Learn Advanced Python Programming
Learn Advanced Python Programming, available at $64.99, has an average rating of 3.95, with 111 lectures, based on 230 reviews, and has 4150 subscribers.
You will learn about Students will learn about some of the advanced topics in Python. It covers a lot of very interesting topics related to machine learning It covers the intuition part of different Python Concepts It covers some Advance Level Applications made in Python This course is ideal for individuals who are Anyone who wants to learn the advanced python programming topics can enroll in this course. It is particularly useful for Anyone who wants to learn the advanced python programming topics can enroll in this course.
Enroll now: Learn Advanced Python Programming
Summary
Title: Learn Advanced Python Programming
Price: $64.99
Average Rating: 3.95
Number of Lectures: 111
Number of Published Lectures: 111
Number of Curriculum Items: 111
Number of Published Curriculum Objects: 111
Original Price: $94.99
Quality Status: approved
Status: Live
What You Will Learn
- Students will learn about some of the advanced topics in Python.
- It covers a lot of very interesting topics related to machine learning
- It covers the intuition part of different Python Concepts
- It covers some Advance Level Applications made in Python
Who Should Attend
- Anyone who wants to learn the advanced python programming topics can enroll in this course.
Target Audiences
- Anyone who wants to learn the advanced python programming topics can enroll in this course.
In this course you will be able to learn about a lot of advanced python programming topics. This course covers topics that are related to machine learning as well many other advanced python programming topics. Anyone who is interested in learning python advanced programming can enroll in this course. It covers all the levels may it be beginner level python programmer, intermediate level python programmer or advanced level python programmer, he/she can enroll in this course and when he completes this course he will surely be enriched with the new and the advanced concepts of python programming. Basics of python is the pre requisite of this course but don’t worry at all, if you don’t have the basic knowledge of python then I have uploaded a crash course at the very end of this course. You can always start from the beginning and cover the basic concepts from the crash course and after that start on with the advanced topics that are a part of this course. Someone who has got advanced knowledge of python programming can also enroll in this course as this course covers the latest trends and updates in the language. This course has the latest advanced topics of python covered and I hope that you learn a lot from this course. Thank You.
Course Curriculum
Chapter 1: Setting up Python and Integrated Development Environment IDE
Lecture 1: Setting up Python and Integrated Development Environment IDE
Chapter 2: Numpy : Numerical Computation in Python
Lecture 1: Numpy Intuition
Lecture 2: Numpy – I
Lecture 3: Numpy – II
Lecture 4: Numpy – III
Lecture 5: Numpy – IV
Lecture 6: Numpy – V
Lecture 7: Numpy – VI
Lecture 8: Numpy – VII
Chapter 3: Pandas : Data Handling Techniques
Lecture 1: Pandas : Data File
Lecture 2: Pandas – I
Lecture 3: Pandas – II
Lecture 4: Pandas – III
Lecture 5: Pandas – IV
Lecture 6: Pandas – V
Lecture 7: Pandas – VI
Lecture 8: Pandas – VII
Lecture 9: Pandas – VIII
Lecture 10: Pandas – IX
Lecture 11: Pandas – X
Lecture 12: Pandas – XI
Lecture 13: Pandas – XII
Lecture 14: Pandas – XIII
Chapter 4: Graphs
Lecture 1: Understanding Graphs
Lecture 2: Installing the Matplotlib Module
Lecture 3: Drawing a Simple Line Graph
Lecture 4: Multi-line Graph
Lecture 5: Drawing a Bar Graph
Lecture 6: Styling a Bar Graph
Lecture 7: Scatter Graph
Lecture 8: Pie Graph
Lecture 9: Histogram
Lecture 10: Using Numpy to make Graphs
Chapter 5: Linked List : Singly and Doubly Linked List
Lecture 1: Introduction
Lecture 2: Singly linked list and doubly linked list
Lecture 3: Create and Traverse SLL
Lecture 4: Insertion in SLL
Lecture 5: Deletion in SLL
Lecture 6: Creating Doubly linked list
Lecture 7: Insertion in DLL
Lecture 8: Append in DLL
Lecture 9: Deletion in DLL
Chapter 6: Regular Expressions
Lecture 1: Introduction
Lecture 2: RE Module Function
Lecture 3: Match Function
Lecture 4: Some Comparisons
Lecture 5: Modifiers
Lecture 6: Examples
Chapter 7: GUI Based Text Editor
Lecture 1: Text Editor – I
Lecture 2: Text Editor – II
Lecture 3: Text Editor – III
Chapter 8: Making a PDF Audio Reader Application
Lecture 1: Introduction to the Application
Lecture 2: Modules Introduction
Lecture 3: Extracting Text From PDF File
Lecture 4: Speak Hello World
Lecture 5: GUI Application 1
Lecture 6: GUI Application 2
Lecture 7: Control Flow Function
Lecture 8: Extracting Text From PDF File
Lecture 9: Configuring the GUI Application
Chapter 9: Single Linear Regression Model
Lecture 1: Simple Linear Regression Intuition – I
Lecture 2: Simple Linear Regression Intuition – II
Lecture 3: Simple Linear Regression Intuition – III
Lecture 4: Simple Linear Regression Intuition Code – I
Lecture 5: Simple Linear Regression Intuition Code – II
Lecture 6: Simple Linear Regression Intuition Code – III
Lecture 7: Simple Linear Regression Intuition Code – IV
Lecture 8: Simple Linear Regression Intuition Code – V
Lecture 9: Simple Linear Regression Intuition Code – VI
Lecture 10: Simple Linear Regression Intuition Code – VII
Chapter 10: Multiple Linear Regression Model
Lecture 1: Multiple Linear Regression Intuition – I
Lecture 2: Multiple Linear Regression Intuition – II
Lecture 3: Multiple Linear Regression Intuition – III
Lecture 4: Multiple Linear Regression Intuition – IV
Lecture 5: Multiple Linear Regression Intuition – V
Lecture 6: Multiple Linear Regression Intuition – VI
Lecture 7: Multiple Linear Regression Code – I
Lecture 8: Multiple Linear Regression Code – II
Lecture 9: Multiple Linear Regression Code – III
Lecture 10: Multiple Linear Regression Code – IV
Lecture 11: Multiple Linear Regression Code – V
Lecture 12: Multiple Linear Regression Code – VI
Lecture 13: Multiple Linear Regression Code – VII
Chapter 11: Polynomial Regression
Lecture 1: Polynomial Regression Intuition – I
Lecture 2: Polynomial Regression Code – I
Lecture 3: Polynomial Regression Code – II
Lecture 4: Polynomial Regression Code – III
Chapter 12: Logistic Regression
Lecture 1: Logistic Regression Intuition – I
Instructors
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Python School
Data Scientist, Entrepreneur and Traveler
Rating Distribution
- 1 stars: 3 votes
- 2 stars: 4 votes
- 3 stars: 9 votes
- 4 stars: 15 votes
- 5 stars: 199 votes
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