Machine learning is a subset of artificial intelligence or artificial intelligence. This tutorial
will teach you how to build a model that can learn and predict the kind of music people like.
By the end of this one hour tutorial you will have a good understanding of machine learning
basics. You do n't need any prior knowledge in machine learning but you need to know
python fairly well. Machine learning has other applications in self-driving cars and robotics
language processing vision processing forecasting things like stock market trends and the
weather games and so on so that 's the basic idea about machine learning next we 'll look at
machine learning in action a machine learning project involves a number of steps the first
step is to import our data which often comes in the form of a csv file. We 're going to look at
the popular python libraries that we use in machine learning projects. The first one is numpy
which provides a multidimensional array. pandas is a data analysis library that provides a
concept called data frame. matplotlib is a two-dimensional plotting library for creating graphs
and plots. scikit-learn is one of the most popular machine learning libraries that provides all
these common algorithms like neural networks and neural networks.
Anaconda is that it will install jupyter as well as all those popular data science libraries like
numpy pandas and so on so we do n't have to manually install this using pip all right now as
part of the next step anaconda suggests to install microsoft vs code we already have this on
our machine so we can go with continue and close the installation. In this notebook we can
write python code and execute it line by line. Kaggle. com is a popular data set that we 're
going to use in this lecture. Create an account you can sign up with facebook google or
using a custom email and password once you sign up then come back here in the search
bar search for video game sales this is the name of a data set called vgsales. csv. This is the
beauty of jupyter. We can easily visualise our data doing this with vs code and terminal
windows is really tedious and clunky so what is this describe method returning basically it 's
returning some basic information about each column in this data set. In a real data science
or machine learning project we 'll have to use some techniques to clean up our data set. One
option is to remove the records that do n't have a value for the year column.
The first element in our array is an array itself. These are the values in this array which
basically represent the first row in our data set, so the video game with ranking 1 is called wii
sports. If we press the escape key green turns to blue and that means this cell is currently in
the command mode. The activated cell can be either in the edit mode or command mode
depending on the mode. When you run a cell this will only execute the code in that cell. The
code in other cells will not be executed. This notebook file includes our source code
organised in cells as well as the output for each cell. We also have autocompletion and
intellisense so in the cell let 's call df dataframe dot. I just wanted to let you know that I have
an online coding school at cordwindmarch. com where you can find plenty of courses on
web and mobile development. In fact I have a comprehensive python course that teaches
you everything about python from the basics to more advanced concepts.
The second step in a machine learning project is cleaning or preparing the data and that
involves tasks such as removing duplicates null values. again this is a made-up pattern. It 's
not the representation of reality so let 's go ahead and download this csv file in my
downloads folder. Here we have this music. csv. The next step is to build a model using a
machine learning algorithm. The method takes two data sets, the input set and the output
will teach you how to build a model that can learn and predict the kind of music people like.
By the end of this one hour tutorial you will have a good understanding of machine learning
basics. You do n't need any prior knowledge in machine learning but you need to know
python fairly well. Machine learning has other applications in self-driving cars and robotics
language processing vision processing forecasting things like stock market trends and the
weather games and so on so that 's the basic idea about machine learning next we 'll look at
machine learning in action a machine learning project involves a number of steps the first
step is to import our data which often comes in the form of a csv file. We 're going to look at
the popular python libraries that we use in machine learning projects. The first one is numpy
which provides a multidimensional array. pandas is a data analysis library that provides a
concept called data frame. matplotlib is a two-dimensional plotting library for creating graphs
and plots. scikit-learn is one of the most popular machine learning libraries that provides all
these common algorithms like neural networks and neural networks.
Anaconda is that it will install jupyter as well as all those popular data science libraries like
numpy pandas and so on so we do n't have to manually install this using pip all right now as
part of the next step anaconda suggests to install microsoft vs code we already have this on
our machine so we can go with continue and close the installation. In this notebook we can
write python code and execute it line by line. Kaggle. com is a popular data set that we 're
going to use in this lecture. Create an account you can sign up with facebook google or
using a custom email and password once you sign up then come back here in the search
bar search for video game sales this is the name of a data set called vgsales. csv. This is the
beauty of jupyter. We can easily visualise our data doing this with vs code and terminal
windows is really tedious and clunky so what is this describe method returning basically it 's
returning some basic information about each column in this data set. In a real data science
or machine learning project we 'll have to use some techniques to clean up our data set. One
option is to remove the records that do n't have a value for the year column.
The first element in our array is an array itself. These are the values in this array which
basically represent the first row in our data set, so the video game with ranking 1 is called wii
sports. If we press the escape key green turns to blue and that means this cell is currently in
the command mode. The activated cell can be either in the edit mode or command mode
depending on the mode. When you run a cell this will only execute the code in that cell. The
code in other cells will not be executed. This notebook file includes our source code
organised in cells as well as the output for each cell. We also have autocompletion and
intellisense so in the cell let 's call df dataframe dot. I just wanted to let you know that I have
an online coding school at cordwindmarch. com where you can find plenty of courses on
web and mobile development. In fact I have a comprehensive python course that teaches
you everything about python from the basics to more advanced concepts.
The second step in a machine learning project is cleaning or preparing the data and that
involves tasks such as removing duplicates null values. again this is a made-up pattern. It 's
not the representation of reality so let 's go ahead and download this csv file in my
downloads folder. Here we have this music. csv. The next step is to build a model using a
machine learning algorithm. The method takes two data sets, the input set and the output
