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[FreeCoursesOnline.Me] [Packt] Real-World Machine Learning Projects With Scikit-Learn - [FCO]
TORRENT SUMMARY
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Predict heart disease, customer-buying behaviors, and much more in this course filled with real-world projects
Video Details
ISBN 9781789131222
Course Length 2 hours 34 minutes
Table of Contents
• PREDICTING THE WINE QUALITY USING MULTIPLE LINEAR REGRESSION
• BIKE SHARING DEMAND PREDICTION USING REGRESSION TREES
• HEART DISEASE PREDICTIONS WITH SUPPORT VECTOR MACHINES
• POKER HAND PREDICTIONS WITH K-MEANS CLUSTERING
• UNDERSTANDING BUYING BEHAVIOR USING 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 - Official Source.
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.
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 Živkovic
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.
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FILE LIST
Filename
Size
1.Predicting the Wine Quality Using Multiple Linear Regression/01.The Course Overview.mp4
11.2 MB
1.Predicting the Wine Quality Using Multiple Linear Regression/02.Exploring the Dataset and Identifying the Problem.mp4
8.8 MB
1.Predicting the Wine Quality Using Multiple Linear Regression/03.Multiple Linear Regression.mp4
12.6 MB
1.Predicting the Wine Quality Using Multiple Linear Regression/04.Implementing the Solution.mp4
42.1 MB
1.Predicting the Wine Quality Using Multiple Linear Regression/05.Evaluating and Improving the Model.mp4
38.8 MB
1.Predicting the Wine Quality Using Multiple Linear Regression/06.Analyzing the Results.mp4
12.6 MB
2.Bike Sharing Demand Prediction Using Regression Trees/07.Exploring the Dataset and Identifying the Problem.mp4
9 MB
2.Bike Sharing Demand Prediction Using Regression Trees/08.Decision Trees and Random Forest.mp4
16.5 MB
2.Bike Sharing Demand Prediction Using Regression Trees/09.Feature Analysis and Engineering.mp4
39.2 MB
2.Bike Sharing Demand Prediction Using Regression Trees/10.Implementing the Solution.mp4
24.4 MB
2.Bike Sharing Demand Prediction Using Regression Trees/11.Analyze the Results.mp4
15.2 MB
3.Heart Disease Predictions with Support Vector Machines/12.Exploring the Dataset and Identifying the Problem.mp4
6.7 MB
3.Heart Disease Predictions with Support Vector Machines/13.Support Vector Machines.mp4
12.1 MB
3.Heart Disease Predictions with Support Vector Machines/14.Feature Analysis and Engineering.mp4
27.5 MB
3.Heart Disease Predictions with Support Vector Machines/15.Implementing the Solution.mp4
29.6 MB
3.Heart Disease Predictions with Support Vector Machines/16.Analyze the Results.mp4
12.8 MB
4.Poker Hand Predictions with K-Means Clustering/17.Exploring the Dataset and Identifying the Problem.mp4
8 MB
4.Poker Hand Predictions with K-Means Clustering/18.K-Means Clustering.mp4
18.4 MB
4.Poker Hand Predictions with K-Means Clustering/19.Feature Analysis and Engineering.mp4
28.7 MB
4.Poker Hand Predictions with K-Means Clustering/20.Implementing the Solution.mp4
26.3 MB
4.Poker Hand Predictions with K-Means Clustering/21.Analyze the Results.mp4
21.6 MB
5.Understanding Buying Behavior Using Hierarchical Clustering/22.Exploring the Dataset and Identifying the Problem.mp4
5.8 MB
5.Understanding Buying Behavior Using Hierarchical Clustering/23.Hierarchical Clustering.mp4
12.2 MB
5.Understanding Buying Behavior Using Hierarchical Clustering/24.Feature Analysis and Engineering.mp4
21.6 MB
5.Understanding Buying Behavior Using Hierarchical Clustering/25.Implementing the Solution.mp4
21.8 MB
5.Understanding Buying Behavior Using Hierarchical Clustering/26.Analyze the Results.mp4
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