- Perform classification using the Weka Library.
- Implement Pattern Recognition of non-labeled data
- Build Regression models for data with multiple features
- Save trained models for further reusability
- Learn how to perform cross-validation
- Leverage Deep Learning in ML problems
- Implement Natural Language Processing with Deep Learning
Developers are worried about using various algorithms to solve different problems. This course is a perfect guide to identifying the best solution to efficiently build machine learning projects for different use cases to solve real-world problems.
In this course, you will learn how to build a model that takes complex feature vector form sensor data and classifies data points into classes with similar characteristics. Then you will predict the price of a house based on historical data. Finally, you will build a Deep Learning model that can guess personality traits using labeled data.
By the end of this course, you will have mastered each machine learning domain and will be able to build your own powerful projects at work.
Style and Approach
This is a step-by-step and fast-paced guide that will help you learn different ML techniques you can use to solve real-world problems, Every section will tackle a practical problem and take your ML skills to the next level
- Build machine learning projects using Java’s extensive library support such as Weka, deeplearning4j, ND4J, and many more
- A practical guide, with a strict focus on case implementations, to creating projects for each machine learning domain
- Solve real-world problems with the help of machine learning with Java ML libraries