Welcome back.
Go to Colab.
Open.
Colab.
Go to upload, browse and select file number five ML with Python.
Click okay.
And by the way, as I told you in the previous videos, all those files are available to download in
the resources folder under each section.
And you can download them from the first section, all in one package.
Okay.
Now this is our ML with Python notebook.
Introduction to Machine Learning with Python.
Welcome.
In this hands on notebook you'll learn what is machine learning.
Types of ML supervised.
Unsupervised, how to use Python for ML, and a full example predicting house prices with linear regressions.
Tools used pandas, matplotlib, NumPy and scikit learn.
So let's introduce our new libraries that we're going to use in Python.
First of all, we need to use numpy.
NumPy in Python is a powerful library used for numerical and scientific computing.
It provides multi-dimensional arrays ndarray that are much faster and more efficient than Python lists,
supports mathematical operations like linear algebra, statistics, trigonometric and etc. on arrays.
Often used in data science, machine learning, and scientific research.
Because of its speedy speed and ability to handle large data sets, works well with other libraries
like pandas, SciPy, TensorFlow, and others.
In short, numpy equals to fast array plus math operations.
The foundation of numerical computing in Python.
So this is a very important library.
Pandas.
We talked about pandas in the previous videos.
We talked about matplotlib in the previous videos.
Now we're going to talk about sklearn.
Scikit learn or sklearn is a popular machine learning library in Python built on top of NumPy, SciPy
and Matplot library provides easy to use tools for data processing.
Data processing includes scaling, encoding, splitting, data sets, machine learning algorithms,
classification, regression, clustering.
As we talked about in the previous videos, and model evaluation, cross validation metrics and selections
well suited for small to medium sized data sets.
Widely used in AI, data science, and predictive modeling.
In short, scikit learn a user friendly toolkit for building, training and evaluating machine learning
models in Python.
Later we're going to use TensorFlow, but scikit learn if you are working on smaller data sets and traditional
ML, for example classification with decision trees, go and use it using TensorFlow or PyTorch.
If you are building deep learning models, for example image recognition, NLP object detections.
Later we're going to use TensorFlow.
But in this notebook I'm going to show you how to use scikit learn.
If you are using a simple data sets for classification.
And later we're going to work with TensorFlow.
So don't worry we're going to learn and and work with different types of libraries.
Click run to import those libraries.
And when you get libraries imported successfully you are good.
And here we go.
All libraries are imported successfully.