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artificial intelligence (ai) course in chandigarh

Description

Artificial intelligence is one of the disruptive technologies that is revolutioning the mankind. In simple words, artificiail intelligence is the science of training machines and give them the ability to learn. Artificial intelligence machines have the ability to learn autonomously. Artificial intelligence machines interact with the enviornment and provide results accordingly. Machine learning and deep learning are sub fields of artificial intelligence. Let us have a brief introduction.

Machine Learning

Machine learning is the sub field of AI. Machine learning helps the software programs to be more accurate in predicting the outcomes. Machine learning gives the capability to machines so that they can work without being explicitly programmed. These machines have the ability to learn from past experience and learn from past experience.

Deep Learning

Deep learning is the sub branch of ML. Deep learning is based on human brain neural network. Deep learning neural networks helps the comuter to teach things which comes naturally to humans. Deep learning technology performs the classification directly from the text, images or any other type of data.

Let us have a look at some of the applications of Artificiail Intelligence: Artificial Intelligence is the future technolgy which is being used in every field of concern. We can see artificial intelligence working in healthcare, finance, stock market, agriculture, education and more. Artificial intelligence is used in weather forecasting, earthquake predictions and more. Technology giants like Google, Amazon, Tesla are using artificial intelligence technology. AI is being used in self driving cars, virtual assistants, recommender systems and more.

Why Artificial Intelligence Course?

Artificial intelligence is the future technology which will have a huge impact on the mankind. Currently, there is a huge shortage of artificial intelligence engineers in India and abroad. This is the reason why salaries of data scientists and artificial intelligence engineers are huge. If you are aspiring to get a carrer in artificial intelligence, DummyByte is the right choice for you. You can undergo 6 months industrial training in artificial intelligence from DummyByte.

Python Overview, Basic Syntax, Variables Type

Python Overview :Introduction, features.

Basic Syntax:Interactive mode programming, script mode programming, identifiers, line and indentation, quotation, comment and command line arguments in python.

Variables Type:Assigning value to a variable, multiple assignment, standard dataypes, number, string, list, tuple, dictionary, data type conversion.

Basic Operators, Decision Making, Python Loops

Basic Operators:Arithmetic operators, comparison operators, assignment operators, bitwise operators, logical operators, membership operators, identity operators.

Decision Making:Single statement suites.

Python Loops:Loops (while, for, nested), control statement of loops.

Number, String

Number (Number: int, long, float, complex):Assigning value to a number, delete the reference to a number, number type conversion, mathematical functions, random number functions, trigonometric functions, mathematical constants.

String:Accessing values in string, updating strings, escape characters, string special characters, string special operators, string formatting operator, triple code, unicode string, built in string methods.

List, Tuple, Dictionary

List: Basic list operations, indexes, accessing values in list, updating list, delete list elements.

Tuple: Basic tuple operations, indexing, accessing values in tuple, updating tuple, delete tuple element.

Dictionary: Accessing values in dictionary, updating dictionary, delete dictionary elements, list under dictionary, dictionary under list, sorting in dictionary.

Date and Time, Python Function

Date and Time:Tick, time tuple, current time, getting formatted time, getting calender.

Python Function: Defining a function, calling a function, overloading concept, function arguments, required arguments, keyword arguments, default arguments, variable length arguments, anonymous function, return statements, concept of variables.

Concept of oops

Concept of oops: Classes and objects, overview of oop terminology, creating classes, creating instance objects, accessing attributes, built in class attributes, destroying objects, class inheritance, overriding methods, overloading operators, data hiding, Encapsulation, data abstraction, polymorphism.

Module, Exception

Module : Import statements, from import, from import * statement, locating modules, PYTHONPATH variable, namespace and scoping, dir () function, reload() function, packages in python.

Exception:Exception handling, assert statement, except clause, try finally clause, argument of exception, raising exception, user defined exception.

Django, Introduction to Djano, Django’s design philosophies, advantages of Django

Django:Introduction to Djano, Django’s design philosophies, advantages of Django.

Django Overview:MVC Pattern used.

Django Project: Creating a Project, structure of project.

Apps Life Cycle, creating an Application.

Create Interface:Admin Interface

View:Creating Views, Simple View

URL Mapping, organizing your URLs, sending parameters to view.

Template System: Render function, Django template language (DTL), filters tags, block and extend tags.

Models: Creating a model, manipulating data (CRUD), other data manipulation, linking models

Page Redirection

Email: Sending a Simple E-mail, multiple mails with send_mass_mail, HTML e-mail, sending e-mail with attachment.

Generic views: static pages, list and display data from DB.

Form processing: Using form in a view, using our own form validation.

File uploading: uploading an image

Sessions: setting up sessions, actions using sessions

Machine Learning Languages, Types, and Examples
  1. Machine Learning vs Statistical Modelling
  2. Supervised vs Unsupervised Learning
  3. Supervised Learning Classification
  4. Unsupervised Learning
  5. Reinforcement Learning
Supervised Learning
  1. K-Nearest Neighbors
  2. Decision Trees
  3. Random Forests
  4. Reliability of Random Forests
  5. NAÏVE BAYES
  6. Support Vector Machine
  7. Ensemble models
  8. Regression Algorithms
  9. Model Evaluation
  10. Model Evaluation: Overfitting & Underfitting
  11. Understanding Different Evaluation Models
Unsupervised Learning
  1. K-Means Clustering plus Advantages & Disadvantages
  2. Hierarchical Clustering plus Advantages & Disadvantages
  3. Measuring the Distances Between Clusters - Single Linkage Clustering
  4. Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
  5. Density-Based Clustering
Feature Engineering and Featurization
  1. Introduction
  2. Types of Time Series
  3. Rolling Window Concept
  4. Understanding Time Series
  5. Feature Extraction from Time Series
  6. Multiple Time Series Feature Extraction
Deep Learning And Neural Networks
  1. Introduction
  2. Neural Network
  3. Recurrent Neural Network
  4. LSTM
  5. Deep RNN
  6. CNN
  7. Gated Reccurent Unit
  8. Artifical Neural Network
  9. Capsule Network
  10. Shallow Neural Networks
BONUS
  1. Overview of Natural Language Processing
  2. Introduction to Computer vision
  3. Introduction to Chatbots