Updated 27 Aug
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Hands-on Machine Learning with Scikit-Learn
Gain insights into Scikit-Learn's datasets, feature engineering, linear/logistic regression, and unsupervised learning. Delve into k-means clustering and neural networks to enhance your machine learning expertise.
4.4
36 lessons
5h
Created by Neko Yan
What's included
- 36 lessons across 12 modules
- 2 quizzes with instant feedback
- AI Tutor on every lesson
Join 3.1 million developers at
What You'll Learn
- Load and inspect Scikit-Learn built-in datasets (iris, mnist) for ML tasks
- Generate synthetic classification and regression datasets and visualize their distributions
- Preprocess data by scaling, normalizing, encoding categoricals, and imputing missing values
- Apply feature selection, text feature extraction (CountVectorizer, TF-IDF), and PCA reduction
- Build end-to-end workflows with Pipeline and FeatureUnion to chain transforms and models
- Evaluate and tune models using sklearn metrics and GridSearchCV for parameter searching
- Train supervised and unsupervised models (LR, logreg, SVM, trees, RF, k-means, MLP, NB, KNN)
Learning Roadmap
1.
Preliminaries
Preliminaries
Get familiar with Scikit-learn, its tools, practical use, and required prerequisites.
2.
Working with Datasets
Working with Datasets
Get started with loading datasets, generating synthetic data, and essential data preprocessing techniques.
3.
Feature Engineering
Feature Engineering
5 Lessons
5 Lessons
Go hands-on with feature selection, extraction, missing value handling, PCA, and pipelines.
4.
General Concepts
General Concepts
3 Lessons
3 Lessons
Break down complex ideas of metrics selection and parameter searching for machine learning.
5.
Linear Regression
Linear Regression
3 Lessons
3 Lessons
Solve problems in building and evaluating linear regression models using Scikit-Learn.
6.
Logistic Regression
Logistic Regression
3 Lessons
3 Lessons
Tackle logistic regression, preprocess data, and evaluate binary classification models.
7.
Support Vector Machine
Support Vector Machine
3 Lessons
3 Lessons
Practice using SVMs for classifying both linear and non-linear data effectively.
8.
Tree Model and Ensemble Method
Tree Model and Ensemble Method
5 Lessons
5 Lessons
Step through decision trees, gradient boosting, parameter tuning, and random forest models using Scikit-Learn.
9.
Unsupervised Learning
Unsupervised Learning
2 Lessons
2 Lessons
Look at key unsupervised learning techniques: K-means clustering and t-SNE for data visualization.
10.
Deep Learning
Deep Learning
3 Lessons
3 Lessons
Go hands-on with building and fine-tuning neural networks using Scikit-Learn's MLPClassifier.
11.
Others
Others
3 Lessons
3 Lessons
Apply your skills to Naive Bayes classifiers and K-Nearest Neighbors for predictive modeling.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Complete more lessons to unlock your certificate
About this course
WHY I WROTE THIS COURSE
Scikit-Learn is a powerful library that provides a handful of supervised and unsupervised learning algorithms. If you’re serious about having a career in machine learning, then scikit-learn is a must know.
In this course, you will start by learning the various built-in datasets that scikit-learn offers, such as iris and mnist. You will then learn about feature engineering and more specifically, feature selection, feature extraction, and dimension reduction.
In the latter half of the course, you will dive into linear and logistic regression where you’ll work through a few challenges to test your understanding. Lastly, you will focus on unsupervised learning and deep learning where you’ll get into k-means clustering and neural networks.
By the end of this course, you will have a great new skill to add to your resume, and you’ll be ready to start working on your own projects that will utilize scikit-learn.
ABOUT THE AUTHOR
Neko Yan
Data lovers, machine learning enthusiasts. Senior machine learning researcher in NLP, reinforcement learning, and display advertisement prediction.
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