Certified Artificial Intelligence Professional VS-1438
Certified Artificial Intelligence Professional Certification Code VS-1438 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 certification course, you will learn the foundation of Artificial Intelligence (AI) and the techniques used to make AI solve problems. Why should one take this certification? This Course is intended for web developers, programmers and graduates wanting to excel in their chosen areas. It is also well suited for those who are already working and would like to take certification for further career progression. Earning Vskills Artificial Intelligence Certification can help candidate differentiate in today's competitive job market, broaden their employment opportunities by displaying their advanced skills, and result in higher earning potential. Who will benefit from taking this certification? Job seekers looking to find employment in IT or software development departments of various companies and students who want to learn Artificial Intelligence. Test Details Duration: 60 minutes No. of questions: 50 Maximum marks: 50, Passing marks: 25 (50%) There is no negative marking in this module. Fee Structure Rs. 5,999/- (Excludes all taxes) * *Fees may change without prior notice, please refer http:// for updated fees Companies that hire Vskills Certified Artificial Intelligence Professional Artificial Intelligence professionals are in great demand. It is the most popular core research techniques in computer science. Companies like Accenture, KPMG, Amazon, IBM, Ericsson and Wipro etc. specializing in Data Science are constantly looking for certified professionals in Artificial Intelligence.
Table of Contents 1. Introduction to Artificial Intelligence 1.1 Introducing AI 1.2 Environments 1.3 Practice: Agents and Environments 2. Search Problems 2.1 Introducing Search Problems 2.2 Brute Force Searching 2.3 Informed Searching 2.4 Local Searching 2.5 Practice: Identifying Search Problems 3. Constraint Satisfaction Problems 3.1 Introducing CSPs 3.2 Solving CSPs 3.3 Practice: Constraint Satisfaction Problems 4. Adversarial Problems 4.1 Adversarial Games 4.2 Imperfect Decisions 4.3 Stochastic Games 4.4 Practice: Using the Minimax Algorithm 5. Uncertainty 5.1 Understanding Uncertainty 5.2 Understanding Utility Theory 5.3 Examining the Markov Decision Process 5.4 Practice: Markov Decision Process 6. Machine Learning 6.1 Learning for Computers 6.2 Decision Trees 6.3 Neural Networks 6.4 Practice: Perceptron Training 7. Reinforcement Learning 7.1 Introducing Reinforcement Learning 7.2 Q-learning Algorithm 7.3 Practice: Q-learning 8. Introducing Natural Language Processing
8.1 Defining NLP 8.2 Basic Models 8.3 Communication 8.4 Practice: NLP Operations
Sample Questions 1. What kind of search is the breadth-first search? A. First in, last out B. Last in, first out C. Last in, last out D. First in, first out 2. Which approach uses the distance between two point without using diagonals? A. Inadmissible approach B. Admissible approach C. Manhaltan distance D. Hamming distance 3. Which is not a search problem component? A. Middle state B. Path Cost C. Goal Cost D. Transition model 4. A recurrent layer is a layer that feeds what? A. independent chains B. unique copies of units C. output into itself D. input into itself 5. Which is not a type of machine learning? A. reinforced B. unsupervised C. supervised D. reiterative Answers: 1 (D), 2 (C), 3 (A), 4 (C), 5 (D)