Basic Statistics and Regression for Machine Learning in Python
Get ready to learn the basics of machine learning and the mathematics of statistical regression, which powers almost all machine learning algorithms.
Python,Machine Learning,Data Science and AI ML
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
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.
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 to learn mathematics for machine learning is the only prerequisite for this course.
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Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to the Course
1 Lectures
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Course Introduction and Table of Contents 10:16 10:16
Environment Setup – Preparing your Computer
2 Lectures
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Essential Components Included in Anaconda
1 Lectures
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Python Basics - Assignment
1 Lectures
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Python Basics - Flow Control
2 Lectures
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Python Basics - List and Tuples
1 Lectures
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Python Basics - Dictionary and Functions
2 Lectures
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NumPy Basics
2 Lectures
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Matplotlib Basics
2 Lectures
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Basics of Data for Machine Learning
1 Lectures
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Central Data Tendency - Mean
1 Lectures
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Central Data Tendency - Median and Mode
2 Lectures
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Variance and Standard Deviation Manual Calculation
2 Lectures
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Variance and Standard Deviation using Python
1 Lectures
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Percentile Manual Calculation
1 Lectures
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Percentile using Python
1 Lectures
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Uniform Distribution
1 Lectures
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Normal Distribution
2 Lectures
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Manual Z-Score calculation
1 Lectures
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Z-Score calculation using Python
1 Lectures
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Multi Variable Dataset Scatter Plot
1 Lectures
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Introduction to Linear Regression
1 Lectures
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Manually Finding Linear Regression Correlation Coefficient
2 Lectures
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Manually Finding Linear Regression Slope Equation
2 Lectures
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Manually Predicting the Future Value Using Equation
1 Lectures
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Linear Regression Using Python Introduction
1 Lectures
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Linear Regression Using Python
2 Lectures
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Strong and Weak Linear Regression
1 Lectures
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Predicting Future Value Using Linear Regression in Python
1 Lectures
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Polynomial Regression Introduction
1 Lectures
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Polynomial Regression Visualization
1 Lectures
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Polynomial Regression Prediction and R2 Value
1 Lectures
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Polynomial Regression Finding SD Components
1 Lectures
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Polynomial Regression Manual Method Equations
1 Lectures
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Finding SD Components for abc
1 Lectures
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Finding abc
1 Lectures
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Polynomial Regression Equation and Prediction
1 Lectures
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Polynomial Regression coefficient
1 Lectures
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Multiple Regression Introduction
1 Lectures
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Multiple Regression Using Python - Data Import as CSV
1 Lectures
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Multiple Regression Using Python - Data Visualization
1 Lectures
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Creating Multiple Regression Object and Prediction Using Python
1 Lectures
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Manual Multiple Regression - Intro and Finding Means
1 Lectures
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Manual Multiple Regression - Finding Components
2 Lectures
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Manual Multiple Regression - Finding abc
1 Lectures
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Manual Multiple Regression Equation Prediction and Coefficients
1 Lectures
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Feature Scaling Introduction
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Standardization Scaling Using Python
2 Lectures
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Standardization Scaling Using Manual Calculation
2 Lectures
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Instructor Details
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Packt Publishing
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