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The Essential Machine Learning Foundations

Published by Pearson (March 25, 2022)

ISBN-13: 9780137903238

  • Course

$899.99

Product details

28.2 hours of video; Quizzes; Credly badging; 365-day course access

Includes

  • Machine learning theory
  • Linear algebra fundamentals
  • Deep learning ML algorithms
  • Data variable type and probability distribution

Language: English

Product Information

An outstanding data scientist or machine learning engineer must master more than the basics of using ML algorithms with the most popular libraries, such as scikit-learn and Keras. To train innovative models or deploy them to run performantly in production, an in-depth appreciation of machine learning theory is essential, which includes a working understanding of the foundational subjects of linear algebra, calculus, probability, statistics, data structures, and algorithms. When the foundations of machine learning are firm, it becomes easier to make the jump from general ML principles to specialized ML domains, such as deep learning, natural language processing, machine vision, and reinforcement learning.

This master class includes the following courses:

  • Linear Algebra for Machine Learning
  • Calculus for Machine Learning
  • Probability and Statistics for Machine Learning
  • Data Structures, Algorithms, and Machine Learning Optimization

Linear Algebra for Machine Learning (Machine Learning Foundations): Introduction

Lesson 1: Orientation to Linear Algebra

Lesson 2: Data Structures for Algebra

Lesson 3: Common Tensor Operations

Lesson 4: Solving Linear Systems

Lesson 5: Matrix Multiplication

Lesson 6: Special Matrices and Matrix Operations

Lesson 7: Eigenvectors and Eigenvalues

Lesson 8: Matrix Determinants and Decomposition

Lesson 9: Machine Learning with Linear Algebra

Summary

Calculus for Machine Learning: Introduction

Lesson 1: Orientation to Calculus

Lesson 2: Limits

Lesson 3: Differentiation

Lesson 4: Advanced Differentiation Rules

Lesson 5: Automatic Differentiation

Lesson 6: Partial Derivatives

Lesson 7: Gradients

Lesson 8: Integrals

Summary

Probability and Statistics for Machine Learning: Introduction

Lesson 1: Introduction to Probability

Lesson 2: Random Variables

Lesson 3: Describing Distributions

Lesson 4: Relationships Between Probabilities

Lesson 5: Distributions in Machine Learning

Lesson 6: Information Theory

Lesson 7: Introduction to Statistics

Lesson 8: Comparing Means

Lesson 9: Correlation

Lesson 10: Regression

Lesson 11: Bayesian Statistics

Summary

Data Structures, Algorithms, and Machine Learning Optimization: Introduction

Lesson 1: Orientation to Data Structures and Algorithms

Lesson 2: "Big O" Notation

Lesson 3: List-Based Data Structures

Lesson 4: Searching and Sorting

Lesson 5: Sets and Hashing

Lesson 6: Tress

Lesson 7: Graphs

Lesson 8: Machine Learning Optimization

Lesson 9: Fancy Deep Learning Optimizers

Summary

Jon Krohn is Co-Founder and Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the data science industry's most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at leading universities and conferences, as well as via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.

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