Machine learning is quickly becoming the most sought after skill in the job market. Specifically, employers are looking for candidates with experience in scikit-learn, the most popular ML Python library. Scikit-learn is a library for Python that provides machine learning developers with many unsupervised and supervised learning algorithms.
Today, we’ll explore this awesome library and show you how to implement its core functions. At the end, we’ll combine what we’ve learned to implement your own linear regression algorithm.
Here’s what we’ll cover today:
- What is Scikit-Learn?
- Getting Started With Scikit-learn
- Datasets and import.sklearn
- Generate Synthetic Regression Data
- Plotting Data With Matplotlib
- Data Preprocessing
- Scikit-learn Linear Regression: Implement an Algorithm
- Wrapping Up and Next Steps
What is Scikit-Learn?
Scikit-learn (or sklearn for short) is a free open-source machine learning library for Python. It’s designed to cooperate with SciPy and NumPy libraries and simplifies data science techniques in Python with built-in support for popular classification, regression, and clustering machine learning algorithms.
Sklearn serves as a unifying point for many ML tools to work seamlessly together. It also gives data scientists a one-stop-shop toolkit to import, preprocess, plot, and predict data.
The project was started by David Cournapeau during the 2007 Google Summer of Code, and this library has grown over the last decade in both popularity and features. Scikit-learn is now the most popular machine learning library on Github.
Scikit-learn provides tools for:
- Regression, including Linear and Logistic Regression
- Classification, including K-Nearest Neighbors
- Model selection
- Clustering, including K-Means and K-Means++
- Preprocessing, including Min-Max Normalization
Advantages of scikit-Learn
Developers and machine learning engineers use Sklearn because:
- It’s easy to learn and use.
- It’s free and open-source.
- It helps in all aspects and algorithms of machine learning, even deep learning.
- It’s very versatile and powerful.
- Detailed documentation and active community.
- It’s the most widely used machine learning toolkit.
Libraries used with scikit-learn
Scikit-learn is a toolkit to expand the functions of the existing SciPy Stack (sometimes called the NumPy Stack). Below, we outline how Scikit-learn uses each library within the SciPy stack for data analysis.
- NumPy: Advanced linear algebra and NumPy array operations.
- SciPy: Contains modules for optimization, linear algebra, and other essential data science functions.
- Matplotlib: Visualization and data plotting in two or three dimensions.
- IPython: Increasing console interactivity.
- SymPy: Symbolic computation and computer algebra.
- Pandas: Data manipulation and analysis, mainly through dataframes and tables.
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