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Deep Learning with Python Training

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Deep Learning with Python Training

Deep Learning with Python Training in Kathmandu, Nepal

Mode: Physical & Online Live Classes (Day/Night)

More than 19000+ students have started their career after getting certified from Broadway Infosys.

Updated On: 03/2026

Created On: 06/2022

Course Overview
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Broadway envisions playing a productive role in the development process of AI by offering courses on Deep Learning with Python programming in Nepal. Deep learning has taken the world by storm for these past few years. It was designed to imitate a human brain, in simple words to find patterns in behavior through neural networks resembling the features of the brain. Deep learning, machine learning or AI has already advanced to the point where intelligent chatbots, self-driving cars, and virtual assistance exist.

As a consequence of Python programming language, which is supposedly a roadmap to deep learning, the world has grown to favor it, including Broadway Infosys Nepal that has been offering job-oriented deep learning with python training in Nepal. This course teaches IT fanatics to use Keras 2.0, the latest version of the python program to contribute their knowledge in the field of Deep learning.

Skills you’ll learn

Python basics : Data types, variables, control flow (if-else, loops), functions, file handling, and data structures such as lists, tuples, sets, and dictionaries.
Python for AI tools : Practice on Google Colab with Gemini, ChatGPT, and Codeium.
Theoretical deep learning : Understanding neural networks, CNNs, GANs, RNNs, autoencoders, and some basics of deep reinforcement learning.
Computer vision : CNNs for image classification and object recognition.
NLP : Text classification and sentiment analysis using deep learning models.
GANs : Producing images from GANs as hands-on projects.

Benefits of Deep Learning with Python

  • Students can learn AI easily and efficiently using Python.
  • It provides an individual with the option to choose between an oops approach and scripting.
  • Candidates can grasp the fundamental concepts of TensorFlow and CNTK.
  • It gives the student a creative enhancement for learning AI
  • There is strong demand for AI experts, making it easier to get a job.
  • It is easy to land a job due to the tremendous demand for AI domain experts.
  • Wider career opportunities

Benefits of Deep Learning with Python at Broadway Infosys

Undergoing the course of Deep Learning with Python at Broadway benefits you in the following ways:

  • Learn from qualified and exceptional IT experts.
  • Experience the process of learning in a well-equipped training lab.
  • Learn at a reasonable cost with the scholarship opportunity.
  • Experience the sessions alongside your professional growth.
  • Assurance of internship opportunities after completing the training.

Our graduates are hired by 470+ companies in Nepal

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Our syllabus outlines are only the headlines of the major modules. To ensure a complete understanding of the course, we offer free counseling. Also, if you have specific modules in mind, you can customize the course. Send your inquiry today!

Lesson 1: Course Outline: Python Programming

  • What exactly is Python?
  • Python's root and its ecosystem
  • Python Installation & IDEs setting up (Google Colab, Jupyter Notebook, VSCode, PyCharm)
  • Python framework & Python syntax
  • Hands-on writing code on Google Colab

  • Data Types & Variables (String, Integer, Float, Complex, Boolean, None)
  • Input and Output Functions
  • Working with the format() method, f-strings, & escape sequences
  • Basic Arithmetic & Operators
  • Type casting, type checking, & validation

  • If Else Conditional Statements (if, else, elif)
  • Loops (for, while)
  • Looping over tuples, strings, & dictionaries
  • Special loops in Python (for/else)
  • Using nested loops and flow control through conditions
  • Resolving real-world problems to improve skills
  • Special Statements: pass, continue, break

AI Tool:

  • Google Colab - Gemini

Lists:

  • Overview & fundamental operations
  • Indexing, slicing, & negative indexing
  • Looping through lists & conditions
  • List methods like .insert(), .append(), .remove(), .sort(), etc.
  • List comprehension with conditions

Tuples:

  • Introduction & operations
  • Indexing, slicing, & looping
  • List versus Tuple
  • Switching between lists and tuples
  • Tuple unpacking

Sets:

  • Introduction & set operations
  • Adding, removing, & discarding items
  • Set operations: union, intersection, and difference
  • Frozenset versus set

Dictionaries:

  • Introduction to dictionaries & methods like .get(), .update(), .keys(), .pop(), etc.
  • Dictionary comprehension
  • Nested dictionaries

AI Tool:

  • Gemini or Codeium

  • Defining functions through def keyword
  • Parameters, Arguments, & Return Statements
  • Returning multiple values
  • Default & keyword arguments
  • Anonymous functions (lambda)
  • Nested functions & closures
  • Scopes in Python: Local and Global

Text File Operations:

  • Reading & writing text files
  • Modes of file (r, w, a, rb, wb)
  • File path handling with the os module

Working with CSV Files:

  • Basics of CSV format and operations
  • Reading & writing CSV files with csv.reader & csv.writer
  • Using dictionaries in CSV files

Working with JSON:

  • Introduction to JSON & its structure
  • Reading & writing JSON data with the json module
  • Parsing JSON strings

AI Tool:

  • Using ChatGPT for prompt engineering

  • Classes & Objects
  • Class versus Object attributes
  • Initializing object attributes with __init__()
  • self keyword
  • Inheritance: single, multiple, and multi-level
  • Polymorphism & operator overloading
  • Function overriding & encapsulation

AI Tools:

  • Pythontutor.com

  • try-except blocks
  • Catching specific exceptions
  • Using else & finally
  • Generating and creating custom exceptions
  • Problem-solving strategies

  • Lambda Functions
  • Generators & Iterators
  • List comprehensions
  • Working with *args & **kwargs

Standard libraries: os, random, math, functools, etc.

Data manipulation with Pandas

  • Working with DataFrames
  • Reading & writing CSV files
  • Data manipulation techniques

Data Visualization:

  • Using Matplotlib, Seaborn, and Plotly

AI Tools:

  • Pandas Profiling

  • Designing and changing databases and tables
  • CRUD operations (CREATE, SELECT, UPDATE, DELETE)
  • Filtering data with the WHERE clause

AI Tools:

  • DBeaver for SQL queries
  • Optimizing & explaining SQL queries with ChatGPT

  • Installing & configuring Git
  • Setting up local & remote repositories
  • Making commits & branching
  • Integrating local repositories to GitHub
  • Pushing changes & cloning repositories

AI Tools:

  • GitHub Copilot for Git commands

  1. Web Scraping + Database + File Operations: Scrape data, store it in SQL, & export to CSV/JSON
  2. Desktop Application (Data Entry System): Develop an application to manage data in JSON/CSV format
  3. CLI Application with CRUD Operations: Design a CLI app with basic CRUD operations & database integration

  • 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 ď‚· 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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