# Free Machine Learning & Data Science Foundations Masterclass

Free Machine Learning & Data Science Foundations Masterclass: Learn The Theoretical and Practical Foundations of Machine Learning. Master Matrices, Linear Algebra, and Tensors in Python

Summary

## Description

To be a good data scientist, you need to know how to use data science and machine learning libraries and algorithms, such as NumPy, TensorFlow and PyTorch, to solve whichever problem you have at hand.

The first step in your journey into becoming an excellent data scientist is broken down as follows:

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• Section 1: Linear Algebra Data Structures
• Section 2: Tensor Operations
• Section 3: Matrix Properties
• Section 4: Eigenvectors and Eigenvalues
• Section 5: Matrix Operations for Machine Learning

## Who this course is for:

• You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities
• You’re a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems
• You’re a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline
• You’re a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!)

## Course Contain

• Data Structure for algebra
• Linear Algebra Exercise
• Tensors
• Scalars
• Vectors and Vector Transposition
• Norms and Unit Vectors
• Basis, Orthogonal, and Orthonormal Vectors
• Matrix Tensors
• Generic Tensor Notation
• Exercises on Algebra Data Structures
• Common tensor operation
• Segment Intro
• Tensor Transposition
• Basic Tensor Arithmetic, incl. the Hadamard Product
• Tensor Reduction
• The Dot Product
• Exercises on Tensor Operations

## What you’ll learn

• Understand the fundamentals of linear algebra, a ubiquitous approach for solving for unknowns within high-dimensional spaces.
• Manipulate tensors using the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch
• Possess an in-depth understanding of matrices, including their properties, key classes, and critical ML operations
• Develop a geometric intuition of what’s going on beneath the hood of ML and deep learning algorithms.
• Be able to more intimately grasp the details of cutting-edge machine learning papers

## This course includes:

• 2 hours on-demand video