Machine Learning with python training

Machine Learning in Python Training in Kathmandu, Nepal

Duration: 10 Weeks
Career: Data Scientist
Training Mode: Both, Physical & Live Online Classes
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Machine Learning with Python Training in Nepal

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.


Benefits of Machine Learning with Python Training in Nepal

Machine learning with Python has several benefits. Some of them are as featured.

  • Students can learn AI easily and efficiently using Python.
  • Provides an individual with the option to choose between oops approach and scripting.
  • Candidates can achieve the basic concepts of Tensorflow and CNTK.
  • Gives the student a creative enhancement for learning AI
  • Easy to land a job due to the tremendous demand of AI domain experts.
  • Wider career opportunities.
  • Develops leadership skills.

Python is easily chosen over other programming languages, for instance, C++ and Java, all thanks to its easily readable and understandable nature. Moreover, for any candidates willing to earn a platform in AI, this course is a stepping stone as it supports deep learning in every way possible. Therefore, please contact us today to learn more about this course which suits you best and earns you key skills.

Benefits of Machine Learning with Python at Broadway Infosys 

Machine learning with Python under the supervision of Broadway can be beneficial in the following ways.

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

Broadway Infosys invites eligible students to join the training sessions on Deep Learning with Python. Enroll your name at the earliest in order to appear in the training routine. Therefore, do it now and do it wisely so you do not have to wait until the next session starts.

Machine Learning with python training - Outlines
    Section 1: Python Programming Outline
  • Installation

  • Python version and pip package manager

  • Introduction to Google Colab, Jupyter Notebook / IDE

    • Introduction to markdown
  • Python Program and statements

  • Python Arithmetic Operators

    • Using Python as calculator
  • How to define a variable name and Variable Naming convention in Python

  • Operator, Operands, and Operator Precedence

  • Changing and updating variable values in Python

  • Assigning multiple values to multiple variables

  • Data types in Python

  • Number data type: int, float, complex

    • Number data type
    • Taking input from the users
    • Type casting and type checking
    • Type validation
    • Number type with conditionals
  • Conditions and Recursion

    • Modulus operator
    • Boolean expressions
    • Logical operators
    • Conditional execution
    • Chained conditionals
    • Nested conditionals
    • Recursion
    • Stack diagrams for recursive functions
    • Infinite recursion
  • Iteration

    • Multiple assignments
    • Updating variables
    • The while statement
    • Break
    • Debugging
    • For loop
  • Python string

    • Introduction
    • Single line vs multiline string
    • Indexing
    • slicing
    • len()
    • Loop: for loop using range()
    • Loop with conditionals
    • continue vs break
    • characters vs substrings
    • Immutable data type
    • String methods: .replace(),.lower(), .upper(),.title() .lstrip(), .rstrip(), .strip(), .split(), .join(), .isdigit(), isupper(), islower(), .format()
  • Python Built-in data types


    • Introduction to list
    • Indexing/Negative indexing
    • Slicing
    • looping & conditionals, len()
      • Different types of for loop ,while loop, for loop vs while loop
    • list of list and nested loop
    • Membership operators: in , not in
    • Mutable vs Immutable data type with exmaple
    • List methods: .insert(), .append(), .remove(), .pop(), .sort(), .extend(), .remove(), .sort()
    • List Comprehension
    • with if and else


    • Introduction to tuple
    • Indexing , slicing, looping
    • list vs tuple
    • Typecasting list -> tuple and tuple ->list
    • tuple unpacking


    • Introduction to set
    • .remove() , .add(), .discard() in sets
    • Type conversion
    • Set operation in Python : union, intersection, difference
    • Frozenset vs set


    • Introduction
    • disctionary methods: .get(), .update(), .keys(), .values(), .pop()
    • Loop and dictionary comprehension
    • Nested Dictionary
  • None type

    • Identity Operators
  • Python Functions

    • Introduction and syntax: Why function is necessary
    • Function definition and function call
    • arguments/parameters in function
    • return statement in function
    • returning multiple value from function
    • Handling multiple return values
    • Default argument vs non default argument and why it is necessary
    • global and local variables
    • *args vs **kwargs
    • Introduction to Recursion and Recursion tree
    • pass keyword
  • OOP in python

    • Class and Objects
    • Class attribute and Object attribute
    • Initilizing object attribute
    • __init__()
    • Self keyword and its importance
    • Inheritance and its types
    • Single Inheritance
    • super() method
    • Mulitple Inheritance
    • Multi-level Inheritance
    • Abstraction and Access specifiers
    • Polymorphism
    • + and len()
    • Operator overloading using Dunders/magic method (user defined class)
    • function overriding
    • Encapsulation
  • Introduction to Exceptions

    • Understanding exceptions in Python
    • Types of exceptions and their meaning
    • Importance of exception handling
    • Handling Exceptions
      • Using try-except blocks to handle exceptions
      • Catching specific exceptions
      • Handling multiple exceptions
      • Using the finally clauses
    • Raising Exceptions
  • File Handling

    • open()
    • modes:
      • read : ‘r’
        • read(), readline(), readlines()
      • write: ‘w’
      • append: ‘a’
      • create: ‘x’
      • Comparison of append and write modes
    • File handling on CSV files.
    • DictReader and DictWriter
    • File handling with exception handling
  • Others

    • Lambda function/Annynomous Function:
      • map() , filter()
    • os library
    • random library
    • math library
  • Introduction to SQL in python

    • Creating database
    • Defining table structure with SQL statements and Specifying column names, data types, and constraints
    • Inserting new records into tables using SQL INSERT statements
    • Retrieving data from tables using SQL SELECT statements
    • Modifying existing data in tables using SQL UPDATE statements
    • Removing tables data  using SQL DELETE statements
    • Filtering data using the WHERE clause in SQL SELECT statements
  • Introduction to git and Github

    • Installing Git Bash
      • Overview of Git Bash
      • Installation
    • Creating a GitHub Account
      • Sign up for a GitHub account
      • Set up profile
    • Creating an Empty Repository
      • Create a new empty repository on GitHub
    • Initializing a Git Repository Locally
      • Initialize a Git repository on your local machine using Git Bash
    • Tracking Files
      • Add files to the staging area
      • Commit changes to the repository
    • Configuration of Global User Information
      • Configure your global and for Git
    • Branching (main)
      • Understand the concept of branches
      • Work with the default main branch
    • Adding a Remote
      • Connect your local repository to the remote repository on GitHub
      • Configure the remote repository URL
    • Pushing Changes
      • Push your local changes to the remote repository on GitHub
    • Cloning
      • Clone a repository from GitHub to your local machine using Git Bash
    • Creating a New Branch
      • Create a new branch for making changes
      • Switch between branches
    • Pushing Changes to a Branch
      • Push your changes to the remote repository on a specific branch
  • Pandas

    • Introduction to Pandas
    • DataFrame Data Structure
    • Reading and writing csv files using DataFrame
    • Manipulating DataFrame
  • Basic Data Visualization

    • Introduction to Matplotlib and Seaborn and plotly
    • Basic plotting using any of these library
  • Project Work (one of the following ):

    • Web Scraping project + Databse
    • Any desktop application: eg. Data Entry application
  • Section 2: Machine Learning Outline
  • Introduction and installation

    • 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
  • Data Preprocessing

    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
  • Linear Regression


    • 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
  • Logistic regression

      sigmoid function

    • Cross validation
    • Confusion Matrix, Precision, Recall and F1 Score
    • Precision and Recall Tradeoff
    • The ROC Curve
  • Support Vector Machine (SVM)

    • Introduction
    • Linear SVM Classification
    • Polynomial Kernel
    • Gaussian Radial Basis Function
    • Support Vector Regression
  • K-nearest neighbor

    • Introduction
    • Lazy learning
    • Euclidean-distance
    • Implementation
  • Naive Bayes classifier

    • Introduction
    • Implementation
    • Classification and clustering applications
  • Decision trees

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


  • Clustering

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

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

    Introduction to Deep Learning

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

         Simple Linear Regression Modelling with Boston Housing Data

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

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