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Deep Learning with Python Training
Deep Learning with Python Training in Kathmandu, Nepal
Thousands of students have started their careers after getting certified by Broadway Infosys
Updated On: 27/04/2026
Created On: 17/06/2022
Course Overview
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.
Why Deep Learning with Python?
Python to Deep Learning, Fully Covered: Start with Python fundamentals, then progress into neural networks a complete path from programming basics to advanced AI.
Real Neural Network Depth: Master ANNs, CNNs, GANs, and RNNs, the core architectures behind image recognition, generative AI, and language models.
Computer Vision & NLP Included: Build face detection, object recognition, and sentiment analysis projects practical applications across two major AI domains.
Framework Fluency: Get grounded in TensorFlow, Keras, and PyTorch, the exact tools used in real-world deep learning work.
Qualified, Exceptional Instructors: Learn from qualified IT experts who bring real depth and skill to every session.
Well-Equipped Training Lab: Experience hands-on learning in a training lab built to support real deep learning work.
Our Student’s Voice
Hear from graduates who have completed our courses.
Sandhya Basnet
Deep Learning with Python Training
It was a great experience with Broadway Infosys to be part of the course. During my period of study, I was able to gain much different knowledge in the related topic and our teacher was also a good tutor who...
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College/Faculty: Pokhara University / Bachelor of Computer Engineering
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Position: Jr. Software Engineer
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Time for you to be the next hire. With our advanced and industry relevant courses, you are on the right stage to start your dream career.
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
- Web Scraping + Database + File Operations: Scrape data, store it in SQL, & export to CSV/JSON
- Desktop Application (Data Entry System): Develop an application to manage data in JSON/CSV format
- CLI Application with CRUD Operations: Design a CLI app with basic CRUD operations & database integration
Lesson 2: AI - Deep Learning in Python (Syllabus)
- 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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