Learn to use Python professionally, learning both Python 2 and Python 3!
Learn advanced Python features, like the collections module and how to work with timestamps!
Create games with Python, like Tic Tac Toe and Blackjack!
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!
About this course
This is the most comprehensive, yet straight-forward, course for the Python programming language on Udemy! Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you Python 3. (Note, we also provide older Python 2 notes in case you need them)
With over 100 lectures and more than 20 hours of video this comprehensive course leaves no stone unturned! This course includes quizzes, tests, and homework assignments as well as 3 major projects to create a Python project portfolio!
This course will teach you Python in a practical manner, with every lecture comes a full coding screencast and a corresponding code notebook! Learn in whatever manner is best for you!
We will start by helping you get Python installed on your computer, regardless of your operating system, whether its Linux, MacOS, or Windows, we've got you covered!
Understand how convolution can be applied to image effects.
Understand how convolution can be applied to audio effects.
Implement Gaussian blur and edge detection in code.
Implement a simple echo effect in code
Understand how convolution helps image classification.
Understand and explain the architecture of a convolutional neural network (CNN)
Implement a convolutional neural network in Theano
Implement a convolutional neural network in TensorFlow.
About this course
This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.
In this course we are going to up the ante and look at the StreetView House Number (SVHN) dataset - which uses larger color images at various angles - so things are going to get tougher both computationally and in terms of the difficulty of the classification task. But will show that convolutional neural networks, or CNNs, are capable of handling the challenge!
Because convolution is such a central part of this type of neural network, we are going to go in-depth on this topic. It has more applications than you might imagine, such as modeling artificial organs like the pancreas and the heart. I'm going to show you how to build convolutional filters that can be applied to audio, like the echo effect, and I'm going to show you how to build filters for image effects, like the Gaussian blur and edge detection.
What is Raspberry Pi? and what are its components?
Understand peripherals that need to be connected to Raspberry Pi
Wire up your Raspberry Pi to create a fully functional computer
Install packages needed to build Image Processing applications
Learn basic programming aspects of Python
Create simple Image Processing applications using Python and OpenCV
Build real-world Image Processing applications on Raspberry Pi
About this course
Image Processing Applications on Raspberry Pi is a beginner course on the newly launched Raspberry Pi 3 and is fully compatible with Raspberry Pi 2 and Raspberry Pi Zero.
You will learn the components of Raspberry Pi, connecting components to Raspberry Pi, installation of NOOBS operating system, basic Linux commands, Python programming and building Image Processing applications on Raspberry Pi.
Users can quickly learn hardware assembly and coding in Python programming for building Image Processing applications. By the end of this course, users will have enough knowledge about Raspberry Pi, its components, basic Python programming, and execution of Image Processing applications in the real time scenario.