Artificial Intelligence (AI) Training

Artificial Intelligence (AI) Training

AI, Deep Learning

Duration: 3 Months
Career: AI Programmer
Training Mode: Both, Physical & Live Online Classes
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Artificial Intelligence (AI) also termed as machine intelligence deals with building of smart machines that has the capacity to perform complicated tasks that normally requires human interference and intelligence.

Artificial Intelligence training refers to the process of teaching AI systems how to perform specific tasks. This involves feeding large amounts of data into the AI system and using algorithms to analyze and learn from that data.

It is accomplished by studying the human brain patterns that results in the development of intelligent software and systems. 

Broadway Infosys offers fully career-oriented Artificial Intelligence (AI) Training in Nepal to help students and professionals acquire skills in computer programming, Robotics, software engineering. 

It is one of the fastest growing fields in computer science industry and is highly beneficial for students in improving their placement prospects. Our Experts provide depth knowledge and guidance on both theoretical and practical basis.


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

Artificial Intelligence (AI) Training in Nepal - Outlines
    Artificial Intelligence
  • Introduction and installation

    • Introduction
    • Supervised vs. Unsupervised Learning
    • Installing Anaconda and Managing Environment
    • Familiarization with Datasets
    • Numpy
    • Scikit Learn
    • Matplotlib
    • Pandas
  • Data Preprocessing

    • Importing the Dataset
    • DataFrame Data Structure
    • Missing Data
    • Querying a DataFrame
    • Manipulating DataFrame
    • Splitting the Dataset into the Training set and Test set
    • Feature Scaling
  • Linear Regression

    • Gradient descent
    • Correlation Analysis and Feature Selection
    • Observe Model Performance
    • Multiple Regression and Feature Importance
    • Cross validation
    • Linear regression implementation
  • Logistic regression

    • Cross validation
    • Confusion Matrix, Precision, Recall and F1 Score
    • Precision and Recall Tradeoff
    • The ROC Curve
  • Support Vector Machine (SVM)

    • Introduction
    • Classification using SVM
    • Support Vector Regression
  • K-nearest neighbor

    • Introduction
    • Lazy learning
    • Euclidean-distance
    • Implementation of K-nearest neighbor
  • Naive Bayes classifier

    • Introduction
    • Implementation of Naive Bayes
    • Classification applications
  • Decision trees

    • Introduction
    • Entropy
    • Implementation
  • Time Series Modeling

    • Overview of Time Series Modeling
    • Time Series Pattern
    • White Noise
    • Stationarity
    • Removal of Non-Stationarity
    • Steps in Time Series Forecasting
    • Examples
  • Text Mining

    • Overview of Text Mining
    • Applications of Text Mining
    • Natural Language Toolkit Library
    • Text Extraction and Preprocessing
    • Tokenization
    • N-grams
    • Stop Word Removal
    • Stemming
    • Lemmatization
    • POS Tagging
    • Named Entity Recognition
  • Recommender Systems

    • Introduction
    • Purposes of Recommender Systems
    • Collaborative Filtering
    • Content based Filtering
    • Hybrid Recommender System
    • User-Movie Recommendation Model
    • Movie-Movie recommendation
  • Deep Learning in Python
  • Introduction and Prerequisite

    • Introduction to Google co-lab and Anaconda
    • Numpy
    • Pandas
    • Plotting and Charting
  • Math for Deep Learning

    • Matrix
    • Calculus
    • Linear Algebra
    • Probability and Statistics
  • Frameworks

    • Tensorflow
    • Pytorch
    • Keras
    • Theano
  • Introduction to Computer Vision

    • Computer Vision Overview
    • Image Formation
    • History
    • Image Processing and Feature Detection
    • Introduction to Open CV
    • Different Types of Filter
    • Feature Detection
    • Edge Detection
    • Haar-like Features
    • Frequency Domain Analysis
  • Introduction to Deep Learning

    • History
    • Why Deep Learning Taking Off
    • Building Block of deep Learning
    • Application of Deep Learning
  • Artificial Neural Networks

    • Multilayer Perceptron
    • Back Propagation
    • Working Of neural network
    • Adjusting the weights
    • Gradient Descent
    • Stochastic Gradient Descent
  • Convolution Neural Network

    • Foundations of Convolutional Neural Network
    • CNN Architecture
    • Convolution Operation
    • ReLU Layer
    • Pooling
    • Flattening
    • Full Connection
    • Softmax & Cross-Entropy
    • Summary
  • Data Preprocessing

    • Get the dataset
    • Importing the Libraries
    • Importing the Dataset
    • Feature Engineering
    • Splitting the Dataset into the Training set and Test set
  • Image Classification and Object Recognition

    • Traditional Computer Vision
    • Image classification using Deep Learning
    • Binary and Multi class Image classification
    • Deep learning in Object Detection (SSD, YOLO)
    • Custom Object recognition Training
  • Introduction to Natural Language Processing

    • NLP Overview
    • Use of NLP
    • Library and Frameworks
    • Why NLP is difficult?
  • Natural Language Processing Basics

    • Spacy Basics
    • Tokenization
    • Stemming
    • Lemmatization
    • Stop Words
    • Word segmentation
    • Part-of-speech tagging
    • Name Entity Recognition
    • Deep Learning for NLP
  • Recurrent Neural Network (RNN)

    • Encoder
    • Decoder
    • LSTM
    • Attention
    • Sequence to Sequence Models
    • Sequence to Sequence Models Architecture
    • Current research on NLP
  • Use case of Natural Language Processing

    • Speech Recognition
    • Natural Language Understanding
    • Natural Language Generation
    • Attention Mechanism
    • BERT
    • Transformers
    • GPT
  • Autoencoders

    • Learning Objectives
    • Intro to Autoencoders
    • Autoencoder Structure
    • Autoencoders
  • Deep Reinforcement Learning

    • Foundations of Reinforcement Learning
    • Policy-Based Methods
    • Value-Based Methods
    • Policy-Based Methods
    • Deep Q-Learning
    • Applications
  • Project Works

    • Project 1: Face Detection
    • Project 2: Binary and Multi Image Classification
    • Project 3: Object Recognition
    • Project 4: Name Entity Recognition for Resumes
    • Project 5: Text Classification
    • Project 6: Sentiment Analysis
Upcoming Class Schedule
28 Jul 2024 08:00 AM - 09:30 AM

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