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Machine learning with python training

Machine Learning in Python Training in Kathmandu, Nepal

Duration: 10 Weeks Career Option: Data Scientist

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

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 Nepal 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.

Pre-requisites for Machine learning with Python Training

Candidates attending this training session are required to master python programming language. Also, candidates who have a basic concept of python with data science is a plus-point.

Courses Outline :- Machine learning with python training
  • Getting Started with Python
  • Environment setup
  • The Python programming language
  • Applications Area

Values and types

  • Variables
  • Variable names and keywords
  • Operators and operands
  • Expressions and statements
  • Interactive mode and script mode
  • Order of operations
  • String operations
  • Comments
  • Debugging

Function calls

  • Type conversion functions
  • Math functions
  • Composition
  • Adding new functions
  • Definitions and uses
  • Flow of execution
  • Parameters and arguments
  • Variables and parameters are local
  • Stack diagrams
  • Fruitful functions and void functions
  • Why functions?
  • Importing with from
  • Debugging

Modulus operator

  • Boolean expressions
  • Logical operators
  • Conditional execution
  • Alternative execution
  • Chained conditionals
  • Nested conditionals
  • Recursion
  • Stack diagrams for recursive functions
  • Infinite recursion
  • Keyboard input
  • Debugging

 

Return values

  • Incremental development
  • Composition
  • Boolean functions
  • More recursion
  • Leap of faith
  • Checking types
  • Debugging

Multiple assignments

  • Updating variables
  • The while statement
  • Break
  • Debugging
  • For loop

A string is a sequence

  • Len
  • Traversal with a for loop
  • String slices
  • Strings are immutable
  • Searching
  • Looping and counting
  • String methods
  • The in operator
  • String comparison
  • Debugging

A list is a sequence

  • Lists are mutable
  • Traversing a list
  • List operations
  • List slices
  • List methods
  • Map, filter and reduce
  • Deleting elements
  • Lists and strings
  • Objects and values
  • Aliasing
  • List arguments
  • Debugging

 

    Dictionary as a set of counters

  • Looping and dictionaries
  • Reverse lookup
  • Dictionaries and lists
  • Memos
  • Global variables
  • Long integers
  • Debugging

Tuples are immutable

  • Tuple assignment
  • Tuples as return values
  • Variable-length argument tuples
  • Lists and tuples
  • Dictionaries and tuples
  • Comparing tuples
  • Sequences of sequences
  • Debugging

     Usage

  • Union, Difference
  • Available methods

Introduction

  • Exceptions versus Syntax Errors
  • Raising an Exception
  • The AssertionError Exception
  • The try and except Block: Handling Exceptions
  • The else Clause

Persistence

  • Reading and writing
  • Format operator
  • Filenames and paths
  • Catching exceptions
  • Databases
  • Writing modules
  • Debugging

      Introduction, Application, and Usage

  • Reader and writer
  • DictReader and DictWriter
  • Simple CSV processing using functional programming

User-defined types

  • Attributes
  • Real World Example
  • Instances as return values
  • Objects are mutable
  • Copying
  • Debugging

Object-oriented features

  • The self
  • Printing objects
  • The init method
  • The __str__ method
  • Other special methods
  • Operator overloading
  • Type-based dispatch
  • Polymorphism
  • @staticmethod
  • Debugging

Introduction

  • checking callable or not
  • Decorators
  • creating and using decorators

 

      Introduction

  • Example
  • Class attributes
  • Private, Protected and Public
  • Multiple Inheritance
  • Class diagrams
  • Debugging
  • Data encapsulation

 

     Advanced Strings, Date & Time

  • Python os, re, sys
  • Comprehensions: List, Dictionary
  • CSV, JSON, XML, SQLite with Python
  • 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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