Learning Scikit-Learn

Scikit-learn is the python library for machine learning. As you would know, machine learning deals with the development of algorithms which allows computers to learn automatically without human intervention. The Scikit-learn library contains various tools and algorithms for machine learning and statistical modeling and is probably the most useful library for machine learning in Python.

Scikit-learn library is build on NumPy, SciPy and matplotlib libraries of the Python’s Scientific Python stack. These are fundamental libraries in Python which is used for scientific computing and data analysis. ‘Scikts’ in fact stands for ‘SciPy toolkit’ which are add on packages for SciPy. Scikit-learn is the SciPy toolkit with modules for machine learning, which explains the second part of the library’s name. There are other Scikits available like the Scikit-image for image processing.

Why Scikit-learn- Its features

Scikit-learn is loaded with simple and efficient tools for data modeling and machine learning tasks. In general, a machine learning problem would have a sample data set and tries to predict properties of unknown data based on the sample data set. Machine learning problems are broadly classified as supervised learning in which you are training your machine learning task for every input with the corresponding task and unsupervised learning in which training data is just a set of inputs without any target values.

There are various methods and algorithms for supervised and unsupervised learning. Any machine learning algorithm you want to use- supervised learning algorithm or unsupervised learning algorithm, for your machine learning problem, there is high chance that it is part of Scikit-learn. Given below are some of the machine learning tasks and algorithms for the tasks available in Scikit-learn

Supervised Learning Tasks

Classification – Deals with identifying to which category an object belongs to. Example is identifying to which class an image belongs to in an image recognition system or identifying which mails are spam in spam. Algorithms for classification are Naïve Bayes, SVM, nearest neighbors, random forest, etc.

Regression – Deals with predicting the output which is a continuous variable (a variable which can take any numeric value). Example is predicting the price of a house depending on size, location, etc or predicting stock prices. Some of algorithms for regression are SVR, ridge regression, lasso, etc.

Unsupervised Learning Tasks

Clustering – Deals with automatic grouping of similar objects within the data. Examples include customer segmentation or grouping experimental outcomes. Algorithms used for Clustering are k-means, Spectral clustering, Gaussian mixtures, etc.

Density Estimation – Determine the distribution of data within the input space and finding the likelihood of objects. Algorithms used for this are Kernel density estimation, Density estimation histograms, etc.
Dimensionality Reduction – This deals with reducing the number of random variables to consider. Algorithms for dimensionality reduction are PCA, feature selection, non negative matrix factorization, etc.

In addition to the algorithms for supervised and unsupervised learning, Scikit-learn also has various modules for Model selection which includes comparing, cross validating and choosing parameters and models (parameter tuning) and Pre processing which includes feature extraction and normalization.

Now, let’s take a look at some of the benefits of Sci-kit learn which makes it a very popular tool for machine learning.

• It is a free to use software released with a BSD license.
• Easy to use
• It comes loaded with a rich set of features for machine learning with a consistent interface.
• Excellent API documentation which really helps user gets the information for working with it.
• It has support from an active community and has a collaborative library built by experts making it a well maintained library.

Learn Scikit-Learn

Scikit-learn is one of the most popular, user friendly and extremely useful tool for machine learning and artificial intelligence. If you want to pursue a career in machine learning/AI as a professional data scientist, Python is a good choice and in that case, Scikit-learn is a library that you cannot miss to learn.

Remember that Scikit-learn is usually used to build models and not to be used for reading and manipulating data which can be done by NumPy, Pandas, etc. So you need to have an idea of NumPy and Pandas library before you start with Scikit-learn. Given below are pre requisites for Scikit-learn.

Pre-requisites:

• Basic knowledge of Python – Loops, Functions and Classes in Python, etc.
• Statistics knowledge –Statistic concepts like random variables, Gaussian distribution, and linear regression.
• Good to have some idea of NumPy and Pandas libraries of Scientific Python stack though these might be covered in many tutorials and online courses for Scikit-learn.

Best Tutorials for Scikit-Learn

We have collected a list of some of the best beginner level tutorials on Scikit-Learn.

• Introduction to machine learning in Python with scikit-learn (video series)
• scikit-learn Tutorials
  
• Python Machine Learning: Scikit-Learn Tutorial
  
• Intro to Machine Learning with Scikit Learn and Python
  
• A Gentle Introduction to Scikit-Learn: A Python Machine Learning Library
• Python Machine Learning Tutorial, Scikit-Learn: Wine Snob Edition

Best Courses For Scikit-Learn

However, if you already decided to take a deep dive, here are some of the best courses we found on Scikit-Learn.

For Beginners

• Supervised Learning with scikit-learn -- Paid
• Machine Learning & AI Foundations: Value Estimations -- Paid

For Intermediate Level

• Machine learning with Scikit-learn -- Paid
• Applied Machine Learning in Python -- Paid