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Artificial Intelligence

Artificial Intelligence is the development of computer systems that are able to perform task that normally requires human intelligence i.e. decision making, and translation of languages. Artificial intelligence is the recreation of human intelligence process. The process includes learning information and rules for using the information and subsequently has the benefit of it for the organization. Virtual personal assistant is another example of Artificial Intelligence. The course elaborates on the thing which mainly focuses on AI module. AI nowadays is being occupied by many companies in order to raise the productivity, and reduce the operational cost bear by the company. Therefore the main goal of AI is to include reasoning, deduction, knowledge representation and planning. Out of which planning plays and important part in the organization which is because it is a pervasive function and is applied all over. For All Modules the expert training session is provided by the eminent staff which is dedicated for training the aspirants within the stipulated time in IT Ridge Technologies.

Following are the prerequisites of the AI functions that is:
• Computer Science 1 and 2,
• Data Structures,
• Analysis of Algorithms,
• 3 Calculus courses,
• Discrete Math,
• Linear Algebra,
• Probability and Statistics,

Types of Artificial Intelligence:
1. Artificial narrow intelligence: (has a narrow-range of abilities) Artificial Narrow Intelligence is ability to imitate human intelligence to a narrow range of parameters and concepts.
2. Artificial General Intelligence: (is about as capable as a human) Artificial General Intelligence is human intelligence or behaviour that is in-distugnshiable from that of human.
3. Artificial Super Intelligence: (is more capable than a human) Artificial Super Intelligence is a copy human intelligence or behaviour but surpasses it. It would surpass all humans at all things: maths, writing books about Orcs & Hobbits, prescribing medicine and much, much more.

All the perspective candidates from different background who have to make the career in Artificial Intelligence and have domain experience either in retail, manufacturing, or sales can takeup this course. Therefore the segment to choose artificial intelligence as a developing tool is of main importance. It not only streamlined the process in the industry but it also helps us to uniform the complex situations emerging in the market for day to day activity. It is subsequently proven that the AI market share has risen and continuously it will not only transform the future of the industry. But subsequently it will generate employment opportunities for the mainstream candidate who believes in driving excellence and learning new skill which will enhance their career growth.

ITRidge, IT Ridge Technologies, itridgetech.com
    1. Advanced search
    2. Constraint satisfaction problems
    3. Knowledge representation and reasoning
      -Non-standard logics
      - Uncertain and probabilistic reasoning (Bayesian networks, fuzzy sets).
      - Foundations of semantic web: semantic networks and description logics.
    4. Rules systems: use and efficient implementation
    5. Planning systems
    1. Computational learning tasks for predictions, learning as function approximation, generalization concept.
    2. Linear models and Nearest-Neighbors (learning algorithms and properties, regularization)
    3. Neural Networks (MLP and deep models, SOM).
    4. Probabilistic graphical models.
    5. Principles of learning processes: elements of statistical learning theory, model validation.
    6. Support Vector Machines and kernel-based models.
    7. Introduction to applications and advanced models.
    1. Formal and statistical approaches to NLP. 2. Statis
    tical methods: Language Model, Hidden Markov Model, Viterbi Algorithm, Generative vs Discriminative
    3. Models 4. Linguistic essentials (tokenization, morphology, PoS, collocations, etc.).
    5. Parsing (constituency and dependency parsing).
    6. Processing Pipelines
    7. Lexical semantics: corpora, thesauri, gazetteers.
    8. Distributional Semantics: Word embedding’s, Character embedding’s.
    9. Deep Learning for natural language.
    10. Applications: Entity recognition, Entity linking, classification, summarization.
    11. Opinion mining, Sentiment Analysis
    12. Question answering, Language inference, Dialogic interfaces.
    13. Statistical Machine Translation.
    14. Statistical Machine Translation.
    15. LP libraries: NLTK, Theano, Tensorflow.
    1. Introduction to the course and to the case study Examples: a voice-activated ambient assistant to answer student queries about the logistics of lectures in a classroom building, or autonomous software for a robotic rover for exploring inaccessible environments
    2. Common designs for smart applications Examples: fuzzy logic in control systems or cloud analysis of field sensors data streams
    3. Make or buy: selecting appropriate procurement strategies Examples: writing your own RRN architecture vs. using cloud services
    4. Development platforms for smart objects Examples: Brillo (IoT devices) or Android TV (Smart TVs) -
    5. Development platforms for smart architectures Examples: TensorFlow (server-side RNNs), or the Face Recognition API (mobile)
    6. Cloud services for smart applications Examples: Google Cloud Machine Learning API, Google Cloud Vision API, Google Cloud Speech API, or Deploying Deep Neural Networks on Microsoft Azure GPU VMs
    7. Deployment and operations Examples: cloud hosting vs. device hosting, or harnessing user feedback to drive improvement, Measuring success: methods and metrics
    8. Examples: defining user engagement and satisfaction metrics, or assessing the naturalness of smart interactions.
    1. Multivariate and matrix calculus
    2. Matrix factorization, decomposition and approximatio
    3. Eigenvalue computation
    4. How to use Ethereum?
    5. Nonlinear optimization: theory and algorithms
    6. Nonlinear optimization: theory and algorithms
    7. MATLAB and other software tools (lab sessions with applications)
    1. Mechanics and kinematics of the robot
    2. Sensors for robotics
    3. Robot Control
    4. Architectures for controlling behaviour in robots
    5. Robotic Navigation
    6. Tactile Perception in humans and robots
    7. Vision in humans and robots
    8. Analysis of case studies of robotic systems
    9. Project laboratory: student work in the lab with robotic systems
    1. The architecture of the Web and the Semantic Web stack; URI.
    2. Resource Description Framework (RDF) and RDF Schema
    3. The query language SPARQL
    4. Linked data: creation of data sets from DB relations; access.
    5. Web Ontology Language (OWL): syntax and semantics
    6. Top ontologies: main definitions and examples (DOLCE and CRM)
    7. Specific ontologies, such as semantic sensor networks.
    8. Extraction of knowledge from KB’s (DBpedia, Freebase)
    9. Project consisting in the creation of ontologies (use of Protegé).
    1. (Timed and) Hybrid Automata: definition and simulation techniques
    2. Stochastic simulation methods (Gillespie’s algorithm and its variants)
    3. Hybrid simulation methods (stochastic/ODEs)
    4. Rule-based modeling
    5. Probabilistic/stochastic model checking: principles, applicability and tools
    6. Statistical model checking
    7. Process mining (basic notions)
    1. The Idea behind Exception
    2. Exceptions & Errors
    3. Types of Exception
    4. Control Flow In Exceptions
    5. Use of try, catch, finally, throw, throws in Exception Handling
    6. Statistical model checking
    7. Process mining (basic notions)

Download Course Content
Course Duration 5 Weeks
Faculty/Trainers 6+ Years of Experience in the field
Training Level Beginner to Advanced
Number of Lectures 60 lectures
Training Materials Will be provided
Certificate Yes
Mode of Training Corporate and Online
Placement Assistance Yes
Oppurtunity Level Entry level to Advanced