Python with Artificial Intelligence (AI) Training

Python with Artificial Intelligence (AI) Training

AI, ML, and Deep Learning using Python

Duration: 3.5 Months ( 145 Hrs. )
Career: AI Programmer
Training Mode: Both, Physical & Live Online Classes
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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

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.
  • Internship assistance 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
  • Installation

  • Python version and pip package manager

  • Introduction to Google Colab, Jupyter Notebook / IDE

    • Introduction to markdown
  • Python Program and statements

  • Python Arithmetic Operators

    • Using Python as calculator
  • How to define a variable name and Variable Naming convention in Python

  • Operator, Operands, and Operator Precedence

  • Changing and updating variable values in Python

  • Assigning multiple values to multiple variables

  • Data types in Python

  • Number data type: int, float, complex

    • Number data type
    • Taking input from the users
    • Type casting and type checking
    • Type validation
    • Number type with conditionals
  • Conditions and Recursion

    • Modulus operator
    • Boolean expressions
    • Logical operators
    • Conditional execution
    • Chained conditionals
    • Nested conditionals
    • Recursion
    • Stack diagrams for recursive functions
    • Infinite recursion

      

  • Iteration

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

    • Introduction
    • Single line vs multiline string
    • Indexing
    • slicing
    • len()
    • Loop: for loop using range()
    • Loop with conditionals
    • continue vs break
    • characters vs substrings
    • Immutable data type
    • String methods: .replace(),.lower(), .upper(),.title() .lstrip(), .rstrip(), .strip(), .split(), .join(), .isdigit(), isupper(), islower(), .format()
  • Python Built-in data types

          List

    • Introduction to list
    • Indexing/Negative indexing
    • Slicing
    • looping & conditionals, len()
      • Different types of for loop ,while loop, for loop vs while loop
    • list of list and nested loop
    • Membership operators: in , not in
    • Mutable vs Immutable data type with exmaple
    • List methods: .insert(), .append(), .remove(), .pop(), .sort(), .extend(), .remove(), .sort()
    • List Comprehension
    • with if and else

    Tuple

    • Introduction to tuple
    • Indexing , slicing, looping
    • list vs tuple
    • Typecasting list -> tuple and tuple ->list
    • tuple unpacking

    Set

    • Introduction to set
    • .remove() , .add(), .discard() in sets
    • Type conversion
    • Set operation in Python : union, intersection, difference
    • Frozenset vs set

    Dictionary

    • Introduction
    • disctionary methods: .get(), .update(), .keys(), .values(), .pop()
    • Loop and dictionary comprehension
    • Nested Dictionary
  • None type

    • Identity Operators
  • Python Functions

    • Introduction and syntax: Why function is necessary
    • Function definition and function call
    • arguments/parameters in function
    • return statement in function
    • returning multiple value from function
    • Handling multiple return values
    • Default argument vs non default argument and why it is necessary
    • global and local variables
    • *args vs **kwargs
    • Introduction to Recursion and Recursion tree
    • pass keyword
  • OOP in python

    • Class and Objects
    • Class attribute and Object attribute
    • Initilizing object attribute
    • __init__()
    • Self keyword and its importance
    • Inheritance and its types
    • Single Inheritance
    • super() method
    • Mulitple Inheritance
    • Multi-level Inheritance
    • Abstraction and Access specifiers
    • Polymorphism
    • + and len()
    • Operator overloading using Dunders/magic method (user defined class)
    • function overriding
    • Encapsulation
  • Introduction to Exceptions

    • Understanding exceptions in Python
    • Types of exceptions and their meaning
    • Importance of exception handling
    • Handling Exceptions
      • Using try-except blocks to handle exceptions
      • Catching specific exceptions
      • Handling multiple exceptions
      • Using the finally clauses
    • Raising Exceptions
  • File Handling

    • open()
    • modes:
      • read : ‘r’
        • read(), readline(), readlines()
      • write: ‘w’
      • append: ‘a’
      • create: ‘x’
      • Comparison of append and write modes
    • File handling on CSV files.
    • DictReader and DictWriter
    • File handling with exception handling
  • Others

    • Lambda function/Annynomous Function:
      • map() , filter()
    • os library
    • random library
    • math library
  • Introduction to SQL in python

    • Creating database
    • Defining table structure with SQL statements and Specifying column names, data types, and constraints
    • Inserting new records into tables using SQL INSERT statements
    • Retrieving data from tables using SQL SELECT statements
    • Modifying existing data in tables using SQL UPDATE statements
    • Removing tables data  using SQL DELETE statements
    • Filtering data using the WHERE clause in SQL SELECT statements
  • Introduction to git and Github

    • Installing Git Bash
      • Overview of Git Bash
      • Installation
    • Creating a GitHub Account
      • Sign up for a GitHub account
      • Set up profile
    • Creating an Empty Repository
      • Create a new empty repository on GitHub
    • Initializing a Git Repository Locally
      • Initialize a Git repository on your local machine using Git Bash
    • Tracking Files
      • Add files to the staging area
      • Commit changes to the repository
    • Configuration of Global User Information
      • Configure your global user.name and user.email for Git
    • Branching (main)
      • Understand the concept of branches
      • Work with the default main branch
    • Adding a Remote
      • Connect your local repository to the remote repository on GitHub
      • Configure the remote repository URL
    • Pushing Changes
      • Push your local changes to the remote repository on GitHub
    • Cloning
      • Clone a repository from GitHub to your local machine using Git Bash
    • Creating a New Branch
      • Create a new branch for making changes
      • Switch between branches
    • Pushing Changes to a Branch
      • Push your changes to the remote repository on a specific branch
  • Pandas

    • Introduction to Pandas
    • DataFrame Data Structure
    • Reading and writing csv files using DataFrame
    • Manipulating DataFrame
  • Basic Data Visualization

    • Introduction to Matplotlib and Seaborn and plotly
    • Basic plotting using any of these library
  • Project Work (one of the following ):

    • Web Scraping project + Databse
      OR
    • Any desktop application: eg. 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
  • ML Web App development Streamlit

    • Introduction to Flask
    • URL and App routing
    • Streamlit application – ML Model Deployment
  • 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
14 Jul 2024 09:30 AM - 11:00 AM
14 Jul 2024 03:00 PM - 04:30 PM
14 Jul 2024 06:00 PM - 07:30 PM
21 Jul 2024 06:30 AM - 08:00 AM
28 Jul 2024 08:00 AM - 09:30 AM
28 Jul 2024 06:00 PM - 07:30 PM

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