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AI Training with Python: Machine Learning, Deep Learning & Generative AI (LLMs) Training
AI, ML, Deep Learning & Generative AI Course in Nepal
Thousands of students have started their careers after getting certified by Broadway Infosys
Updated On: 22/07/2026
Created On: 17/06/2022
Course Overview
The AI training in Nepal with Python is a comprehensive artificial intelligence program that aims to give professionals, students, and technology enthusiasts practical and theoretical knowledge to enable them to thrive in the fast-paced world of Artificial Intelligence.
The course is hands-on in its approach, using Python, the standard programming language for AI, to impart fundamental and advanced concepts from three important streams: Machine Learning, Deep Learning, and Generative AI via Large Language Models.
From building traditional ML models to venturing into state-of-the-art Neural Networks and generative AI tools such as ChatGPT, it offers a pathway for forward-looking learners.
Tools Covered
Some of the major industry-relevant tools you'll work with in this course include:
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Python
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Jupyter Notebook
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Google Colab
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VS Code
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Pandas
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NumPy
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Matplotlib
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Seaborn
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Plotly
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Scikit-learn
Why AI training in Nepal with Python: Machine Learning, Deep Learning & Generative AI?
Three Streams, One Program: Master Machine Learning, Deep Learning, and Generative AI/LLMs together, a complete progression instead of scattered, disconnected training.
GPU-Enabled Training Labs: Train CNNs and deep learning models on lab machines built for the job, so your models finish in minutes instead of hours on a personal laptop.
Cutting-Edge GenAI Skills: Work with LLMs, Hugging Face, LangChain, and RAG pipelines, and build real Q&A bots and semantic search apps using the same tools powering today's AI industry.
Live App Deployment Access: Deploy your Streamlit and FastAPI apps to a dedicated Broadway server, so you leave with a live, shareable project link instead of code that only runs on your own machine.
From Model to Deployment: Go beyond training models; learn to deploy them as live apps using Streamlit and FastAPI, so your projects are demo-ready, not just notebooks.
Our Student’s Voice
Hear from graduates who have completed our courses.
Mr. Sakshyam Karki
AI Training with Python: Machine Learning, Deep Learning & Generative AI (LLMs) Training
I thoroughly enjoyed embarking on this journey of acquiring new knowledge and refining my existing skills. This was an incredible experience in learning about artificial intelligence.
Ms. Khusbu Ayer
AI Training with Python: Machine Learning, Deep Learning & Generative AI (LLMs) Training
Our instrcutor was an excellent and always answered any queries that we had. She’s very helpful, and I appreciate getting to learn from her.
Er. Bimala Sharma
AI Training with Python: Machine Learning, Deep Learning & Generative AI (LLMs) Training
Taking the AI with Python course with Broadway Infosys was a game-changer for our team. We learned not just how AI works, but also how to use it effectively with practical tools.
The hands-on sessions made complex concepts easy to...
College/Faculty: International School Of Management and Technology / Bachelors in Computer System Engineering
Working At: Daraz
Position: Jr. Artificial Intelligence
College/Faculty: Kathford International College of Engineering & Management / BCT
Working At: Hospital for Children, Eye, ENT and Rehabilitation Services
Position: Artificial Intelligence Developer
College/Faculty: Citizen College / BCA
Working At: IMS Software Pvt. Ltd.
Position: Software Support Engineer
College/Faculty: Kantipur Engineering College / Bachelor in Computer Engineering
Working At: Surgience Nepal
Position: Researcher and Developer
Our graduates are hired by 470+ companies in Nepal
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.
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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: Python Programming Language
- 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
- 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 for SQL
- 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: Machine Learning
- Overview of AI, Machine Learning, and Deep Learning
- Real-world use cases in industries like healthcare, finance, and e-commerce
- Installation of Python, Jupyter, Google Colab, and Git
- Types of data: structured vs. unstructured
- Handling missing values and outliers
- Encoding categorical variables and feature scaling
- Exploratory Data Analysis (EDA) techniques and visualization
- Linear Regression: theory, implementation, and evaluation
- Logistic Regression for binary classification
- Polynomial Regression
- Model evaluation metrics: MSE, RMSE, R-squared, Accuracy, Precision, Recall
- Projects in each algorithm
- Support Vector Machines (SVM): linear and kernel methods
- K-Nearest Neighbors (KNN): algorithm and distance metrics
- Naive Bayes: theory, implementation, and use in spam filtering
- Decision Trees and Random Forests: entropy, information gain, overfitting control
- Projects in each Algorithm
- Clustering techniques: K-Means, Hierarchical Clustering, DBSCAN
- Dimensionality reduction with PCA and t-SNE
- Anomaly detection and recommendation system with unsupervised models
- Introduction to ML project architecture (Model + UI + API)
- Preparing trained ML model for deployment
- Saving and loading models (Pickle/Joblib)
- Building interactive ML apps using Streamlit
- Creating input forms and displaying predictions in Streamlit
- Introduction to FastAPI
- Creating RESTful prediction APIs using FastAPI
- Request/response validation using Pydantic
- Testing APIs with Swagger UI
- Deploying ML applications (Streamlit app, FastAPI)
Lesson 3: Deep Learning
- Math essentials: vectors, matrices, activation functions, derivatives
- Introduction to TensorFlow and Keras for model building
- Structure and working of neural networks
- Activation functions, forward and backward propagation
- Regularization, dropout, and optimization techniques
- Regression using ANN
- Classification using ANN
- CNN architecture and layers
- Image preprocessing and augmentation
- Transfer Learning
- Basics of object detection (YOLO)
- Image classification Project
- Text preprocessing: tokenization, stopword removal, stemming, lemmatization
- Word embeddings (Word2Vec, GloVe)
- RNNs and LSTMs for sequence modeling
- Sentiment analysis and Named Entity Recognition (NER)
- Simple chatbot and text classification project
Lesson 4: Generative AI & Large Language Models (LLMs)
- What are LLMs? Overview of GPT, BERT, T5, LLaMA
- Transformer architecture: self-attention, encoder-decoder models
- Pretraining vs fine-tuning vs instruction tuning
- Prompt types: zero-shot, few-shot, chain-of-thought
- Best practices for crafting effective prompts
- Hugging Face Transformers and Datasets libraries
- OpenAI API for text generation
- LangChain for chaining prompts and workflows
- Vector search using FAISS and ChromaDB
- Text summarization and question answering
- Retrieval-Augmented Generation (RAG)
- Embedding-based semantic search
- PDF/CSV/Website Q&A bots using LangChain
- Chatbot development using LLMs and vector stores
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