Dive into a comprehensive machine learning tutorial that covers the fundamentals using both Python and R programming languages. Learn to set up the necessary software, understand models, and apply them in practical scenarios. Explore key concepts like linear regression, data types, polynomial regression, and working with vectors and matrices. Engage in hands-on exercises and projects, including price prediction for used cars and analyzing diamond data. Master essential tools and libraries such as Jupyter, R Studio, and NumPy. Gain insights into train-test splits, R-squared calculations, and model comparison techniques. Perfect for beginners aiming to enhance their programming skills and build a strong foundation in machine learning.
Overview
Syllabus
Intro with my face in it
WHY Machine Learning?
Python or R - which one to choose?
Installing the required tools
Crash Course - our jupyter environment
Installing R and R Studio
Crash Course R and R Studio
Intro Vectors
Intro Data Tables
What is a model?
Problems where machine learning is used
Intuition - Linear Regression
More in Linear Regression
How to support the channel and get even more sweet Machine Learning knowledge
Python import data and draw graphic
Python Linear Regression Part 1
Python Linear Regression Part 2
R Linear Regression Part 1
R Linear Regression Part 2
R Linear Regression Part 3
R Linear Regression Part 4
Excursus - Why Quadratic?
Intro Project - Used Cars Price Prediction
Python sample project
R Sample Solution Used Cars
Train and Test Data Intro
Python Train Test Part 1
Python Train Test Part 2
Python Train Test Challenge
R Train Test Part 1
R Train Test Part 2
R Train Test Challenge
Intuition Linear Regression multiple variables part 1
Intuition Linear Regression multiple variables part 2
Python Linear Regression multiple variables part 1
Python Linear Regression multiple variables part 2
R Linear Regression multiple variables
R squared part 1
R squared part 2
Python R2 calculation
Python Compare Models via R2
R - R2 Calculation
R - Compare models with R2
Intro Project R2
Python Project R2 calculation
R Project calculate R2
Data Types Part 1
Numerica and Nominal data
Ordinal Data
Python working with nominal data
Python linear regression with nominal data
R Nominal Data and linear regression
Why can we get rid of a column?
Polynomial Regression Part 1
Polynomial Regression Part 2
Python Polynomial Regression Part 1
Python Polynomial Regression Part 2
R Polynomial Regression Part 1
R Polynomial Regression Part 2
Practice Project Diamond Data
Python Sample Solution Diamonds
R Sample Solution Diamonds
R Vectors and Matrices
R - Access Elements of Vectors
R - Naming Elements
R - Matrices
R - Naming Matrices
R - Datatables
Vectorizing Calculations
Why Numpy
Numpy Arrays
Numpy Arrays application
Numpy Matrices
NP Where Function
Taught by
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