Artificial Intelligence (AI) Training

AI Deep Learning using Python

Duration: 3.5 Months Career Option: AI Programmer

We also offer online classes for trainees who cannot attend the classes physically

Artificial Intelligence (AI) Training in Nepal

Artificial Intelligence (AI), also referred to as machine intelligence, seems more and more like a portal to a revolution as each day goes by. The revolution, where Siri and Cortana might speak for human rights and civilization as well as serving advises which would best ours.

However, it is up to the generations to come and cultivate the seeds of powerful/efficient AI tools and techs. Broadway Infosys has launched the Artificial Intelligence Training in Nepal with the same vision in mind to empower Nepalese manpower and experts in the field. Profitable AI courses in Nepal are very limited. Broadway being one the very best IT establishments in the country will help you see through the weight. We run our classes with very trusted and professional experts, who provide guidance on theoretical and practical knowledge as well as share their experience and challenges in the work field. This course gives any individual an excellent view of developing artificial intelligence.

Benefits of Artificial Intelligence Training in Nepal

AI training in Nepal rewards an individual with the following talents

  • Provides the variety of scope and job roles such as computer programmer, Robotics, software engineer etc.
  • Applications of AI assets in fields like education, healthcare, finance, aviation and marketing etc.
  • Learn to create productive tools such as smart reply, autonomous driving, toxicity detection, mitosis detection etc.
  • Learn to understand tenser flow algorithms.
  • Provides depth knowledge about neural networks and robot locomotion.
  • Gives creativity and the intelligence a great boost.

Benefits of Artificial Intelligence Training at Broadway Infosys

One careful choice you make gives a bigger picture. So, here are the reasons why choosing Broadway is a careful choice.

  • A chance to learn from highly experienced professional experts.
  • Tools and technologies equipped are by far advanced.
  • Provides the course at the reasonable price
  • Scholarships available for deserving /students.
  • Guaranteed internship for deserving students after completing the training
  • Regular assignments are given to test the capability of a student.
  • Counseling is offered in order to keep students encouraged.
  • Opportunity to be a part of a friendly learning environment.
  • Project Work at the end of training session

Pre-requisites for Artificial Intelligence (AI) Training

Candidates aspiring to be AI developers are required to have knowledge of the algorithm and basic mathematics. Further, an individual having interest in modern technology has an additional advantage. Nonetheless, Broadway admires anybody who wants to have a career in deep learning and welcomes them to take the training.

Courses Outline :- Artificial Intelligence (AI) Training
  • Environment setup
  • The python programming language
  • What is program?
  • What is debugging?
  • Values and types
  • Variables
  • Variable names and keywords
  • Operators and operands
  • Expressions and statements
  • Interactive mode and script mode
  • Order of operations
  • String operations
  • Comments
  • Debugging
  • Function calls
  • Type conversion functions
  • Math functions
  • Composition
  • Adding new functions
  • Definitions and uses
  • Flow of execution
  • Parameters and arguments
  • Variables and parameters are local
  • Stack diagrams
  • Fruitful functions and void functions
  • Why functions?
  • Importing with from
  • Debugging
  • Modulus operator
  • Boolean expressions
  • Logical operators
  • Conditional execution
  • Alternative execution
  • Chained conditionals
  • Nested conditionals
  • Recursion
  • Stack diagrams for recursive functions
  • Infinite recursion
  • Keyboard input
  • Debugging
  • Return values
  • Incremental development
  • Composition
  • Boolean functions
  • More recursion
  • Leap of faith
  • Checking types
  • Debugging
  • Multiple assignments
  • Updating variables
  • The while statement
  • Break
  • Debugging
  • For loop
  • A string is a sequence
  • Len
  • Traversal with a for loop
  • String slices
  • Strings are immutable
  • Searching
  • Looping and counting
  • String methods
  • The in operator
  • String comparison
  • Debugging
  • A list is a sequence
  • Lists are mutable
  • Traversing a list
  • List operations
  • List slices
  • List methods
  • Map, filter and reduce
  • Deleting elements
  • Lists and strings
  • Objects and values
  • Aliasing
  • List arguments
  • Debugging
  • Dictionary as a set of counters
  • Looping and dictionaries
  • Reverse lookup
  • Dictionaries and lists
  • Memos
  • Global variables
  • Long integers
  • Debugging
  • Tuples are immutable
  • Tuple assignment
  • Tuples as return values
  • Variable-length argument tuples
  • Lists and tuples
  • Dictionaries and tuples
  • Comparing tuples
  • Sequences of sequences
  • Debugging
  • Usage
  • Union, Difference
  • Available methods
  • Introduction
  • Exceptions versus Syntax Errors
  • Raising an Exception
  • The AssertionError Exception
  • The try and except Block: Handling Exceptions
  • The else Clause
  • Persistence
  • Reading and writing
  • Format operator
  • Filenames and paths
  • Catching exceptions
  • Databases
  • Writing modules
  • Debugging
  • Introduction, Application and Usage
  • Reader and writer
  • DictReader and DictWriter
  • Simple CSV processing using functional programming
  • User-defined types
  • Attributes
  • Real World Example
  • Instances as return values
  • Objects are mutable
  • Copying
  • Debugging
  • Object-oriented features
  • The self
  • Printing objects
  • The init method
  • The __str__ method
  • Other special methods
  • Operator overloading
  • Type-based dispatch
  • Polymorphism
  • @staticmethod
  • Debugging
  • Introduction
  • Checking callable or not
  • Decorators
  • Creating and using decorators
  • Introduction
  • Example
  • Class attributes
  • Private, Protected and Public
  • Multiple Inheritance
  • Class diagrams
  • Debugging
  • Data encapsulation
  • Installing Git
  • status, log, commit push, pull commands 
  • Branch, Tags and Multiple remote concept and Implementation
  • checkout, reset, rebase, merge concept
  • Gitlab vs Github vs Bitbucket
  • Trello, Slack, Jira
  • Web Scraping project (includes handling web scraping tools, proper file handling and implementation of sql)
  • GUI project (any desktop application e.g: calculator, data entry application)
  • Introduction to Google co-lab and Anaconda
  • Numpy
  • Pandas
  • Plotting and Charting

Project1: Mini Project

  • Tensorflow
  • Pytorch
  • Keras
  • Theano
  • Computer Vision Overview
  • Image Formation
  • History
  • Introduction to Open CV
  • Different Types of Filter
  • Feature Detection
  • Edge Detection
  • Haar-like Features
  • Frequency Domain Analysis 

Project2: Face Detection

  • Introduction to Deep Learning
  • History
  • Why Deep Learning Taking Off
  • Building Block of deep Learning
  • Application of Deep Learning
  • Multilayer Perceptron
  • Back Propagation
  • Working Of neural network
  • Adjusting the weights
  • Gradient Descent
  • Stochastic Gradient Descent
  • Foundations of Convolutional Neural Network
  • CNN Architecture
  • Convolution Operation
  • ReLU Layer
  • Pooling
  • Flattening
  • Full Connection
  • Softmax & Cross-Entropy
  • Summary
  • Get the dataset
  • Importing the Libraries
  • Importing the Dataset
  • Splitting the Dataset into the Training set and Test set  Feature Engineering
  • Traditional Computer Vision
  • Image classification using Deep Learning
  • Binary and Multi class Image classification
  • Deep learning in Object Detection  SSD,YOLO

Project3: Binary and Multi Image Classification

Project4: Object Recognition

  • The Idea Behind GANs
    • How Do GANs Work?
  • Generator
  • Discriminator
  • Generative Adversarial Networks Representation
  • Mathematical Details About GANs
    • Applications of GANs
  • Current Research On GAN

Project 5: Image Creation with GAN

  • NLP Overview
  • Use of NLP
  • Library and Frameworks
  • Why NLP is difficult?
  • Spacy Basics
  • Tokenization
  • Stemming
  • Lemmatization
  • Stop Words
    • Word segmentation
  • Part-of-speech tagging
  • Recurrent Neural Network(RNN)
  • Encoder
  • Decoder
  • LSTM
  • Attention
  • Sequence to Sequence Models
  • Sequence to Sequence Models Architecture
  • Current research on NLP

Project6: Text Classification

Project7: Sentiment Analysis

  • Speech Recognition
  • Natural Language Understanding
  • Natural Language Generation
  • Attention Mechanism
  • Foundations of Reinforcement Learning
  • Policy-Based Methods
  • Value-Based Methods
  • Policy-Based Methods
  • Deep Q-Learning
  • Applications
  • Why GPU is needed?
  • Google Cloud Platform
  • Amazon Web Serves
  • Floyd Hub
  • GPU Hardware
  • Alpha Go
  • Self-Driving Car
  • IBM Watson
  • Amazon Alexa
  • Face Book Artificial Intelligence
  • Google Deep Mind
  • Project 1: Mini Project
  • Project 2: Face Detection
  • Project 3: Binary and Multi Image Classification
  • Project 4: Object Recognition
  • Project 5: Image Creation with GAN
  • Project 6: Text Classification
  • Project 7: Sentiment Analysis
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I've been a part of this institute for the last 4 years, and this institute has helped me to boost my career. Well, the computer lab is very well-equipped, and most of the mentors come from top ... Read More