Building Your First scikit-learn Solution
This course covers both the why and how of using scikit-learn. You'll delve into scikit-learn’s niche in the ever-growing taxonomy of machine learning libraries, and important aspects of working with scikit-learn estimators and pipelines.
What you'll learn
Even as the number of machine learning frameworks and libraries increases on a daily basis, scikit-learn is retaining its popularity with ease. scikit-learn makes the common use cases in machine learning - clustering, classification, dimensionality reduction, and regression - incredibly easy. In this course, Building Your First scikit-learn Solution, you'll gain the ability to identify the situations where scikit-learn is exactly the tool you are looking for, and also those situations where you need something else. First, you'll learn how scikit-learn’s niche is traditional machine learning, as opposed to deep learning or building neural networks. Next, you'll discover how seamlessly it integrates with core Python libraries. Then, you'll explore the typical set of steps needed to work with models in scikit-learn. Finally, you'll round out your knowledge by building your first scikit-learn regression and classification models. When you’re finished with this course, you'll have the skills and knowledge to identify precisely the situations when scikit-learn ought to be your tool of choice, and also how best to leverage the formidable capabilities of scikit-learn.
Table of contents
- Version Check 0m
- Module Overview 1m
- Prerequisites and Course Outline 1m
- Introducing Machine Learning 4m
- Learning from Data: Training and Prediction 6m
- Traditional and Representation ML Models 7m
- The Niche of scikit-learn in ML 5m
- Exploring scikit-learn Libraries 7m
- Supervised and Unsupervised Learning 7m
- Installing scikit-learn Libraries 4m
- Summary 1m
- Module Overview 1m
- The Machine Learning Workflow 4m
- Using scikit-learn in the Machine Learning Workflow 7m
- Choosing the Right Estimator: Classification 4m
- Choosing the Right Estimator: Clustering 2m
- Choosing the Right Estimator: Regression and Dimensionality Reduction 3m
- Exploring Built-in Datasets in scikit-learn 6m
- Exploring the Boston Newsgroups and Digits Datasets 3m
- California Housing Dataset: Exploring Numeric and Categorical Features 6m
- California Housing Dataset: Exploring Relationships in Data 5m
- Summary 2m
About the author
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providin... more
