Predict heart disease, customer-buying behaviors, and much more in this course filled with real-world projects
About This Video
- Observe data from multiple angles and use machine learning algorithms to solve real-world problem to make your projects successful.
- Use Regression Trees, Support Vector Machines, K-Means Clustering, and customer segmentation algorithms in real world situations.
- Apply your knowledge to practical real-world projects using ML models to get insightful solutions
Scikit-Learn is one of the most powerful Python Libraries with has a clean API, and is robust, fast and easy to use. It solves real-world problems in the areas of health, population analysis, and figuring out buying behavior, and more.
In this course you will build powerful projects using Scikit-Learn. Using algorithms, you will learn to read trends in the market to address market demand. You'll delve more deeply to decode buying behavior using Classification algorithms; cluster the population of a place to gain insights into using K-Means Clustering; and create a model using Support Vector Machine classifiers to predict heart disease.
By the end of the course you will be adept at working on professional projects using Scikit-Learn and Machine Learning algorithms.
The code bundle for this video course is available at - https://github.com/PacktPublishing/Real-World-Machine-Learning-Projects-with-Scikit-Learn
Table of contents
- Chapter 1 : Predicting the Wine Quality Using Multiple Linear Regression
- Chapter 2 : Bike Sharing Demand Prediction Using Regression Trees
- Chapter 3 : Heart Disease Predictions with Support Vector Machines
- Chapter 4 : Poker Hand Predictions with K-Means Clustering
- Chapter 5 : Understanding Buying Behavior Using Hierarchical Clustering
- Title: Real-World Machine Learning Projects with Scikit-Learn
- Release date: August 2018
- Publisher(s): Packt Publishing
- ISBN: 9781789131222
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