deviation using Python
1 Lectures
Percentile Manual Calculation
1 Lectures
Percentile using Python
1 Lectures
Uniform Distribution
1 Lectures
Normal Distribution
2 Lectures
Manual Z-Score calculation
1 Lectures
Z-Score calculation using Python
1 Lectures
Multi Variable Dataset Scatter Plot
1 Lectures
Introduction to Linear Regression
1 Lectures
Manually Finding Linear Regression Correlation Coefficient
2 Lectures
Manually Finding Linear Regression Slope Equation
2 Lectures
Manually Predicting the Future Value Using Equation
1 Lectures
Linear Regression Using Python Introduction
1 Lectures
Linear Regression Using Python
2 Lectures
Strong and Weak Linear Regression
1 Lectures
Predicting Future Value Using Linear Regression in Python
1 Lectures
Polynomial Regression Introduction
1 Lectures
Polynomial Regression Visualization
1 Lectures
Polynomial Regression Prediction and R2 Value
1 Lectures
Polynomial Regression Finding SD Components
1 Lectures
Polynomial Regression Manual Method Equations
1 Lectures
Finding SD Components for abc
1 Lectures
Finding abc
1 Lectures
Polynomial Regression Equation and Prediction
1 Lectures
Polynomial Regression coefficient
1 Lectures
Multiple Regression Introduction
1 Lectures
Multiple Regression Using Python - Data Import as CSV
1 Lectures
Multiple Regression Using Python - Data Visualization
1 Lectures
Creating Multiple Regression Object and Prediction Using Python
1 Lectures
Manual Multiple Regression - Intro and Finding Means
1 Lectures
Manual Multiple Regression - Finding Components
2 Lectures
Manual Multiple Regression - Finding abc
1 Lectures
Manual Multiple Regression Equation Prediction and Coefficients
1 Lectures
Feature Scaling Introduction
1 Lectures
Standardization Scaling Using Python
2 Lectures
Standardization Scaling Using Manual Calculation
2 Lectures
Instructor Details
Packt Publishing
Packt are an established, trusted, and innovative global technical learning publisher, founded in Birmingham, UK with over eighteen years experience delivering rich premium content from ground-breaking authors and lecturers on a wide range of emerging and established technologies for professional development.
Packt’s purpose is to help technology professionals advance their knowledge and support the growth of new technologies by publishing vital user focused knowledge-based content faster than any other tech publisher, with a growing library of over 9,000 titles, in book, e-book, audio and video learning formats, our multimedia content is valued as a vital learning tool and offers exceptional support for the development of technology knowledge.
We publish on topics that are at the very cutting edge of technology, helping IT professionals learn about the newest tools and frameworks in a way that suits them.
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