Machine Learning, Neural Networks and Artificial intelligence are big buzzwords of the decade. It is not surprising that today these fields are expanding pretty quickly and are used to solve a vast amount of problems. We are witnesses of the new golden period of these technologies. However, today we are merely innovating. Majority of the concepts used in these fields were invented 50 or more years ago.

Linear algebra, probability and calculus are the'languages' in which machine learning is formulated. Learning these topics will contribute a deeper understanding of the underlying algorithmic mechanics and allow development of new algorithms. When confined to smaller levels, everything is math behind deep learning. So it is essential to understand basic linear algebra before getting started with deep learning and programming it. The core data structures behind Deep-Learning are Scalars, Vectors, Matrices and Tensors.

Linear Algebra is a continuous form of mathematics and is applied throughout science and engineering because it allows you to model natural phenomena and to compute them efficiently. Because it is a form of continuous and not discrete mathematics, a lot of computer scientists don't have a lot of experience with it. Linear Algebra is also central to almost all areas of mathematics like geometry and functional analysis. Its concepts are a crucial prerequisite for understanding the theory behind Machine Learning, especially if you are working with Deep Learning Algorithms. You don't need to understand Linear Algebra before getting started with Machine Learning, but at some point, you may want to gain a better understanding of how the different Machine Learning algorithms really work under the hood.

Linear Algebra is a continuous form of mathematics and it is applied throughout science and engineering because it allows you to model natural phenomena and to compute them efficiently. Because it is a form of continuous and not discrete mathematics, a lot of computer scientists don't have a lot of experience with it. Linear Algebra is also central to almost all areas of mathematics like geometry and functional analysis. Its concepts are a crucial prerequisite for understanding the theory behind Machine Learning, especially if you are working with Deep Learning Algorithms. You don't need to understand Linear Algebra before you get started with Machine Learning but at some point, you want to gain a better intuition for how the different machine learning algorithms really work under the hood.

Deep learning involves a lot of matrix math, and it's important for you to understand the basics before diving into building your own neural networks. These lessons provide a short refresher on what you need to know for this course, along with some guidance for using the NumPy library to work efficiently with matrices in Python. Python is convenient, but it can also be slow. However, it does allow you to access libraries that execute faster code written in languages like C. NumPy is one such library: it provides fast alternatives to math operations in Python and is designed to work efficiently with groups of numbers -- like matrices. NumPy is a large library and we are only going to scratch the surface of it here.