HOT
blank

Learn Python & OpenCV for Computer Vision Deep Learning, OCR

Development

Rating4.9
Enroll Now

Learn Python & OpenCV for Computer Vision Deep Learning, OCR, available at $59.99, has an average rating of 4.9, with 72 lectures, 2 quizzes, based on 24 reviews, and has 117 subscribers.

You will learn about Learn Python from the ground up and build your own computer vision and deep learning solutions. Understand Python data types, operators, loops, functions, modules, and file handling, as well as best coding practices. Master advanced Python concepts such as lambda functions, object-oriented programming, decorators, and generators. Learn to use Python built-in libraries such as DateTime, Math, Random, Statistics, Sys, and OS. Gain expertise in Numpy, Pandas, Matplotlib, and OpenPyXL for high-performance data manipulation and visualization. Build a strong foundation in OpenCV to work with images and videos efficiently. Use OpenCV to perform image thresholding, noise removal, cropping, rotation, annotation, and detection. Apply OpenCV to live webcam and recorded video streams. Develop Python solutions for web scraping, sending emails using Flask, and extracting text from PDF documents. Build OpenCV solutions for template matching and object tracking in real time. This course is ideal for individuals who are Beginners to Python and OpenCV who are interested in learning computer vision and deep learning. or Software developers who want to add computer vision and deep learning capabilities to their applications. or Data scientists who want to use computer vision and deep learning to analyze and interpret data. or Researchers who want to use computer vision and deep learning to develop new technologies and solutions. or Anyone who is interested in learning about computer vision and deep learning, regardless of their background or experience. It is particularly useful for Beginners to Python and OpenCV who are interested in learning computer vision and deep learning. or Software developers who want to add computer vision and deep learning capabilities to their applications. or Data scientists who want to use computer vision and deep learning to analyze and interpret data. or Researchers who want to use computer vision and deep learning to develop new technologies and solutions. or Anyone who is interested in learning about computer vision and deep learning, regardless of their background or experience.

Enroll now: Learn Python & OpenCV for Computer Vision Deep Learning, OCR

Summary

Title: Learn Python & OpenCV for Computer Vision Deep Learning, OCR

Price: $59.99

Average Rating: 4.9

Number of Lectures: 72

Number of Quizzes: 2

Number of Published Lectures: 72

Number of Published Quizzes: 2

Number of Curriculum Items: 74

Number of Published Curriculum Objects: 74

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

  • Learn Python from the ground up and build your own computer vision and deep learning solutions.
  • Understand Python data types, operators, loops, functions, modules, and file handling, as well as best coding practices.
  • Master advanced Python concepts such as lambda functions, object-oriented programming, decorators, and generators.
  • Learn to use Python built-in libraries such as DateTime, Math, Random, Statistics, Sys, and OS.
  • Gain expertise in Numpy, Pandas, Matplotlib, and OpenPyXL for high-performance data manipulation and visualization.
  • Build a strong foundation in OpenCV to work with images and videos efficiently.
  • Use OpenCV to perform image thresholding, noise removal, cropping, rotation, annotation, and detection.
  • Apply OpenCV to live webcam and recorded video streams.
  • Develop Python solutions for web scraping, sending emails using Flask, and extracting text from PDF documents.
  • Build OpenCV solutions for template matching and object tracking in real time.

Who Should Attend

  • Beginners to Python and OpenCV who are interested in learning computer vision and deep learning.
  • Software developers who want to add computer vision and deep learning capabilities to their applications.
  • Data scientists who want to use computer vision and deep learning to analyze and interpret data.
  • Researchers who want to use computer vision and deep learning to develop new technologies and solutions.
  • Anyone who is interested in learning about computer vision and deep learning, regardless of their background or experience.

Target Audiences

  • Beginners to Python and OpenCV who are interested in learning computer vision and deep learning.
  • Software developers who want to add computer vision and deep learning capabilities to their applications.
  • Data scientists who want to use computer vision and deep learning to analyze and interpret data.
  • Researchers who want to use computer vision and deep learning to develop new technologies and solutions.
  • Anyone who is interested in learning about computer vision and deep learning, regardless of their background or experience.

Become a Computer Vision and Deep Learning Expert with Python and OpenCV

In this comprehensive course, you’ll learn everything you need to know to master computer vision and deep learning with Python and OpenCV. You’ll start with the basics of Python and OpenCV, and then gradually work your way up to more advanced topics, such as:

  • Image processing

  • Object detection and tracking

  • Scene classification

  • Image segmentation

  • Deep learning models for computer vision

You’ll also learn how to build your own computer vision and deep learning applications using Python and OpenCV. By the end of the course, you’ll be able to develop cutting-edge computer vision solutions to real-world problems.

What sets this course apart?

  • Comprehensive coverage of both Python and OpenCV

  • 35+ supporting notebooks available for download

  • 5 different varieties of projects to develop

  • Tutorials on setting up PyCharm and using Google Colab

  • Regular assessments to help you track your progress

Why learn Computer Vision and Deep Learning?

Computer vision and Deep Learning are two of the most rapidly growing fields in technology today. They are used in a wide variety of applications, including:

  • Self-driving cars

  • Medical imaging

  • Robotics

  • Security and surveillance

  • Social media and entertainment

If you’re interested in a career in computer vision or deep learning, or if you simply want to learn about these exciting technologies, then this course is for you!

Course Curriculum

Chapter 1: Course Starter

Lecture 1: Learning Path

Lecture 2: Course Starter – How to approach the course

Lecture 3: Udemy Review

Chapter 2: Introduction to Python

Lecture 1: Objectives

Lecture 2: What is Python?

Lecture 3: Journey of Python

Lecture 4: Why Learn Python?

Chapter 3: Python Setup

Lecture 1: Objectives

Lecture 2: Python Setup on Windows

Lecture 3: Python Setup on Ubuntu

Lecture 4: Using Google Colab

Chapter 4: Data Types and Operators

Lecture 1: Objectives

Lecture 2: Variables and Data Types

Lecture 3: Data Type – Numeric and Boolean with Variables

Lecture 4: Data Type – String

Lecture 5: Data Type – Lists

Lecture 6: Data Type -Tuples

Lecture 7: Data Type – Sets

Lecture 8: Data Type – Dictionaries

Lecture 9: Operators

Chapter 5: Loops – If-Else, For, While

Lecture 1: Objectives

Lecture 2: If Elif and Else Statements

Lecture 3: For and While Loop

Chapter 6: Functions, Modules & File Handling

Lecture 1: Objectives

Lecture 2: Functions with *args and **kwargs Arguments

Lecture 3: Modules and Packages

Lecture 4: I/O with Basic Files

Chapter 7: Popular Coding Practices and Exception Handling

Lecture 1: Objectives

Lecture 2: Popular Coding Practices

Lecture 3: Exception Handling

Chapter 8: Advanced Functions – Lambda, Map, Filter, Reuse

Lecture 1: Objectives

Lecture 2: Lambda Expressions, Map, and Filter Functions

Chapter 9: Object Oriented Programming, Decorator and Generator

Lecture 1: Objectives

Lecture 2: Object Oriented Programming Concept

Lecture 3: Object Oriented Programming Implementation

Lecture 4: Decorator

Lecture 5: Generator

Chapter 10: Built-in Modules – DateTime, Math, Random, Statistics, Sys, OS

Lecture 1: Objectives

Lecture 2: DateTime

Lecture 3: Math, Random and Statistics

Lecture 4: Sys and OS

Chapter 11: External Libraries – Numpy, Pandas, Matplotlib, OpenPyXL

Lecture 1: Objectives

Lecture 2: Numpy

Lecture 3: Pandas

Lecture 4: Matplotlib

Lecture 5: OpenPyXL

Chapter 12: Introduction to OpenCV

Lecture 1: Objectives

Lecture 2: What is OpenCV?

Lecture 3: Tool Setup – OpenCV – Ubuntu

Lecture 4: Tool Setup – OpenCV – Windows

Lecture 5: Image Basics

Lecture 6: Image Reading , Writing and Resizing

Chapter 13: Image Thresholding

Lecture 1: Objectives

Lecture 2: Simple Thresholding, Adaptive Thresholding and Otsu's Binarization

Chapter 14: Image Noise Removal

Lecture 1: Objectives

Lecture 2: Noise Removal – Small Dots, Image Blurring, Dilation, Erosion and Kernels

Chapter 15: Image Cropping & Rotation Techniques

Lecture 1: Objectives

Lecture 2: Image Cropping & Rotation

Chapter 16: Image Annotation

Lecture 1: Objectives

Lecture 2: Draw Text, Rectangle, Circle and Line on Image

Chapter 17: Image Detection Techniques

Lecture 1: Objectives

Lecture 2: Image Detection – Blob, Edge and Contour

Chapter 18: OpenCV for Videos

Lecture 1: Objectives

Lecture 2: Reading from Recorded Video & Reading and Writing from LIVE camera

Chapter 19: OpenCV for OCR

Lecture 1: Overview of OCR using EasyOCR

Lecture 2: How to Implement EasyOCR for OCR Operations

Chapter 20: Project 1 – Python Web Scraping using BeautifulSoup and RegEx

Lecture 1: Project Demo – Python Web Scraping using BeautifulSoup and RegEx

Chapter 21: Project 2 – Sending Email with Python (Flask)

Lecture 1: Project Demo – Sending Email with Python (Flask)

Chapter 22: Project 3 – Extract text from PDF using Python

Lecture 1: Project Demo – Extract text from PDF using Python

Chapter 23: Project 4 – Template matching using OpenCV

Lecture 1: Project Demo – Template matching using OpenCV

Chapter 24: Project 5 – Track Object by Marking in Live Camera using OpenCV

Lecture 1: Project Demo – Track Object by Marking in Live Camera using OpenCV

Chapter 25: More Learnings

Lecture 1: More Learnings

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 0 votes
  • 3 stars: 1 votes
  • 4 stars: 3 votes
  • 5 stars: 19 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

You may also like

Top 10 Game Development Courses to Learn in December 2024

Top 10 Software Testing Courses to Learn in December 2024

Top 10 Internet Of Things Courses to Learn in December 2024