Machine Learning
Machine learning is a subset of artificial intelligence or artificial intelligence. 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 multi-dimensional 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 jupiter we can easily visualize 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 which 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 organized 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. After you watch this tutorial if you want to
learn more you may want to look at my python course it comes with a 30 day money back guarantee
and a certificate of completion.
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
the 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
Machine learning is a subset of artificial intelligence or artificial intelligence. 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 multi-dimensional 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 jupiter we can easily visualize 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 which 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 organized 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. After you watch this tutorial if you want to
learn more you may want to look at my python course it comes with a 30 day money back guarantee
and a certificate of completion.
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
the 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
