Python with Artificial Intelligence Training in Nepal | Generative AI Course (LLM) with Python

Course Overview

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The AI Integrated training course is a comprehensive 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.

Skills you’ll learn

Python Programming : Strengthen coding skills with a focus on AI applications.

Machine Learning Algorithms : Implement classification, regression, and clustering.

Deep Learning Frameworks (TensorFlow, PyTorch) : Build neural networks for advanced tasks.

Real-World AI Projects : Deploy intelligent solutions in practical scenarios.

Benefits of AI with Python: Machine Learning, Deep Learning & Generative AI (LLMs) Training

AI training in Nepal rewards an individual with the following talents.

Provides a variety of scope and job roles such as computer programmer, robotics engineer, software engineer, etc.
Applications of AI assets in fields such as education, healthcare, finance, aviation, and marketing, among others.
Learn to create productive tools such as smart reply, autonomous driving, toxicity detection, mitosis detection, etc.
Learn to understand TensorFlow algorithms.
Provides in-depth knowledge about neural networks and robot locomotion.
Gives creativity and intelligence a great boost.

Benefits of AI with Python: Machine Learning, Deep Learning & Generative AI (LLMs) Training at Broadway Infosys

A chance to learn from highly experienced professional experts.
Tools and technologies equipped are by far advanced.
Provides the course at a reasonable price.
Scholarships are available for deserving students.
Internship assistance for deserving students after completing the training.
Regular assignments are given to assess a student's capability.
Counseling is offered to encourage students.
Opportunity to be a part of a friendly learning environment.
Project work at the end of the training session.

Students who got hired learning this course

Hear from graduates who have completed our courses.

view more success stories

Ms. Kusum Basnet

Jr. Artificial Intelligence

Daraz

College/Faculty

International School Of Management and Technology / Bachelors in Computer System Engineering

Mr. ⁨Aayush Pandey

Artificial Intelligence Developer

Hospital for Children, Eye, ENT and Rehabilitation Services

College/Faculty

Kathford International College of Engineering & Management / BCT

Mr. ⁨Sajal Rokka

Software Support Engineer

IMS Software Pvt. Ltd.

College/Faculty

Citizen College / BCA

Mr. ⁨Aliz Shrestha

Researcher and Developer

Surgience Nepal

College/Faculty

Kantipur Engineering College / Bachelor in Computer Engineering

Our graduates are hired by 350+ 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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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

Building a complete machine learning model
Deploying ML projects using Streamlit

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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AI with Python: Machine Learning, Deep Learning & Generative AI (LLMs) Training

Course Overview

Skills you’ll learn

Benefits of AI with Python: Machine Learning, Deep Learning & Generative AI (LLMs) Training

Benefits of AI with Python: Machine Learning, Deep Learning & Generative AI (LLMs) Training at Broadway Infosys

Students who got hired learning this course

Ms. Kusum Basnet

College/Faculty

Mr. ⁨Aayush Pandey

College/Faculty

Mr. ⁨Sajal Rokka

College/Faculty

Mr. ⁨Aliz Shrestha

College/Faculty

Our graduates are hired by 350+ companies in Nepal

Lesson 1: Python Programming Language

1.1 Introduction to Python

1.2 Python Basics

1.3 Control Structures

1.4 Data Structures

1.5 Functions

1.6 File Handling

1.7 Object-Oriented Programming (OOP)

1.8 Error Handling and Debugging

1.9 Advanced Python Concepts

1.10 Python Libraries and Frameworks

1.11 Introduction to SQL in Python

1.12 Git and GitHub Introduction

1.13 Final Project Options:

Lesson 2: Machine Learning

2.1 Introduction to AI & ML

2.2 Data Preprocessing

2.3 Regression Models

2.4 Classification Algorithms

2.5 Unsupervised Learning

2.6 ML Project Development

Lesson 3: Deep Learning

3.1 Math & Frameworks

3.2 Artificial Neural Networks (ANN)

3.3 Convolutional Neural Networks (CNN)

3.4 Natural Language Processing (NLP)

Lesson 4: Generative AI & Large Language Models (LLMs)

4.1 LLM Foundations

4.2 Prompt Engineering

4.3 Tools & Ecosystem

4.4 LLM Applications and Projects

Earn a High Value Industry Certificate

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