Exploring Artificial Intelligence Training
Exploring Artificial Intelligence Training
Training Exploring Artificial Intelligence - Online E-Learning Course. Order and start immediately for the best price.
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- Artificial Intelligence
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Exploring Artificial Intelligence E-Learning
Order this great E-Learning Training Exploring Artificial Intelligence online course 1 year 24/7 access to rich interactive videos, voice, practice assignments, progress monitoring through reports and tests per subject to test the knowledge directly. After the course you will receive a certificate of participation.
Artificial intelligence (AI) has been one of the core research techniques in computer science since the beginning of the field, and in recent years an emergence in research and computational power has made AI development more accessible for developers. In this learning path, you will learn the foundation of AI and the techniques used to make AI solve problems.
Course content
Introducing AI
Course: 31 Minutes
- Course Introduction
- What Is Artificial Intelligence?
- Fields and Applications
- AI Techniques
- Defining Agents
- Types of Agents
Environments
Course: 18 Minutes
- Describing Environments
- Environment's Observability
- Number of Agents in Environments
- Certainty in an Environment
- Environment's Behavior
Practice: Agents and Environments
Course: 3 Minutes
- Exercise: Describe an Agent and an Environment
Introducing Search Problems
Course: 15 Minutes
- Course Introduction
- Search Problems Defined
- Search Problem Examples
- Representing Search Problems for Search Algorithms
Brute Force Searching
Course: 9 Minutes
- Breadth-first Search
- Depth-first Search
- Iterative Deepening Search
Informed Searching
Course: 14 Minutes
- Greedy Best-first Search
- Heuristics
- Making Good Heuristics
- A* Search
Local Searching
Course: 5 Minutes
- Hill-climbing Search
- Simulated Annealing
Practice: Identifying Search Problems
Course: 2 Minutes
- Exercise: Describe Search Problems
Introducing CSPs
Course: 6 Minutes
- Course Introduction
- What Are Constraint Satisfaction Problems (CSPs)?
- Constraint Satisfaction Problem Examples
Solving CSPs
Course: 19 Minutes
- Backtracking Search
- Variable Ordering
- Arc Consistency
- Constraint Propagation
- Using Inference with Search
- Using a Local Search for CSPs
- Solving a Sudoku Puzzle
Practice: Constraint Satisfaction Problems
Course: 3 Minutes
- Exercise: Solve a Constraint Satisfaction Problem
- Course Introduction
- Adversarial Problems
- Adversarial Problem Representation
- Minimax Algorithm
- Alpha-beta Pruning
Imperfect Decisions
Course: 15 Minutes
- Evaluation Functions
- Cutoff Search
- Lookup Tables
- The Game of Chess
Stochastic Games
Course: 11 Minutes
- Expectiminimax Value
- Stochastic Evaluation Functions
- Monte Carlo Tree Search
Practice: Using the Minimax Algorithm
Course: 2 Minutes
- Exercise: Use Minimax and Pruning to Play a Game
Understanding Uncertainty
Course: 10 Minutes
- Course Introduction
- What Is Uncertainty?
- Uncertainty Representation
Understanding Utility Theory
Course: 13 Minutes
- Utility Theory
- Utility and Preferences
- Utility and Risks
- Value of Information
Examining the Markov Decision Process
Course: 21 Minutes
- Markov Chains
- Markov Decision Process
- MDP Value Iteration
- Partially Observable Markov Decision Process (POMDP)
- POMDP Value Iteration
- Applying POMDPs
Practice: Markov Decision Process
Course: 3 Minutes
- Exercise: Describe the Markov Decision Process
Learning for Computers
Course: 9 Minutes
- Course Introduction
- How Computers Can Learn
- Learning From Examples
Decision Trees
Course: 12 Minutes
- Using Decision Trees
- Decision Tree Learning: Information Gain
- Decision Tree Learning: Choosing Attributes
- Overfitting
Neural Networks
Course: 25 Minutes
- Artificial Neural Networks
- Neural Network Structure
- Types of Neural Networks
- Perceptron Learning
- Deep Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
Practice: Perceptron Training
Course: 2 Minutes
- Exercise: Make a Perceptron Learn From Examples
Introducing Reinforcement Learning
Course: 21 Minutes
- Course Introduction
- What Is Reinforcement Learning?
- Additive and Discounted Rewards
- Passive Learning
- Direct Utility Estimation
- Temporal Difference Learning
- Active Learning
- Exploration and Exploitation Policies
Q-learning Algorithm
Course: 12 Minutes
- Defining Q-learning
- Implementing Q-learning
- Off-policy and On-policy Learning
- Function Approximation
- Deep Q-learning
Practice: Q-learning
Course: 2 Minutes
- Exercise: Describe Q-learning
Defining NLP
Course: 17 Minutes
- Course Introduction
- Introducing Natural Language Processing (NLP)
- Base NLP Operations
- Porter Stemming Algorithm
- Named Entity Recognition
Basic Models
Course: 11 Minutes
- Building Natural Language Processing (NLP) Models
- Text Classification
- Naïve Bayes Classification
- Information Retrieval (IR)
Communication
Course: 14 Minutes
- Answering Questions
- Parsing
- Machine Translation
- Speech Recognition
Practice: NLP Operations
Course: 2 Minutes
- Exercise: Describe NLP Operations
| Language | English |
|---|---|
| Qualifications of the Instructor | Certified |
| Course Format and Length | Teaching videos with subtitles, interactive elements and assignments and tests |
| Lesson duration | 9 Hours |
| Progress monitoring | Yes |
| Access to Material | 365 days |
| Technical Requirements | Computer or mobile device, Stable internet connections Web browsersuch as Chrome, Firefox, Safari or Edge. |
| Support or Assistance | Helpdesk and online knowledge base 24/7 |
| Certification | Certificate of participation in PDF format |
| Price and costs | Course price at no extra cost |
| Cancellation policy and money-back guarantee | We assess this on a case-by-case basis |
| Award Winning E-learning | Yes |
| Tip! | Provide a quiet learning environment, time and motivation, audio equipment such as headphones or speakers for audio, account information such as login details to access the e-learning platform. |
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