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

Multiple Linear Regression

Evaluating and Improving the Model

Analyzing the Results

Bike Sharing Demand Prediction Using Regression Trees

Decision Trees and Random Forest

Heart Disease Predictions with Support Vector Machines

Support Vector Machines

Poker Hand Predictions with K-Means Clustering

K-Means Clustering

Understanding Buying Behavior Using Hierarchical Clustering

Hierarchical Clustering

Video Description

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-...

Style and Approach

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

Video Preview

What You Will Learn

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

Authors

Nikola Živković

Nikola Zivkovic is a software developer with over 7 years' experience in the industry. He earned his Master’s degree in Computer Engineering from the University of Novi Sad in 2011, but by then he was already working for several companies. At the moment he works for Vega IT Sourcing from Novi Sad. During this period, he worked on large enterprise systems as well as on small web projects. Also, he frequently talks at meetups and conferences and he is a guest lecturer at the University of Novi Sad. You can read his articles on his blog – rubikscode.net.

Predicting the Wine Quality Using Multiple Linear Regression

The Course Overview

Multiple Linear Regression

Evaluating and Improving the Model

Analyzing the Results

Bike Sharing Demand Prediction Using Regression Trees

Decision Trees and Random Forest