Data Science Bootcamp: Hands-On Java Training
Build neural networks, machine learning algorithms, classifiers and more, step by step through real examples with data visualization and more!
The Best Java Data Science Guide
Do you need to learn data science and algorithms?
Are you tired of theory and want to learn through practical projects you can put on your resume and upload to Github?
This is the course for you.
Enroll now to learn data science with Java programming.
Learn through hands-on coding examples and learn to solve problems quickly.
Don't wait! Enroll while spots are open.
Java Data Science
Section 1: Course Introduction
Section 2: K-Nearest Neighbors Project
- Learn how to set up data, enable user input, and set up a K-Nearest Neighbors algorithm.
- Learn how to work with matrices, RealMatrix, and Intstream.
- Sort euclidian distances and show classification.
- Calculate range and normalize values before testing the application.
Section 3: Decision Trees Project
- Build a 'display best feature to split on' project using a dataset and the Decision Trees algorithm.
- Learn key data science fundamentals including information gain and entropy.
- Build features, feature values, datasets, and information gain tables.
Section 4: Neural Networks Project
- Build your own neural network algorithm.
- Work with target result data, weight and activation functions.
- Test the application and train your model until the error is zero.
Section 5: Naive Bayes Project
- Build a classifier by learning different Naive Bayes algorithms.
- Learn how to classify user input and calculate log sum, probability and conditional properties.
- Test your applications to see your text classification at work.
A dataset and source code is provided with each project!
A SCHOOL YOU CAN TRUST
- Lifetime access that never expires
- Project-based curriculum to superboost your portfolio
- Graduation certificate for every course
- Absolute beginner-friendly
- New courses every month
- Efficient lectures with step by step explanations
- Relevant industry topics 8 years of award-winning course delivery
- 700,000 students in 186 countries
- Learn with free tools and affordable courses
- Experience with object-oriented programming fundamentals. If you need experience, enrol in Introduction to Algorithms in Java first.
- We will show you how to install a free IDE such as Eclipse or Android Studio.
- We will show you how to install Java (also free)
Frequently Asked Questions
PreviewIntroduction To K-Nearest Neighbors (11:20)
StartDataset for KNearestNeighbors Project
Start00 Set Up The Data (8:30)
Start01 Enable User Input (10:29)
Start02 Handle User Input (14:40)
Start03 Set Up K-Nearest Neighbors (9:17)
Start04 Divide Matrices With Intstream (10:29)
Start05 Store User Input As A Realmatrix (7:59)
Start06 Build Matrix Methods (11:47)
Start07 Find Euclidian Distances (11:19)
Start08 Sort Euclidian Distances (6:17)
Start09 Show Classification (2:58)
Start10 Calculate Range And Normalize Values (11:54)
Start11 Test The Application (2:50)
StartKNearestNeighborsLab Source Code
StartIntroduction to K-Nearest Neighbors PDF
Start00 Introduction To Decision Trees, Information Gain And Entropy (6:12)
StartDataSet for Decision Trees Project
Start01 Set Up Dataset And Classes (14:55)
Start02 Represent Each Feature Value (6:29)
Start03 Build A Dataset Of Feature Values (10:11)
Start04 Build A Dataset From A Larger Dataset (8:05)
Start05 Build A Dataset Class (6:27)
Start06 Display Information Gain Table (9:40)
Start07 Display Feature To Split On (6:32)
Start08 Test And Run The Project (3:40)
StartDecision Trees Project Source Code
StartIntroduction to Decision Trees, Information Gain and Entropy PDF