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Machine Learning with python training

Machine Learning with Python
AI INTEGRATED COURSE

Machine Learning with python training

Machine Learning in Python Training in Kathmandu, Nepal

Mode: Physical & Online Live Classes (Day/Night)
Successful student from Broadway Infosys Mr. Sagar Khanal

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

Updated On: 03/2026

Created On: 04/2018

Course Overview
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Machine learning explores the dynamics of discovering and displaying data patterns, construction of algorithms which enables a computer to make a prediction and study the patterns of computational learning theory using Artificial Intelligence. Broadway Infosys delivers Machine learning with python training in Nepal in order to establish competent AI experts and companies in Nepal.

The use of Python in Machine learning is the key feature in serving the sole purpose of the training program which is to empower machine learning technology in Nepal through the help of young IT enthusiast. This course is career-oriented as it teaches students to respond to real-world problems of machine learning. Also, the training program is the key to ensure the objective of Broadway Infosys Nepal.

Skills you’ll learn

Python Basics : You’ll learn how to write Python code, work with data types, loops, functions, and libraries.
Data Handling with Pandas : You’ll use Pandas to load, clean, and explore data in tables (DataFrames).
Machine Learning Basics : You’ll understand what ML is, how models learn, and the difference between supervised and unsupervised learning.
Supervised Learning : You’ll train models like Linear Regression, Decision Trees, and Random Forests to make predictions from labeled data.

Benefits of Machine Learning with Python Training

  • Students can learn AI easily and efficiently using Python.
  • It provides an individual with the option to choose between the OOPS approach and scripting.
  • Candidates can achieve the basic concepts of TensorFlow and CNTK.
  • It gives the student a creative enhancement for learning AI.
  • It is easy to land a job due to the tremendous demand for AI domain experts.
  • Wider career opportunities.
  • Develops leadership skills.

Benefits of Machine Learning with Python Training at Broadway Infosys

  • Learn from the ultimate Python experts in Nepal.
  • Availability of well-equipped training labs.
  • Affordable training costs.
  • Opportunity for job placement having a huge payroll.
  • Scholarships are provided to deserving students.
  • It helps expand your professional network and ideas.
  • Experience the interactive and productive learning environment.

Students who got hired learning this course

Hear from graduates who have completed our courses.

view more success stories
Successful student from Broadway Infosys Mr. Sagar Khanal
Mr. Sagar Khanal

Associate Frontend Engineer

Techgaun Nepal

College/Faculty
Pokhara University / Bachelor of Computer Emgineering

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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 Outline

  • 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 Machine Learning concepts
  • Supervised vs. Unsupervised Learning
  • Installing Anaconda and Managing Environment
  • Introduction to Spyder and Jupyter notebook
  • Familiarization with Datasets
  • Data Exploration and Analysis
  • Machine Learning Libraries
    • Numpy
    • Scikit Learn
    • Matplotlib
    • Seaborn
    • Pandas
    • Scipy

Get the dataset

  • Importing the Libraries
  • Importing the Dataset
  • Missing Data
  • Categorical Data
  • Splitting the Dataset into the Training set and Test set
  • Feature Scaling

Introduction

  • Optimization and gradient descent
  • Linear regression implementation
  • Correlation Analysis and Feature Selection
  • Evaluate Model Performance
  • Multiple Regression and Feature Importance
  • Variance Bias Trade Off - Validation Curve
  • Variance Bias Trade Off - Learning Curve
  • Cross validation

  sigmoid function

  • Cross validation
  • Confusion Matrix, Precision, Recall and F1 Score
  • Precision and Recall Tradeoff
  • The ROC Curve

  • Introduction
  • Linear SVM Classification
  • Polynomial Kernel
  • Gaussian Radial Basis Function
  • Support Vector Regression

  • Introduction
  • Lazy learning
  • Euclidean-distance
  • Implementation

  • Introduction
  • Implementation
  • Classification and clustering applications
     

  • Decision trees
  • Basics
  • Entropy
  • Information gain
  • Implementation

 

  • Introduction
  • Principal Component Analysis
  • K-means clustering
  • Hierarchical clustering

  • Introduction to Reinforcement Learning
  • Applications of Reinforcement Learning
  • Examples including AlphaGo case study

Introduction to Deep Learning

  • Artificial Neural Networks
  • Introduction to deep learning libraries such as tensorflow, keras, pytorch etc.
  • Research works in deep learning

     Simple Linear Regression Modelling with Boston Housing Data

  • Iris Flowers Classification Project
  • MNIST Project - Logistic Regression
  • Text Classification using SVM
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