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TECHINAUT

B LEVEL

4.5 (15)
NIELIT

Artificial Intelligence and Neural Networks

BE2-R4- B level (NIELIT)

A neural network's basic structure comprises an input layer, a hidden layer, and an output layer.Learning about neural networks or want to create your algorithms, it is essential to have a solid grasp of AI and machine learning.

Artificial Intelligence

11 Lessons

Artificial Intelligence

120 Hours - (Theory: 48 hrs + Practical: 72 hrs)

Artificial Intelligence

100+ students enrolled

Overview
Course Description

This course will give students an understanding of the core techniques of artificial intelligence, how they are applied, and the fundamental challenges of this field. It will also introduce students to various problems related to the area. It is intended for students who want to pursue a career in computer science.

What you'll learn
  • How to apply deep learning to solve complex problems.
  • A course in AI will equip you with the knowledge and skills to tackle real-world problems and future-proof your career.
  • There are several different approaches to AI and neural networks.
  • Learn about supervised and unsupervised learning and how to apply these techniques.
  • The Deep Learning specialization will teach you about the latest developments in this field.
  • This course will give you a solid understanding of recent developments and the tools to build your machine-learning models.
Course Content

  • Artificial IntelligenceLecture 1.1 Natural & Artificial Intelligence, Definitions of AI

    Artificial Intelligence 02:53

  • Artificial IntelligenceLecture 1.2 Nature of AI Solutions, Testing Intelligence

    Artificial Intelligence 02:53

  • Artificial IntelligenceLecture 1.3 AI Techniques, Testing Intelligence

    Artificial Intelligence 02:53

  • Artificial IntelligenceLecture 1.4 Data Pyramid, Computer Based Information Systems in the Pyramid

    Artificial Intelligence 02:53

  • Artificial IntelligenceLecture 2.1 Problems and Problem Spaces

    Artificial Intelligence 02:53

  • Artificial IntelligenceLecture 2.2 Problem Characteristics, Production Systems

    Artificial Intelligence 02:53

  • Artificial IntelligenceLecture 2.3 Control Strategies

    Artificial Intelligence 02:53

  • Artificial IntelligenceLecture 2.4 Exhaustive Searches and Blind Methods

    Artificial Intelligence 02:53

  • Artificial IntelligenceLecture 3.1 Introduction and Characteristics of Weak Methods

    Artificial Intelligence and Problem solving 02:53

  • Artificial IntelligenceLecture 3.2 Heuristic Search Techniques, Generate and Test

    Artificial Intelligence and Problem solving 02:53

  • Artificial IntelligenceLecture 3.3 Hill Climbing, Branch and Bound technique

    Artificial Intelligence and Problem solving 02:53

  • Artificial IntelligenceLecture 3.4 Best First Search and A* Algorithm

    Artificial Intelligence and Problem solving 02:53

  • Artificial IntelligenceLecture 3.5 Problem Reduction, AND / OR graphs

    Artificial Intelligence and Problem solving 02:53

  • Artificial Intelligence and Problem solvingLecture 4.1 Knowledge Representation (KR)

    Problem solving 02:53

  • Artificial Intelligence and Problem solvingLecture 4.2 Using Predicate logic

    Problem solving 02:53

  • Artificial Intelligence and Problem solvingLecture 5.1 Knowledge Based Systems (KBS) Architecture

    Artificial Intelligence and Problem solving 02:53

  • Problem solvingLecture 5.2 Difficulties with KBS Development Process

    Artificial Intelligence and Problem solving 02:53

  • Problem solvingLecture 5.3 Knowledge Acquisition, Knowledge Update

    Artificial Intelligence and Problem solving 02:53

  • Problem solvingLecture 6.1 Crisp and Fuzzy Logic

    Artificial Intelligence and Problem solving 02:53

  • Problem solvingLecture 6.2 Fuzzy Membership Functions

    Artificial Intelligence and Problem solving 02:53

  • Problem solvingLecture 6.3 Fuzzy Rule Based Systems

    Artificial Intelligence and Problem solving 02:53

  • Problem solvingLecture 6.4 Probability and Bayes’ Theorem

    Artificial Intelligence and Problem solving 02:53

  • Problem solvingLecture 6.5 Certainty factors, Dempster-Shafer theory

    Artificial Intelligence and Problem solving 02:53

  • Problem solvingLecture 6.6 Non Monotonic Reasoning and Truth Monitoring Systems

    Artificial Intelligence and Problem solving 02:53

  • AI ProgrammingLecture 7.1 Introduction to AI Languages like LISP, CLISP

    AI Programming 02:53

  • AI ProgrammingLecture 7.2 Introduction to PROLOG Programming

    AI Programming 02:53

  • AI ProgrammingLecture 8.1 Introduction to Natural Language Processing

    AI Programming 02:53

  • AI ProgrammingLecture 8.2 Syntactic Processing, Semantic Analysis

    AI Programming 02:53

  • Lecture 8.3 Parsing techniques, Context –free grammar

    AI Programming 02:53

  • Lecture 8.4 Recursive Transitions Nets

    AI Programming 02:53

  • AI ProgrammingLecture 9.1 Introduction to Neural Computing and Artificial Neural Network

    AI Programming 02:53

  • AI ProgrammingLecture 9.2 Fundamental Concepts

    AI Programming 02:53

  • AI ProgrammingLecture 10.1 Hopfield Model, Parallel Relaxation

    AI Programming 02:53

  • AI ProgrammingLecture 10.2 Perceptron, Lineraly Separable Problems

    AI Programming 02:53

  • AI ProgrammingLecture 10.3 Multi layer Perceptron, Non-Lineraly Separable Problems

    AI Programming 02:53

  • AI ProgrammingLecture 10.4 Self Organizing Networks: Kohonens Networks

    AI Programming 02:53

  • Paradigms in ArtificialLecture 11.1 Objectives of Learning, Hebb’s Rule

    Paradigms in Artificial 02:53

  • AI ProgrammingLecture 11.2 Delta Rule, Supervised Learning

    Paradigms in Artificial 02:53

  • AI ProgrammingLecture 11.3 Unsupervised Learning

    Paradigms in Artificial 02:53

  • AI ProgrammingLecture 10.4 Self Organizing Networks: Kohonens Networks

    Paradigms in Artificial 02:53
About Us
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TECHINAUT

B LEVEL

4.5 Instructor Rating
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100+ Courses

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Techinaut

45000+ students enrolled

UI/UX Designer, with 7+ Years Experience. Guarantee of High Quality Work.

Skills: Web Design, UI Design, UX/UI Design, Mobile Design, User Interface Design, Sketch, Photoshop, GUI, Html, Css, Grid Systems, Typography, Minimal, Template, English, Bootstrap, Responsive Web Design, Pixel Perfect, Graphic Design, Corporate, Creative, Flat, Luxury and much more.

Available for:

  • 1. Full Time Office Work
  • 2. Remote Work
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  • 4. Contract
  • 5. Worldwide
Reviews
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Nicole Brown

UX/UI Designer

4.5 Instructor Rating

“ This is the second Photoshop course I have completed with Cristian. Worth every penny and recommend it highly. To get the most out of this course, its best to to take the Beginner to Advanced course first. The sound and video quality is of a good standard. Thank you Cristian. “

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  • Techinaut Course 120 hours on-demand video
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  • Techinaut Course Enrolled: 100+ students
  • Techinaut Course Duration: 120 hours
  • Techinaut Course Chapters: 9
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  • Techinaut Course Level: Beginner