Winsomesoft

 40 hours of Instructor Training Classes                            24/7 Support

 Lifetime Access to Recorded Sessions                              Practical Approach

 Real World use cases and Scenarios                                 Expert & Certified Trainers

Artificial intelligence is becoming increasingly relevant in the modern world where everything is driven by data and automation. It is used extensively across many fields such as image recognition, robotics, search engines, and self-driving cars. In this Artificial Intelligence training at Bangalore, we will explore various real-world scenarios. We will start by talking about various realms of artificial intelligence. We’ll then move on to discuss more complex algorithms, such as Extremely Random Forests, Hidden Markov Models, Genetic Algorithms, Artificial Neural Networks, and Convolutional Neural Networks, and so on.

This artificial intelligence course is for Python programmers looking to use artificial intelligence algorithms to create real-world applications. This artificial intelligence course is friendly to Python beginners, but familiarity with Python programming would certainly be helpful so you can play around with the code. It is also useful to experienced Python programmers who are looking to implement artificial intelligence techniques.

Learning Objectives for this Course:

This course covers foundational concepts and hands-on learning of leading machine learning tools, such as Python and TensorFlow.
Over the course of the 40 Hours, candidates will not only gain theoretical knowledge of machine learning tools, but also gain exposure to business perspectives and industry best practices through lectures, Practice sessions, Assignments and project submissions.

Software to be installed –

Anaconda – https://www.anaconda.com/download/

Machine Requirement:

Recommended – Machine with 4GB RAM, i3 or above quad-core processor

Requirement: Working Internet Connection throughout the training for participants.

Python Libraries used (Most of them are already available in Anaconda, others we will install during the training)

Overview of Artificial Intelligence

• Introduction
• Definition
• Intelligent agents

Representation and search State Space Search

• Information on State Space Search
• Graph theory on State Space Search
• Problem Solving through State Space Search
• Solution for State Space Search
• FSM
• BFS on Graph
• DFS algo
• DFS with iterative deepening
• backtracking algo
• trace backtracking on graph part 1
• trace backtracking on graph part 2
• Summary State Space Search

Representation and search Heuristic Search

• Heuristic Search Overview
• Heuristic Calculation technique part 1
• Heuristic Calculation technique part 2
• Simple hill climbing
• best first search algo
• tracing best first search 1
• best first search continue
• admissibility 1
• mini-max
• two ply min max
• alpha beta pruning

Machine Learning

• machine learning_overview
• perceptron learning
• perceptron with linearly separable
• backpropagation with multilayer neuron
• W for hidden node and back propagation algo
• backpropagation algorithm explained
• back propagation calculation_part01
• back propagation calculation_part02
• updation of weight and cluster
• k-means cluster‚NNalgo and application of machine learning

Logics and reasoning

• logics_reasoning_overview_propositional calculas part 1
• logics_reasoning_overview_propositional calculas part 2
• proportional calculus
• predicate calculus
• First order predicate calculus
• modus ponus‚tollens
• unification and deduction process
• resolution refutation
• resolution refutation in detail
• resolution refutation example-2 convert into clause
• resolution refutation example-2 apply refutation
• unification substitution and skolemization
• prolog overview_some part of reasoning
• model based and CBR reasoning

Rule based Programming

• production system
• trace of production system
• knight tour prob in chessboard
• Goal driven_data driven production system part _ 1
• Goal driven_data driven production system part _ 2
• goal driven Vs data driven and inserting and removing facts
• defining rules and commands
• CLIPS installation and clipstutorial 1
• CLIPS tutorial 2
• CLIPS tutorial 3
• CLIPS tutorial 4
• CLIPS tutorial 5_part01
• CLIPS tutorial 5_part02
• tutorial 6
• CLIPS tutorial 7
• CLIPS tutorial 8
• variable in pattern tutorial 9
• tutorial 10
• more on wildcardmatching_part01
• more on wildcardmatching_part02
• more on variables
• deffacts and deftemplates_part01
• deffacts and deftemplates_part02
• template indetail part1
• not operator
• forall and exists_part01
• forall and exists_part02
• truth and control
• tutorial 12

Decision Making

• intelligent agent
• simple reflex agent
• simple reflex agent with internal state
• goal based agent
• utility based agent
• basics of utility theory
• maximum expected utility
• decision theory and decision network
• reinforcement learning
• MDPand DDN

Stochastic methods

• basics of set theory part _ 1
• basics of set theory part _ 2
• probability distribution
• baysian rule for conditional probability
• examples of bayes theorm