by Nikola Živković | Released August 2018 | ISBN: 9781789131222

Predict heart disease, customer-buying behaviors, and much more in this course filled with real-world projectsAbout This VideoObserve 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 solutionsIn DetailScikit-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 Show and hide more Publisher Resources Download Example Code

1. Chapter 1 : Predicting the Wine Quality Using Multiple Linear Regression
   • The Course Overview 00:02:54
   • Exploring the Dataset and Identifying the Problem 00:04:16
   • Multiple Linear Regression 00:07:20
   • Implementing the Solution 00:10:44
   • Evaluating and Improving the Model 00:09:53
   • Analyzing the Results 00:02:45
2. Chapter 2 : Bike Sharing Demand Prediction Using Regression Trees
   • Exploring the Dataset and Identifying the Problem 00:04:03
   • Decision Trees and Random Forest 00:07:58
   • Feature Analysis and Engineering 00:09:59
   • Implementing the Solution 00:06:11
   • Analyze the Results 00:03:50
3. Chapter 3 : Heart Disease Predictions with Support Vector Machines
   • Exploring the Dataset and Identifying the Problem 00:03:19
   • Support Vector Machines 00:06:19
   • Feature Analysis and Engineering 00:06:59
   • Implementing the Solution 00:07:34
   • Analyze the Results 00:03:14
4. Chapter 4 : Poker Hand Predictions with K-Means Clustering
   • Exploring the Dataset and Identifying the Problem 00:03:48
   • K-Means Clustering 00:10:26
   • Feature Analysis and Engineering 00:07:17
   • Implementing the Solution 00:06:42
   • Analyze the Results 00:05:28
5. Chapter 5 : Understanding Buying Behavior Using Hierarchical Clustering
   • Exploring the Dataset and Identifying the Problem 00:02:49
   • Hierarchical Clustering 00:06:36
   • Feature Analysis and Engineering 00:05:17
   • Implementing the Solution 00:05:31
   • Analyze the Results 00:03:44
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