Introduction to Artificial Intelligence with Java

Introduction to Artificial Intelligence with Java
Introduction to Artificial Intelligence with Java
English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 10m | 900 MB

Build real-world Artificial Intelligence applications with Java to intelligently interact with the world around you

Artificial Intelligence, increasingly relevant in the modern world where everything is driven by technology and data, is the process of automating any system or process to carry out complex tasks and functions automatically, in order to achieve optimal productivity.

This video explains the basics of AI using popular Java-based libraries and frameworks to build your smart applications. We will cover easy-to-complex artificial intelligence tasks such as genetic programming, heuristic searches, reinforcement learning, neural networks, and segmentation with the practical approach we mentioned earlier.

By the end of this video, you will have a solid understanding of Artificial Intelligence concepts. You will be able to build your own smart applications for multiple domains, as required.

A step-by-step guide to building artificial intelligent solutions with Java

What You Will Learn

  • Get to grips with the different aspects of Artificial Intelligence
  • Leverage different Java packages and tools such as WEKA, Rapidminer, deeplearning4j, and more
  • Understand logic programming and how to use it
  • Create machine-learning models using supervised and unsupervised machine learning techniques
  • Implement different deep learning algorithms in deeplearning4j and build applications based on it
  • Understand the basics of heuristic searching and genetic programming
  • Differentiate between syntactic and semantic similarity between texts
  • Perform sentiment analysis for effective decision-making with Lingpipe
Table of Contents

Introduction to Artificial Intelligence and Java
1 The Course Overview
2 Understanding AI Problems Related to Supervised_Unsupervised Learning
3 Difference between Classification and Regression
4 Installing JDK and JRE
5 Setting Up of Netbeans IDE
6 Import Java Libraries and Export Code Projects as JAR Files

Exploring Search
7 Introduction to Search
8 Implementation of Dijkstra’s Search
9 Understand the Notion of Heuristics
10 Brief Introduction of Algorithm.mp4 Algorithm
11 Implementation of Algorithm.mp4 Algorithm

AI Games and Rule Based System
12 Introduction of Min-Max Algorithm
13 Implementation of Min-Max Algorithm Using an Example
14 Installing Prolog
15 Introduction of Rule-Based Systems with Prolog
16 Setting Up the Prolog with Java
17 Executing Prolog Queries Using Java

Interfacing with Weka
18 Brief Introduction to Weka
19 Installing and Interfacing with Weka
20 Reading and Writing Datasets
21 Converting Datasets

Handling Attributes
22 Filtering Attributes
23 Discretizing Attributes
24 Attribute Selection

Supervised Learning
25 Developing a Classifier
26 Model Evaluation
27 Making Predictions
28 Saving_Loading Models

Semi-Supervised and Unsupervised Learning
29 Working with K-means Clustering
30 Evaluating a Clustering Model
31 Introduction to Semi-Supervised Learning
32 Difference Between Unsupervised and Semi-Supervised Learning
33 Self-training_Co-training Machine Learning Models
34 Making Predictions with Semi-Supervised Machine Learning Models