Scikit-learn, or sklearn for short, is the first Python library we turn to when building machine learning models. Sklearn is unanimously the favorite Python library among data scientists. As a newcomer to machine learning, you should be comfortable with sklearn and how to build ML models, including:
- Linear Regression using sklearn
- Logistic Regression using sklearn, and so on.
There’s no question - scikit-learn provides handy tools with easy-to-read syntax. Among the pantheon of popular Python libraries, scikit-learn (sklearn) ranks in the top echelon along with Pandas and NumPy.
We love the clean, uniform code and functions that scikit-learn provides. The excellent documentation is the icing on the cake as it makes a lot of beginners self-sufficient with building machine learning models using sklearn.
In short, sklearn is a must-know Python library for machine learning. Whether you want to build linear regression or logistic regression models, decision tree or a random forest, sklearn is your go-to library.
- What is scikit-learn or sklearn?
- What’s the difference between scikit-learn and sklearn?
- How do I install sklearn?
- Do I need to know machine learning to use sklearn?
- What kind of machine learning models can I build using sklearn?
- How much programming knowledge do I need to have to master sklearn?
- Which programming language should I know for sklearn?
- What are the different functions or areas that sklearn covers?
- Which machine learning focused organization are using sklearn?
- What kind of projects can I work on using sklearn?
This free course by Analytics Vidhya will teach you all you need to get started with scikit-learn for machine learning. We will go through the various components of sklearn, how to use sklearn in Python, and of course, we will build machine learning models like linear regression, logistic regression and decision tree using sklearn!
Scikit-learn is THE go-to Python library for building machine learning models. So if you’re in any of the below roles/phases, this course is for you!
- Machine learning aspirant
- Machine learning fresher
- Data science enthusiast
- Team leader
- In a senior role for a machine learning project
- Just want to understand how machine learning models are designed
You don’t need to be a machine learning master to get started with sklearn! There are primarily two prerequisites:
- Basic machine learning knowledge: It would help if you knew the different machine learning models, such as linear regression and logistic regression. This will help you work with sklearn in a much more efficient manner
- Basic Python knowledge: You should ideally know the basics of Python to be able to work with sklearn. We recommend the incredibly popular (and free) ‘Python for Data Science’ course to get your feet wet
- Welcome to this course
- What is scikit-learn?
- Components of scikit-learn
- Community / Organizations using scikit-learn
- Introduction to Data Science Life Cycle
- Scikit-learn for Data Preprocessing
- Treating missing values
- Treating Outliers
- Feature Engineering
- Dimensionality Reduction
- Introduction to Model Building and Evaluation
- Understanding Problem Statement
- Building a prototype model
- Data Exploration and Preprocessing
- Encode the categorical variables
- Scale the data
- Model Building
- Feature Importance
- Identifying features to build the ML pipeline
- Pipeline Design
- Building Pipeline
- Predict the Target
What is scikit-learn or sklearn?
Sklearn, short for scikit-learn, is a Python library for building machine learning models. Sklearn is among the most popular open-source machine learning libraries in the world.
Scikit-learn is being used by organizations across the globe, including the likes of Spotify, JP Morgan, Booking.com, Evernote, and many more.
What’s the difference between scikit-learn and sklearn?
Scikit-learn and sklearn are one and the same! This Python library is popularly known as sklearn because that’s how you use it when working in Python.
You’ll soon be very familiar with commands that include “from sklearn import….”!
How do I install sklearn?
Sklearn comes with the ANaconda distribution by default. So if you already have Python in your machine, you’ll have sklearn inbuilt. However, that might not be the latest version.
There are a couple of other ways to install scikit-learn:
- Install the latest official release from the sklearn website
- Building the package from source. This is best for users who want the latest-and-greatest features and aren’t afraid of running brand-new code
Do I need to know machine learning to use sklearn?
Basic machine learning knowledge will definitely help. Sklearn helps us build machine learning models in Python but we need to know which model we want to build, how to tune that model, and how to evaluate it.
For example, let’s say you want to perform linear regression using sklearn. You would need to know what linear regression is, right? Also, it would help you to understand how to improve your linear regression model’s accuracy once you run it. All of these things will help you build better machine learning models using the sklearn library.
What kind of machine learning models can I build using sklearn?
Good question! Here’s where sklearn really shines. You can build all sorts of machine learning models using sklearn, for both supervised and unsupervised learning.
Here’s a broad list of machine learning models you can build using sklearn:
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machine (SVM)
- Naive Bayes
- K-means Clustering
- k-Nearest Neighbor, among many others!
How much programming knowledge do I need to have to master sklearn?
You should know basic Python. That’s it. Familiarity with Pandas and NumPy will of course be hugely beneficial.
We recommend taking Analytics Vidhya’s free ‘Python for Data Science’ course if you want to learn Python. That course will teach you all you need to know about Python from scratch and set you up perfectly for the ‘Getting Started with sklearn’ course.
Which programming language should I know for sklearn?
You’ll know the answer to this by now! Python is the programming language you will be working with for building machine learning models using sklearn.
What are the different functions or areas that sklearn covers?
Scikit-learn has reorganized and restructured its functions & packages into six main modules:
- Classification: Identifying which category an object belongs to
- Regression: Predicting a continuous-valued attribute associated with an object
- Clustering: For grouping unlabeled data
- Dimensionality Reduction: Reducing the number of random variables to consider
- Model Selection: Comparing, validating and choosing parameters and models
- Preprocessing: Feature extraction and normalization
Which machine learning focused organization are using sklearn?
Pretty much every machine learning focused organization has leveraged sklearn. Here are a few top organizations:
- J.P. Morgan
- Hugging Face
You can view the full list on sklearn’s official documentation page.
What kind of projects can I work on using sklearn?
You can work on both supervised learning and unsupervised learning projects using sklearn. Under supervised, sklearn can be applied on both regression as well as classification problems.
So whether it’s a simple linear regression model, or a complex ensemble learning technique, sklearn is your library! We suggest going to our DataHack platform, picking up a problem you want, and applying what you’ve learned in this course.
Who should take the Getting Started with scikit-learn (sklearn) for Machine Learning course?
This course is designed for anyone who wants to get started with machine learning. So whether you’re a machine learning aspirant who is just starting out or a team leader looking to understand how it all works, this course is for you.
I have some programming experience but no background in machine learning. Is this course right for me?
Sure. You will be able to follow along with the Python code but it’ll definitely help if you know basic machine learning algorithms, like linear regression, logistic regression, and decision trees.
What is the fee for the course?
This course is free of cost!
How long would I have access to the “Getting Started with scikit-learn (sklearn) for Machine Learning” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.
How much effort do I need to put in for this course?
You can complete the “Getting Started with scikit-learn (sklearn) for Machine Learning” course in a few hours. You are also expected to apply your knowledge and learning of this course to solve machine learning problems. The time taken in projects varies from person to person.
I’ve completed this course and have decent knowledge about sklearn and the different machine learning algorithms. What should I learn next?
That’s great! We highly recommend expanding your skillset and portfolio by taking the next step in the Applied Machine Learning course. That is a comprehensive course covering the entire end-to-end machine learning pipeline and includes a thorough deep dive into the various machine learning algorithms, including linear regression and logistic regression, of course!
Can I download the videos in this course?
We regularly update the “Getting Started with scikit-learn (sklearn) for Machine Learning” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.
Which programming language is used to teach the Getting Started with scikit-learn (sklearn) for Machine Learning course?
This course uses Python programming language throughout