Python Machine Learning Tutorial (Data Science)
Programming with Mosh
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 . 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
will 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
Programming with Mosh
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 . 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
will 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
