Real-World Machine Learning Projects with Scikit-Learn [Video]

• Work with Scikit-Learn's Machine Learning tools to build efficient real -world projects using Scikit-Learn 
• Predict demand for your products (to help your business adapt) by using Regression Trees
• Use Support Vector Machines to learn how to train your model to predict the chances of heart disease 
• Analyze the population and generate results in line with ethnicity and other factors using K-Means Clustering
• Understand the buying behavior of your customers using Customer Segmentation to drive the sales of your products

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

The course takes the approach of firstly defining the problem and then giving you the solution, along with the steps to solve it practically by using Python using Scikit-Learn. You will build examples from scratch, progressing from simpler problems to complicated ones

• 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