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  • Google Colab for Python Programming
  • Operators used in Python.
  • How to create and use arrays in Python using NumPy
  • Built-in function in NumPy
  • Generating Random Numbers using NumPy
  • How to plot and visualize the data using Matplotlib
  • How to create lists and use them in Python
  • for loops and while loops in Python
  • Strings in Python
  • How to create dictionaries and use them in Python
  • Seaborn Library
  • Pandas
  • Creating Functions in Python
  • Creating Classes in Python
  • Tuples
  • The lambda( ) function
  • map( ), reduce( ) and filter( ) functions

Fundamental Course of Python Programming for Absolute beginners using Google Colab. This course covers all the fundamental libraries such as NumPy, Matplotlib, Pandas, Seaborn etc. Furthermore many built-in Python function are explained with examples.

Course Contents

Introduction of the Google Colab

Operators in Python

Arrays with Numpy

Matplotlib for Data Visualization

Lists in Python

Loops in Python

Strings in Python

Dictionaries in Python

Seaborn

Pandas

Functions

Creating Classes in Python

Tuples

lambda() function

map() replace() and filter() functions

zip() and join()

 

Who this course is for:

 

  • Anyone who wants to learn Python from scratch.

  • Students and Researchers in Artificial Intelligence.

  • Students and Researchers who want to develop Python Programming skills to solve their Tasks.

  • Want to switch from Other Programming Languages to Python.

What you’ll learn

  • Operators used in Python.

  • How to create and use arrays in Python using NumPy

  • Built-in function in NumPy

  • Generating Random Number using NumPy

  • How to plot and visualize the data using Matplotlib.

  • How to create lists and use them in Python

  • for loops and while loops in Python

  • Strings in Python

  • How to create dictionaries and use them in Python.

  • Seaborn Library

  • Pandas

  • Creating Functions in Python

  • Creating Classes in Python

  • Tuples

  • The lambda( ) function

  • map( ), reduce( ) and filter( ) functions

  • zip( ) and join( ) functions

  • Only gmail account for using Google colab
  • Anyone who wants to learn Python from scratch.
  • Students and Researchers in Artificial Intelligence
  • Students and Researchers who want to develop Python Programming skills to solve their Tasks.
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  • Section 1 : Introduction 2 Lectures 00:00:00

    • Lecture 1 :
    • Introduction of the Course Preview
    • Lecture 2 :
    • Course Material
      Dwonload all the course material and then upload them to your google drive. I f you don't know how to upload to google drive then don't worry, it is explained in detailed in the next section " Introduction to Google Colab"
  • Section 2 : Introduction to Google Colab 2 Lectures 00:00:00

    • Lecture 1 :
    • Introduction of the Section
    • Lecture 2 :
    • Using Google Colab
  • Section 3 : Operators in Python 3 Lectures 00:00:00

    • Lecture 1 :
    • Arithmetic With Python
    • Lecture 2 :
    • Comparison and Logical Operations
    • Lecture 3 :
    • Conditional Statements
  • Section 4 : Arrays With NumPy 3 Lectures 00:00:00

    • Lecture 1 :
    • NumPy Arrays-Part01
    • Lecture 2 :
    • NumPy Arrays-Part02
    • Lecture 3 :
    • NumPy Arrays-Part03
  • Section 5 : Matplotlib for Data Visualization 4 Lectures 00:00:00

    • Lecture 1 :
    • Plotting and Visualization Part-01
    • Lecture 2 :
    • Plotting and Visualization Part-02
    • Lecture 3 :
    • Plotting and Visualization Part-03
    • Lecture 4 :
    • Plotting and Visualization Part-04
  • Section 6 : Lists In Python 1 Lectures 00:00:00

    • Lecture 1 :
    • Lists In Python
  • Section 7 : Loops in Python 3 Lectures 00:00:00

    • Lecture 1 :
    • For Loops Part-01
    • Lecture 2 :
    • For Loops Part-02
    • Lecture 3 :
    • while loop
  • Section 8 : Strings in Python 2 Lectures 00:00:00

    • Lecture 1 :
    • Strings in Python
    • Lecture 2 :
    • Print formatting With Strings
  • Section 9 : Dictionaries In Python 2 Lectures 00:00:00

    • Lecture 1 :
    • Dictionaries Part-01
    • Lecture 2 :
    • Dictionaries Part-02
  • Section 10 : Seaborn 3 Lectures 00:00:00

    • Lecture 1 :
    • Seaborn Part-01
    • Lecture 2 :
    • Seaborn Part-02
    • Lecture 3 :
    • Seaborn Part-03
  • Section 11 : Pandas 4 Lectures 00:00:00

    • Lecture 1 :
    • Pandas Part-01
    • Lecture 2 :
    • Pandas Part-02
    • Lecture 3 :
    • Pandas Part-03
    • Lecture 4 :
    • Pandas Part-04
  • Section 12 : Functions in Python 2 Lectures 00:00:00

    • Lecture 1 :
    • Functions in Python Part-01
    • Lecture 2 :
    • Functions in Python Part-02
  • Section 13 : Creating Classes in Python 1 Lectures 00:00:00

    • Lecture 1 :
    • Classes in Python
  • Section 14 : Tuples 1 Lectures 00:00:00

    • Lecture 1 :
    • Tuples
  • Section 15 : The lambda( ) function 1 Lectures 00:00:00

    • Lecture 1 :
    • lambda( ) functions
  • Section 16 : map( ), reduce( ) and filter( ) in Python 3 Lectures 00:00:00

    • Lecture 1 :
    • map( ) function
    • Lecture 2 :
    • reduce( ) function
    • Lecture 3 :
    • filter( ) functions
  • Section 17 : zip() and join() in Python 2 Lectures 00:00:00

    • Lecture 1 :
    • zip( ) function
    • Lecture 2 :
    • join( ) functions
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    It's simple. When you sign up, you'll immediately have unlimited viewing of thousands of expert courses, paths to guide your learning, tools to measure your skills and hands-on resources like exercise files. There’s no limit on what you can learn and you can cancel at any time.
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    Yes absolutely & thats one of the advantage of self-paced courses. You can anytime pause or resume the course & come back & forth from one lecture to another lecture, play the videos mulitple times & so on.
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  • Do i need a pc to access the course or can i do it on mobile & tablet as well?

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  • Is there any free trial?

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  • Can i cancel anytime?

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Dr. Zeeshan is PhD in Electrical and Computer Engineering from Toronto Metropolitan University. He has more than 18 years of teaching and research experience. He has taught many courses related to Computer Science and Computer Engineering. such as Machine Learning, Deep Learning, Image Processing, Computer Vision, Signal Processing and Python Programming. He has publications in reputed journals and conferences.
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