Practical Linear Algebra for Data Professionals: Build unique solutions for your data science problems by coding linear algebra in Python

Begin as a novice and become an expert data scientist who implements linear algebra through Python programs About This Book * Delve deep into the mathematics behind data science applications *Implement popular machine learning algorithms with Python and linear algebra *Explore in detail the key elements of linear algebra, such as linear transformation and algebraic linear equation system Who This Book Is For If you want to gather a deep understanding of the mathematics behind python packages and want to apply these concepts for your own data science problems, this is an ideal book for you. To grasp the concepts explained here, you must be able to write basic code in Python and understand basic mathematics and linear algebra. What You Will Learn * Get to grips with concepts, such as scalars, vectors, matrices, and tensors *Calculate the Euclidean distance between two n-dimensional points *Set a colorful image to black and white and change its brightness and contrast *Use the PCA algorithm to improve accuracy in a regression problem *Compress data using the SVD algorithm *Make predictions with continuous data using the scikit-learn library In Detail In the world of data science, linear algebra is the cornerstone of analysis and prediction. With the very powerful data science libraries of Python, you can build applications that use complex linear algebra to complete the tasks. At the beginning of the book, you'll explore the idea behind linear algebra and study its background to understand and apply it to problems. You'll explore ways to use linear algebra in your applications with NumPy and Pandas, the data science libraries of Python. As you progress, you'll learn how to use linear transformation to changing the colors of an image, encrypt, and decrypt it. By learning techniques, such as Gaussian elimination and LU decomposition, you'll be able to develop applications that easily perform complex numerical analysis. You'll also learn how to construct a curve or a mathematical function that'll help predict the missing points in your data. In the final topics of the book, you'll learn how to use linear algebra in four famous algorithms: Linear Regression, Support Vector Machine (SVM), Singular Value Decomposition (SVD), and Principal Component Analysis (PCA). By the end of the book, you'll have the confidence and the skill to develop highly-efficient data science applications with linear algebra and Python.

About the Author :
Joe Ganser holds a masters in applied physics from Columbia University in NY. He was previously a physicist and worked on a Ph.D. in physics before going into data science. His current research interests have been in marketing analytics, algorithmic finance, quantum optics, and healthcare. Abhinav Maurya is a research scientist at Amazon with research interests spanning artificial intelligence, machine learning, data science, computational social science, and AI strategy and policy. He has studied Computer Science and Engineering at IIT Bombay and Machine Learning and Public Policy at Carnegie Mellon University. In the past, he has worked at or collaborated with many cutting-edge technology corporations such as Microsoft, LinkedIn, Yelp, Uber, Robert Bosch GmbH, and IBM Research.