Learn to implement 10 Machine Learning algorithms from scratch with just Python and NumPy. A library hides the implementation details and if you're really looking to understand what goes behind the covers and understand how things work, this course has you covered. This is a course by AssemblyAI where you don't rely on libraries like Pytorch or Tensorflow to implement the Machine learning algorithms but you implement them yourself from scratch with nothing but Python and NumPy. You need basic Python, object oriented programming and the basics of NumPy to follow along as it's a practical course with a lot of code. However scary math formulas are referred too.If you do have experience with Andrew Ng's deep learning courses which require high school level math and teach the basics of the notations you should not face any issues even on that part.

If you are starting to work with convolutional layers in deep learning you may be confused at times with the mix of parameters, computations and channels involved. From stripe to padding, input and output channels, kernels and learnable parameters, there is a lot going on. In this article, we are going to go deep till the very bottom of what goes on within these conv layers. First, we initialize the libraries we will need. This will allow us to code both with pytorch and with the numpy library so that we can compare their results later.

If you are starting to work with convolutional layers in deep learning you may be confused at times with the mix of parameters, computations and channels involved. From stripe to padding, input and output channels, kernels and learnable parameters, there is a lot going on. In this article, we are going to go deep till the very bottom of what goes on within these conv layers. First, we initialize the libraries we will need. This will allow us to code both with pytorch and with the numpy library so that we can compare their results later.

Description Python is the most emerging programming language in the world. It is used for web and software development. It has a lot of things that you can't consider in other programming languages. You will learn and everything by coding the programs. This course will take you from beginner to expert in Python, easily and smartly.

During my journey as a Machine Learning (ML) practitioner, I found it has become ultimately easy for any human with limited knowledge on algorithms to take advantage of free python libraries such as scikit-learn to solve a ML problem. Truth be said, it's easy and sometimes no brainer to achieve this, as there are so many codes available in GitHub, Medium, Kaggle etc., You just need some amount of time looking at these codes to arrive at a solution to a problem of your choice. But, what if we learn every algorithm or procedures behind each machine learning pipeline that does all the heavy lifting for us inside these amazing libraries. In this blog post and the series of blog posts to come, I will be focusing on implementing machine learning algorithms from scratch using python and numpy. Sure you might argue with me for the first paragraph.