Python with Artificial Intelligence (AI) Training

Python with Artificial Intelligence (AI) Training

AI, ML, and Deep Learning using Python

Duration: 4 Months ( 145 Hrs. )
Career: AI Programmer
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Description

Python with 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

Python with Artificial Intelligence (AI) Training - Outlines
    Python Programming Language
  • Basic Syntaxes

    • Environment setup
    • The python programming language
    • What is program?
    • What is debugging?
  • Variables, Expressions and Statements

    • 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
  • Functions

    • 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
  • Conditions and Recursion

    • 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
  • Fruitful Functions

    • Return values
    • Incremental development
    • Composition
    • Boolean functions
    • More recursion
    • Leap of faith
    • Checking types
    • Debugging
  • Iteration

    • Multiple assignments
    • Updating variables
    • The while statement
    • Break
    • Debugging
    • For loop
  • String

    • 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
  • Lists

    • 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

     

  • Dictionaries

    • Dictionary as a set of counters
    • Looping and dictionaries
    • Reverse lookup
    • Dictionaries and lists
    • Memos
    • Global variables
    • Long integers
    • Debugging
  • Tuples

    • 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
  • Set

    • Usage
    • Union, Difference
    • Available methods
  • Exception Handling

    • Introduction
    • Exceptions versus Syntax Errors
    • Raising an Exception
    • The AssertionError Exception
    • The try and except Block: Handling Exceptions
    • The else Clause       
  • Files

    • Persistence
    • Reading and writing
    • Format operator
    • Filenames and paths
    • Catching exceptions
    • Databases
    • Writing modules
    • Debugging
  • CSV

    • Introduction, Application and Usage
    • reader and writer
    • DictReader and DictWriter
    • Simple CSV processing using functional programming
  • Pandas

    • Introduction to Pandas
    • DataFrame Data Structure
    • DataFrame Indexing and Loading
    • Querying a DataFrame
    • Indexing Dataframes
    • Manipulating DataFrame
  • Database with Python

    • Installations
    • Introduction, Application and Usage
    • Basic Structured Query Language
    • CRUID operations
  • Basic Data Visualization

    • Principles of Information Visualization
    • Visualizing Data Using Spreadsheets
    • Matplotlib
    • Plotly
    • Scatterplots
    • Line Plots
    • Bar Charts
    • Histograms
    • Plotting with Pandas
  • Classes and Objects

    • User-defined types
    • Attributes
    • Real World Example
    • Instances as return values
    • Objects are mutable
    • Copying
    • Debugging
  • Classes and Methods

    • Object-oriented features
    • The self
    • Printing objects
    • The init method
    • The __str__ method
    • Other special methods
    • Operator overloading
    • Type-based dispatch
    • Polymorphism
    • @staticmethod
    • Debugging
  • Callable and Non-Callable Object

    • Introduction
    • Checking callable or not
    • Decorators
    • Creating and using decorators
  • Inheritance

    • Introduction
    • Example
    • Class attributes
    • Private, Protected and Public
    • Multiple Inheritance
    • Class diagrams
    • Debugging
    • Data encapsulation
  • GIT

    • 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
  • Final Project

    As per the recommendation of students, one of the following projects will be done by the instructor themselves!

    • 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)
  • 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
11 Dec 2022 08:00 AM - 09:30 AM
11 Dec 2022 03:00 PM - 04:30 PM
12 Dec 2022 11:00 AM - 12:30 PM
18 Dec 2022 09:30 AM - 11:00 AM
25 Dec 2022 06:30 AM - 08:00 AM

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