ARTIFICIAL INTELLIGENCE TRAINING HUBLI DHARWAD IN
“Artificial Intelligence Training Hubli Dharwad” | “Artificial Intelligence Course Hubli Dharwad” | Artificial intelligence (AI) is the simulation of human intelligence through machines & mostly through computer systems. Artificial Intelligence Course is a subfield of the computer. It allows computers to do things which are normally done by human beings. Any program can be said to be Artificial intelligence if it is able to do something that the humans do it using their intelligence through AI Programming Language. AI is a broad topic ranging from simple calculators to self-steering technology to something that might radically change the future. Learn Artificial Intelligence Master Program Hubli Dharwad by the industry experts, the program is conducted by Mildaintrainings.
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Learn Artificial Intelligence
Learn Artificial Intelligence
What is Artificial Intelligence?
Artificial intelligence (AI) is the simulation of human intelligence through machines & mostly through computer systems. Artificial Intelligence is a sub field of computer. It allows computers to do things which are normally done by human beings. Any program can be said to be Artificial intelligence if it is able to do something that the humans do it using their intelligence. In other words, Artificial Intelligence means the power of a machine to copy the human intelligent behavior. It is all about designing machines that can think.
Obviously, there is a lot more to it. AI is a broad topic ranging from simple calculators to self-steering technology to something that might radically change the future.
Applications of Artificial Intelligence (AI):
The main goals of AI include deduction & reasoning, knowledge representation, planning, natural language processing (NLP), learning, perception, & the ability to manipulate & moving objects. Long-term goals of AI research include achieving Creativity, Social Intelligence, and General (human level) Intelligence.
Artificial Intelligence (AI) has mainly influenced different sectors that we may not identify. Ray Kurzweil says “Many thousands of AI applications are deeply embedded in the infrastructure of every industry.” John McCarthy, one of the founders of AI, once said that “as soon as it works, no one calls it AI anymore.” Broadly, AI is classified into the following:
- Machine learning
- Neural network
- Deep learning
Why is Artificial Intelligence used?
Artificial Intelligence has been used in wide range of fields these days. Examples are medical diagnosis, robots, remote sensing, etc. Artificial intelligence is around us in many ways but we do not realize it. Smart cars can be an example of Artificial Intelligence & what it does?
These cars are working on self-driving, in the self-driving car you can read the newspaper while you are heading towards your office and also does some other important works. It will help you in saving your precious time, learn artificial intelligence today for better tomorrow
- Greater precision & accuracy can be achieved through AI
- These machines do not get affected by the planetary environment or atmosphere
- Robots can be programmed to do the works which are a bit difficult for human beings to do
- Artificial Intelligence (AI) will open up doors to new technological breakthroughs
- As they are machines they don’t stop for sleep or food or rest. They just need some source of energy to work
- Fraud detection becomes easier with Artificial Intelligence
- Using AI the time-consuming tasks can be done more efficiently
Where is Artificial Intelligence used?
Some of the top MNCs that are using Artificial Intelligence (AI)
Five best AI companies to work for in 2018 based on Glassdoor Research and % of employees who would recommend this company to a friend as today. These five companies together have 96 open AI positions today or 18.7% of all open AI jobs on Glassdoor.
Artificial Intelligence Training Course Objectives
After completion of AI course you will be able to:
- Identify potential areas of applications of AI
- Basic ideas & techniques in the design of intelligent computer systems
- Statistical & decision–theoretic modeling paradigm
- How to build agents that exhibit reasoning & learning
- Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
Pre requisites for taking Artificial Intelligence (AI) Training course:
The topics included in this topic will be related to probability theorem and linear algebra. So a basic knowledge in statistics and mathematics is an added advantage to take up this course. Technical background is a must.
Target Audience for this course Artificial Intelligence Training (AI) course:
The target audience for this course includes students and professionals who are interested in learning robotics & biometrics. This course is also meant for people who are very keen about learning Artificial Intelligence.
The avg. annual base pay for an Artificial Intelligence (AI) job listed on Glassdoor is around $111,118 PA. There are around 512 AI jobs on Glassdoor today, with AI Software Engineer, AI Data Scientist, AI Software Development Engineer and AI Research Scientist having a combined total of 118 open positions.
Around 67% of all AI jobs listed on Glassdoor are located in San Jose, San Francisco, Seattle, Los Angeles and New York City, USA. Some of the countries working great in the field of AI are Argentina, Australia, Brazil, Canada, Egypt, Ethiopia, Ghana, India, Iran, Kenya, Malaysia, Mexico, New Zealand, Nigeria, Pakistan, Peru, Russia, Saudi Arabia, South Africa, Zimbabwe etc.
AI technologies you will know after doing Artificial Intelligence Training:
Based on Forrester’s analysis, here’s is a list of the ten hottest AI technologies:
1. Natural Language Generation
2. Speech Recognition
3. Virtual Agents
4. Machine Learning Platforms
5. AI-optimized Hardware
6. Decision Management
7. Deep Learning Platforms
9. Robotic Process Automation
10. Text Analytics and NLP
AI Programming Language and Tools
AI Programming Language and Tools
Advanced Analytics Tools
- Apache Spark
Artificial Intelligence Tools
Learn Artificial Intelligence AI Course
Learn Artificial Intelligence AI Course
1. Overview of Artificial Intelligence
- History of artificial intelligence
- Detailed explanation of Artificial intelligence with a definition and meaning.
- Why artificial intelligence is important in today’s world?
- What is involved in artificial intelligence?
- The academic disciplines which are related to artificial intelligence.
- What is intelligent agents?
- Agents and environment
- Concept of rationality
- Types of agents – Generic agent, Autonomous agent, Reflex agent, Goal Based Agent, Utility based agent
2. Representation and Search : State Space Search
- Introduction to State Space Search in artificial intelligence, representation.
- Components of search systems.
- The areas where state space search is used.
Graph theory on state space search
- What is a graph theory?
- How may graph theory be used to model problem solving as a search through a graph of problem states?
- The And-Or graph is explained with its uses.
- Introduction on components of the graph theory.
Problem-Solving through state space search
- General Problem, Variants, types of problem-solving approach is explained.
- Depth First Search searches deeper into the problem space.
- Advantages, disadvantages and algorithm of depth first search.
DFS with iterative deepening (DFID)
- What iterative deepening search?
- Combination of breadth first search and depth first search.
- Its properties & algorithm along with examples.
- What is backtracking?
- Implementation of Artificial Intelligence.
- Description of the methods
- When backtracking can be used?
- For what applications backtracking algorithm can be used.?
3. Representation and Search: Heuristic Search
- Heuristic search: Rule of thumb
- Heuristic search: Search strategies.
- The general meaning and the technical meaning of Heuristic search.
- Heuristic search: Function of the nodes and the goals.
- Heuristic search techniques: Pure Heuristic Search
- Heuristic search techniques: A* Algorithm
- Heuristic search techniques: Iterative- Deepening A*
- Heuristic search techniques: Depth First Branch and Bound
- Heuristic search techniques: Heuristic Path Algorithm
- Heuristic search techniques: Recursive Best-First Search
function of the nodes and the goals.
Simple hill climbing
- Simple Hill Climbing technique in Heuristic search.
- Function optimization of hill climbing.
- Problems with simple hill climbing and its example.
Best-first search algorithm
- Combined advantages of breadth first and depth first searches.
- What is admissibility?
- Heuristic, its formulation, construction
- Admissible heuristic using a puzzle problem.
- How to estimate the cost to reach the goal state?
- Introduction to the Min-Max algorithm.
- Explanation of the two players MIN and MAX.
- Use of Min-Max Algorithm in two-player games such as Chess and others.
- Introduction to search trees.
- Speeding the algorithm
- Adding alpha beta cut-offs
- The Alpha value of the node.
- The beta value of the node.
- Improvements over minimax algorithm.
- Pseudo code and a detailed game example.
4. Machine Learning
- Introduction about the Machine learning.
- History of machine learning,
- Types of problems and tasks in machine learning and its algorithms.
Perceptron learning and Neural networks
- What is a learning rule?
- How to develop the perceptron learning rule?
- Advantages and disadvantages of the perceptron rule.
- The model of perceptron learning with theory and examples.
The types of neural networks
- single layer perceptron network and multi-layer neuron network
- The perceptron network architecture
- Steps for constructing learning rules
- Linear separable problem
- Back propagation algorithm and learning rule in multi-layer perceptron
- How to calculate back propagation algorithm
Updation of weight
- The weight matrix of perceptron.
- Learning of processing elements related to weight.
- Modeling approaches: Centroid-based.
- Modeling approaches: Hierarchical.
- Class of problem.
- Class of methods.
- Cluster algorithm: k-Means
- Cluster algorithm: k-Medians
- Cluster algorithm: Expectation Maximisation
- Cluster algorithm: Hierarchical clustering
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5. Logic and Reasoning
- Facts about logics in artificial intelligence.
- Why it is useful?
- The arguments and its logical meanings.
- Proof theory.
- Theorems, semantics, models and arguments.
First Order Predicate calculus (FOPC)
- Predicate calculus: Variables and Constants.
- Formula for FOPC
Modus ponens and Modus tollens
- Conditional statement and the affirmation of the antecedent of the conditional statement.
Unification and deduction process
- Unification algorithm.
- Expressions and transactions.
- Resolution rules – meaning, propositional and example
- Power of false and other examples
- what is Skolemization?
- How Skolemization works?
- Uses of Skolemization
- Skolem theories
6. Rule Based Programming
- What is production system?
- Components of AI production system.
- Four classes of production system.
- Advantages and disadvantages of production system.
- Rules and commands of production system.
- Data driven search.
- Goal driven search.
- Its differences.
CLIPS installation and CLISP Training/Tutorial (ai programing language)
- What is CLIPS?
- What are expert systems?
- History of CLIPS
- Facts and Rules
- Components of CLIPS
- Variables and Pattern matching
- Defining classes and instances
- Wildcard matching
- Field constraints
- Mathematical operators
- Truth and control tutorial
7. Decision Making
- Generic agent.
- Autonomous agent.
- Reflex agent.
- Goal based agent.
- Utility based agent.
- Utility functions.
- Maximize expected utility.
- Basis of utility theory.
- Six axioms of utility theory.
- Introduction to decision theory.
- Perspectives and disciplines of decision science.
- A few different decision theory also explained
- Graphical representation of a decision problem.
- why reinforcement learning?
- How does it work?
- What are the motivations?
- What technology is used?
- Who uses it?
- Where can the reinforcement learning be applied?
- The limitations of reinforcement learning.
Markov Decision Processes (MDP)
- Dynamic programming.
- Linear programming.
Dynamic Decision Networks (DDN)
- DDN is a feature based extension of MDP.
8. Stochastic methods
- Importance of set theory.
- What is a set.
- Set notation.
- Well defined sets.
- Number sets.
- Set equality.
- Cardinality of a set.
- Subsets and proper subsets and finally power sets
- Basic concepts.
- Joint probability distribution.
Bayesian rule for conditional probability
- What is Bayes’ theorem
- How to calculate conditional probability using Bayes’ theorem?
FAQ | Artificial Intelligence Training Hubali-Dharwad
What are the prerequisites for learning Artificial Intelligence Training Course?
- Fundamentals of Python programming
- Basic knowledge of statistics
- Basic machine learning knowledge will be added advantage
If you don’t know about python, statistics, machine learning etc. don’t worry we will guide you so that you can Learn AI Programming Langage and statistics, it will take hardly 15-20 hours.
Who should pursue this Artificial Intelligence course?
With the huge demand for AI in industries, Mildaintrainings’s AI course is well suited for a variety of roles and disciplines, including:
- People aspiring to be an ‘Artificial Intelligence Scientist’ or Machine Learning engineers
- Lead Analytics Managers of analysts team
- Information Architects
- Analytics professionals
- Graduates looking to build a career in Artificial Intelligence and machine learning
- Experienced professionals who would like to harness Artificial Intelligence in their fields to get more insight
What is the course duration?
The duration of Artificial Intelligence training course will be 32-40 hours. At Mildaintraings we will cover basic of all courses, and if you want to gain in-depth knowledge than you will have to learn below-mentioned courses separately:
Why should I take bootcamp/training from Mildaintraining?
One must take Artificial Intelligence training from Mildaintrainings because our trainers are having more than 10 years of industry practical training experience & also we at Mildain Training providing six (6) months technical support and try to solve all the queries.
Who are the instructors and how are they selected?
Our highly qualified trainers are industry experts with several years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us.
If I need to cancel my enrollment, can I get a refund?
Yes, you can cancel your enrollment if necessary prior to 3rd session i.e first two sessions will be for your evaluation. We will refund the full amount without deducting any fee for more details check our Refund Policy
How do I enroll for the AI Engineering course?
You can enroll for this training on our website or you can fill the form on our website and make an online payment using any of the following options:
- Visa Credit or Debit Card
- National Electronic Funds Transfer (NEFT)
Once payment is received you will automatically receive a payment receipt and access information via email.
What are the objectives of Artificial Intelligence course and what can I expect?
- Access to exclusive forums, moderated by expert faculty and industry thought leaders
- 15+ In-Demand Skills & Tools
- 10+ Real-Life Projects
- Mildaintrainings Job Assistance/Guidance
Who will provide the certificate?
At Mildaintrainings you will be provided with participation certificate after successful completion (*course, test and projects) of Artificial Intelligence course from Mildaintrainings.
When will the classes be held?
Classes will be held in weekend & weekdays accordingly, for one to one sessions you have to tell your dates prior, in order to book next dates.
What if I miss the class?
Don’t worry at all, if you’ll miss the class, in that case, we will accommodate you for backup classes by adjusting you in next live session of the same course.
How can I learn more about this training program?
Contact us using the chat option on the bottom-right of any page on the Mildaintraining website, or you can fill the Contact Us form and post your queries. Our customer service representatives can provide you with more details. We can also arrange one to one call or meeting with the trainer if you need.
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