Computational Linear Algebra with Python & NumPy

Why take this course?
🎓 Unlock the Power of Linear Algebra with Python & NumPy!
🚀 Course Title: Computational Linear Algebra with Python & NumPy
👨🏫 Instructor: Christ Raharjasa
🔥 Headline: Master Linear Algebra Computations using NumPy & SciPy, Matrix Operations, Linear Decomposition, and Principal Component Analysis!
Course Description:
Welcome to the Computational Linear Algebra with Python & NumPy course - your gateway to mastering the intersection of linear algebra and Python programming. This course is meticulously designed for data scientists, machine learning engineers, and anyone eager to strengthen both their coding prowess in Python and their mathematical acumen in linear algebra.
What You'll Learn:
📚 Introduction to Linear Algebra: We'll kick off by covering the fundamental concepts of linear algebra, its practical applications, and the key mathematical tools you need to be successful.
🔢 Scalar, Vectors, Matrices & Tensors: Dive into the world of scalar, vectors, matrices, and tensors - the building blocks of data manipulation. You'll understand their roles in Python programming with NumPy.
- ✨ Matrix Operations: From addition and subtraction to multiplication, you'll learn how to perform these operations efficiently with NumPy.
- 🔄 Inverse & Transpose: Discover the practical applications of matrix inverse and transpose using NumPy for your data science projects.
- 🎴 Determinants: Calculate determinants both by hand and with NumPy, essential for understanding matrix properties.
🚀 Linear Equations & Eigenvalues: Solve complex systems of linear equations and calculate eigenvalues and eigenvectors using Python, enhancing your problem-solving skills.
🧙♂️ Linear Decomposition (LU, QR, Cholesky): Learn the theoretical background and practical implementation of LU, QR, and Cholesky decompositions using NumPy.
🛠️ Tensors in NumPy: Understand how to create, manipulate, and utilize tensors for more complex data analysis tasks.
🔬 Singular Value Decomposition (SVD): Explore the SVD technique and its applications, such as image compression.
🌍 Real-World Projects: Apply your newfound skills to real-world projects including:
- 🎬 Building a recommendation engine using linear decomposition.
- 🖼️ Creating an image compressor with singular value decomposition.
- 🏠 Predicting real estate market trends using linear regression.
- 📝 Performing text mining with non negative matrix factorization (NMF).
- ➡️ Reducing dimensionality and extracting features using principal component analysis (PCA).
Why Computational Linear Algebra?
Linear algebra is the bedrock upon which many advanced mathematical concepts in machine learning, data science, and engineering are built. By mastering these skills, you'll be able to:
- 🤖 Implement algorithms central to machine learning such as linear regression, support vector machines, and neural networks.
- 📊 Analyze large datasets more efficiently and solve optimization problems.
- 🚀 Model physical systems, design control systems, and solve differential equations in engineering applications.
Join us on this journey to harness the power of computational linear algebra with Python & NumPy! 🚀
Enroll now and transform your data science and programming skills to new heights! 🌟
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