Python Image Recognition: Hands-On Data Science Course [Udemy Free Course]

Learn data science and Python image recognition programmingBuddyClub

Discount - Free

Python image recognition sounds exciting, right? However, it can also seem a bit intimidating. There's no need to be scared! This tutorial will teach you Python basics and how to use TensorFlow. Take this chance to discover how to code in Python and learn TensorFlow linear regression then apply these principles to automated Python image recognition.

Maximum Results in Minimal Time

Through this course, you'll master Python image recognition software and learn with hands-on examples. That's right! This course interweaves theory with practical examples to make your learning experience as immersive as possible. You will learn to take full advantage of Artificial Intelligence (AI) and start working smarter, not harder.

You can finish this course in one afternoon. It is short but full of crucial information. The whole tutorial is split into three sections. You'll start with Python image recognition. After that, you'll move on to TensorFlow basics. The last section will explain how to utilize the CIFAR-10 dataset to train machine learning and computer vision algorithm. All of the information will be explained slowly and steadily. And since it's all in short video lessons format, it will be easy to follow and digest.

Image Recognition Model from Scratch

In the Python image recognition section, you'll find video lectures showing you how to download and install Python and PyCharm. After that, you'll learn Python basics. You'll discover how basic types of variables work as well as how to perform operations on numbers and strings. Then you'll move on to more complex concepts like multi-value variables, controlling flow, functions, and classes.

In the second part of this course, you'll learn TensorFlow. Your tutor will explain what it is and how to set it up. You'll discover what constant & operation nodes, placeholder nodes, and variable nodes are as well as how to use and run them. In the end, you'll build a functional linear regression model.

After you learn Python and TensorFlow, you'll move on to the last section of the course. This part will explain how to install and import libraries using the CIFAR-10 dataset. You'll discover how to display and play with CIFAR-10 images using PIL (Python Imaging Library) as well as how to retrieve data from them. Eventually, using all the knowledge you previously gained, you'll build a simple image recognition model and a training data set. In the end, you'll test out the model and see how it all works!

Fun to Use & Easy to Learn

It's crucial to learn Python if you want to work with Artificial Intelligence (AI). It's one of the most popular programming languages in the world. Python is highly versatile and is known as a general-purpose language. It can be used to program practically anything you want. Python is a brilliant choice for data analysis, artificial intelligence, scientific computing and, backend web development. You can also use Python to code games, productivity tools, and desktop apps.

The good news is that Python is easy to learn. It is a very high-level programming language which means that it reads like English. Therefore, it's a perfect language to begin programming for a newbie. Python was created to be easy to understand and exciting to use. Even the name is a reference to an iconic British comedy group Monthy Python. Therefore, with the right tutorials to guide you, learning and using Python will prove to be a fun and engaging task.

Don't waste any more time. Kickstart your career by learning Python and TensorFlow as well as creating a working image recognition model. Enroll now!

• Lectures -28
• Skill Level -Beginner
• Language - English
• Published -9/2018

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Course Includes

• How to Code Using Python
• How to Build Models of Linear Regression in TensorFlow
• How to Make an Image Recognition Model Using CIFAR-10

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What Will I learn?

• 📺 5h 22m on-demand video
• 📱 Access on mobile
• 💓 Lifetime access

Requirements

• ⚓ Windows PC, Mac OS, or Linux
• ⚓ PyCharm Community Edition 2017.2.3.

Who is the target audience?

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