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