Course Fee: £ 249.99 50% off £499.99
(VAT will be applied for the UK Customers)
10 days(Once in a Week)

What You will Learn

  • Learn to use Python professionally, learning both Python 2 and Python 3!
  • Create games with Python, like Tic Tac Toe and Blackjack!

  • Learn advanced Python features, like the collections module and how to work with timestamps!

  • Learn to use Object Oriented Programming with classes!

  • Understand complex topics, like decorators.

  • Understand how to use both the Jupyter Notebook and create .py files

  • Get an understanding of how to create GUIs in the Jupyter Notebook system!

  • Build a complete understanding of Python from the ground up!

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Outcomes

  • You can code your own Robot with this programming language

  • Make fun games

  • Play Music

  • You can code like to become ethical hacker

  • To understand why Python is a useful scripting language for developers

  • To learn how to design and program Python applications

  • To learn how to use lists, tuples, and dictionaries in Python programs

  • To learn how to identify Python object types

  • To learn how to use indexing and slicing to access data in Python programs

  • To define the structure and components of a Python program

  • To learn how to write loops and decision statements in Python

  • To learn how to write functions and pass arguments in Python

  • To learn how to build and package Python modules for reusability

  • To learn how to read and write files in Python

  • To learn how to design object‐oriented programs with Python classes

  • To learn how to use class inheritance in Python for reusability

  • To learn how to use exception handling in Python applications for error handling

Course Content

Python Phase

  • Introduction

    • Strong and Dynamic Typing
    • Interactive Mode
  • Data Types I

    • Numerical Data Types
    • Operators on Numbers
    • Strings
    • Lists
    • Tuple
    • Lists, Strings and Tuples
    • Operations on Sequences
    • Indexing in Python
  • Control Statements

    • The If Statement
    • For Loops
    • While Loops
  • Functions

    • Functions
    • Return Values and Parameters
    • Functions are Objects
    • Functions & Modules
  • Input/Output

    • String Formatting
    • Literal String Interpolation (f-strings)
    • Command Line Input
    • Files
    • The with statement
  • Errors and Exceptions

    • Syntax Errors, Indentation Errors
    • Exceptions
    • Raising Exceptions
    • The with Statement
  • Data Types II

    • The with Statement
  • Object Oriented Programming

    • Using Simple Classes as Structs
    • Classes – Constructor
    • Methods on Objects
    • Converting Objects to Strings
    • Comparing Objects
    • Operator overloading
    • Class Variables
    • Class Methods and Static Methods
    • Inheritance (1)
    • Private Attributes / Private Class Variables
  • Modules and  Packages

    • Importing Modules
    • Creating a Module
    • Creating a Package
    • Modules Search Path
    • Random Numbers
    • Time Access and Conversion
    • Date and Time
    • Operations on Path Names
    • Files and Directories
    • Access to Command Line Parameters
  • Advanced Technics

    • List Comprehension
    • Dynamic Attributes
    • getattr, setattr, hasattr
    • Map
    • Filter
  • Tools

  • Regular Expressions (optional)

  • Summary and Outlook

AI and Machine Learning Phase

  • Algorithms

    • A*
    • Multi-Layer Perception (MLP)
    • Support Vector Machine (SVM)
    • Depth First Search
    • Breadth First Search
    • Best First Search
    • ANT Colony Optimization
  • Machine Learning

    • Linear Regression
    • Logistic Regression
    • Decision Tree
    • SVM
    • Naive Bayes
    • kNN
    • K-Means
    • Random Forest
    • Dimensionality Reduction Algorithms
    • Gradient Boosting algorithms
      • GBM
      • XGBoost
      • LightGBM
      • CatBoost
  • 1- Sklearn

    • CNN
    • ANN
    • RNN
    • RCNN
  • Supervised Learning

    • KNN
    • Logistic Regression
    • Decision Tree
    • Random Forest
  • Un-Supervised Learning

    • Apriori Algorithm
    • K-Means
  • Reinforcement Learning

    • Markov Decision Process
    • Envi

Week 1

First Session

Machine Learning with Python

  • Introduction

    • Strong and Dynamic Typing
    • Interactive Mode
  • Data Types I

    • Numerical Data Types
    • Operators on Numbers
    • Strings
    • Lists
    • Tuple
    • Lists, Strings and Tuples
    • Operations on Sequences
    • Indexing in Python
  • Control Statements

    • The If Statement
    • For Loops
    • While Loops

Second Session

Machine Learning

  • Linear Regression

  • Logistic Regression

  • Support Vector Machine

Week 2

First Session

Python

  • Functions

    • Functions
    • Return Values and Parameters
    • Functions are Objects
    • Functions & Modules
  • Input/Output

    • String Formatting
    • Literal String Interpolation (f-strings)
    • Command Line Input
    • Files
    • The with statement
  • Errors and Exceptions

    • Syntax Errors, Indentation Errors
    • Exceptions
    • Raising Exceptions
    • The with Statement
  • Data Types II

    • The with Statement

Second Session

Artificial Intelligence

  • Algorithms

    • A*
    • Multi-Layer Perception (MLP)
    • Depth First Search
    • Breadth First Search
    • Best First Search

Week 3

First Session

  • Object Oriented Programming

    • Using Simple Classes as Structs
    • Classes – Constructor
    • Methods on Objects
    • Converting Objects to Strings
    • Comparing Objects
    • Operator overloading
    • Class Variables
    • Class Methods and Static Methods
    • Inheritance (1)
    • Private Attributes / Private Class Variables

Second Session

  • Introduction to Supervised Learning

    • KNN
    • Logistic Regression
    • Decision Tree
    • Random Forest

Week 4

First Session

  • Simple Car Game Design Part 1

Second Session

  • Simple Car Game Design Part 2

Week 5

Artificial Intelligence use in Music

First Session

  • Use of Tensorflow and Other Libraries

Second Session

  • Play music using Artificial Intelligence

Week 6

First Session

  • Modules and Packages

  • Importing Modules

  • Creating a Module

  • Creating a Package

  • Modules Search Path

  • Random Numbers

  • Time Access and Conversion

  • Date and Time

  • Operations on Path Names

  • Files and Directories

  • Access to Command Line Parameters

Second Session

  • Supervised Learning

    • KNN
    • Logistic Regression
    • Decision Tree
    • Random Forest

Week 7

First Session

Python

  • Modules and Packages

  • Advanced Technics

    • List Comprehension
    • Dynamic Attributes
    • getattr, setattr, hasattr
    • Map
    • Filter
  • Tools

  • Regular Expressions (optional)

  • Summary and Outlook

Machine Learning

  • Un-Supervised Learning

    • Apriori Algorithm
    • K-Means

Second Session

Machine Learning

  • Read and Clean Data

Week 8

First Session

  • How to make an algorithm

  • How to Make a code of that Algorithm

Second Session

  • Use of Different Libraries and Algorithm

Week 9

First Session

  • Make a program to predict Weather

Second Session

  • Make a Program to Find Fraud

Week 10

First Session

  • What is Robotics

  • What are the algorithms use in robots?

Second Session

  • Write a program for a Robot at online tool