Math for Machine Learning

Math for Machine Learning, available at $39.99, has an average rating of 3.75, with 81 lectures, based on 85 reviews, and has 1104 subscribers.

You will learn about Refresh your machine learning knowledge. Apply fundamental techniques of machine learning. Gain a firm foundation in machine learning for furthering your career. Learn a subject crucial for data science and artificial intelligence. This course is ideal for individuals who are Working Professionals or Anyone interested in gaining mastery of machine learning or Data Scientists or AI professionals or Adult Learners or College Students It is particularly useful for Working Professionals or Anyone interested in gaining mastery of machine learning or Data Scientists or AI professionals or Adult Learners or College Students.

Enroll now: Math for Machine Learning

Summary

Title: Math for Machine Learning

Price: $39.99

Average Rating: 3.75

Number of Lectures: 81

Number of Published Lectures: 81

Number of Curriculum Items: 81

Number of Published Curriculum Objects: 81

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Refresh your machine learning knowledge.
Apply fundamental techniques of machine learning.
Gain a firm foundation in machine learning for furthering your career.
Learn a subject crucial for data science and artificial intelligence.

Who Should Attend

Working Professionals
Anyone interested in gaining mastery of machine learning
Data Scientists
AI professionals
Adult Learners
College Students

Target Audiences

Working Professionals
Anyone interested in gaining mastery of machine learning
Data Scientists
AI professionals
Adult Learners
College Students

Would you like to learn a mathematics subject that is crucial for many high-demand lucrative career fields such as:

Computer Science
Data Science
Artificial Intelligence

If you’re looking to gain a solid foundation in Machine Learning to further your career goals, in a way that allows you to study on your own schedule at a fraction of the cost it would take at a traditional university, this online course is for you. If you’re a working professional needing a refresher on machine learning or a complete beginner who needs to learn Machine Learning for the first time, this online course is for you.

Why you should take this online course: You need to refresh your knowledge of machine learning for your career to earn a higher salary. You need to learn machine learning because it is a required mathematical subject for your chosen career field such as data science or artificial intelligence. You intend to pursue a masters degree or PhD, and machine learning is a required or recommended subject.

Why you should choose this instructor: I earned my PhD in Mathematics from the University of California, Riverside. I have created many successful online math courses that students around the world have found invaluable—courses in linear algebra, discrete math, and calculus.

In this course, I cover the core concepts such as:

Linear Regression
Linear Discriminant Analysis
Logistic Regression
Artificial Neural Networks
Support Vector Machines

After taking this course, you will feel CARE-FREE AND CONFIDENT. I will break it all down into bite-sized no-brainer chunks.�I explain each definition and go through each example STEP BY STEP so that you understand each topic clearly. I will also be AVAILABLE TO ANSWER ANY QUESTIONS you might have on the lecture material or any other questions you are struggling with.

Practice problems are provided for you, and detailed solutions are also provided to check your understanding.

30 day full refund if not satisfied.

Grab a cup of coffee and start listening to the first lecture. I, and your peers, are here to help. We’re waiting for your insights and questions! Enroll now!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Linear Regression

Lecture 1: Linear Regression

Lecture 2: The Least Squares Method

Lecture 3: Linear Algebra Solution to Least Squares Problem

Lecture 4: Example: Linear Regression

Lecture 5: Summary: Linear Regression

Lecture 6: Problem Set: Linear Regression

Lecture 7: Solution Set: Linear Regression

Chapter 3: Linear Discriminant Analysis

Lecture 1: Classification

Lecture 2: Linear Discriminant Analysis

Lecture 3: The Posterior Probability Functions

Lecture 4: Modelling the Posterior Probability Functions

Lecture 5: Linear Discriminant Functions

Lecture 6: Estimating the Linear Discriminant Functions

Lecture 7: Classifying Data Points Using Linear Discriminant Functions

Lecture 8: LDA Example 1

Lecture 9: LDA Example 2

Lecture 10: Summary: Linear Discriminant Analysis

Lecture 11: Problem Set: Linear Discriminant Analysis

Lecture 12: Solution Set: Linear Discriminant Analysis

Chapter 4: Logistic Regression

Lecture 1: Logistic Regression

Lecture 2: Logistic Regression Model of the Posterior Probability Function

Lecture 3: Estimating the Posterior Probability Function

Lecture 4: The Multivariate Newton-Raphson Method

Lecture 5: Maximizing the Log-Likelihood Function

Lecture 6: Example: Logistic Regression

Lecture 7: Summary: Logistic Regression

Lecture 8: Problem Set: Logistic Regression

Lecture 9: Solution Set: Logistic Regression

Chapter 5: Artificial Neural Networks

Lecture 1: Artificial Neural Networks

Lecture 2: Neural Network Model of the Output Functions

Lecture 3: Forward Propagation

Lecture 4: Choosing Activation Functions

Lecture 5: Estimating the Output Functions

Lecture 6: Error Function for Regression

Lecture 7: Error Function for Binary Classification

Lecture 8: Error Function for Multi-class Classification

Lecture 9: Minimizing the Error Function using Gradient Descent

Lecture 10: Backpropagation Equations

Lecture 11: Summary of Backpropagation

Lecture 12: Summary: Artificial Neural Networks

Lecture 13: Problem Set: Artificial Neural Networks

Lecture 14: Solution Set: Artificial Neural Networks

Chapter 6: Maximal Margin Classifier

Lecture 1: Maximal Margin Classifier

Lecture 2: Definitions of Separating Hyperplane and Margin

Lecture 3: Maximizing the Margin

Lecture 4: Definition of Maximal Margin Classifier

Lecture 5: Reformulating the Optimization Problem

Lecture 6: Solving the Convex Optimization Problem

Lecture 7: KKT Conditions

Lecture 8: Primal and Dual Problems

Lecture 9: Solving the Dual Problem

Lecture 10: The Coefficients for the Maximal Margin Hyperplane

Lecture 11: The Support Vectors

Lecture 12: Classifying Test Points

Lecture 13: Maximal Margin Classifier Example 1

Lecture 14: Maximal Margin Classifier Example 2

Lecture 15: Summary: Maximal Margin Classifier

Lecture 16: Problem Set: Maximal Margin Classifier

Lecture 17: Solution Set: Maximal Margin Classifier

Chapter 7: Support Vector Classifier

Lecture 1: Support Vector Classifier

Lecture 2: Slack Variables: Points on Correct Side of Hyperplane

Lecture 3: Slack Variables: Points on Wrong Side of Hyperplane

Lecture 4: Formulating the Optimization Problem

Lecture 5: Definition of Support Vector Classifier

Lecture 6: A Convex Optimization Problem

Lecture 7: Solving the Convex Optimization Problem (Soft Margin)

Lecture 8: The Coefficients for the Soft Margin Hyperplane

Lecture 9: The Support Vectors (Soft Margin)

Lecture 10: Classifying Test Points (Soft Margin)

Lecture 11: Support Vector Classifier Example 1

Lecture 12: Support Vector Classifier Example 2

Lecture 13: Summary: Support Vector Classifier

Lecture 14: Problem Set: Support Vector Classifier

Lecture 15: Solution Set: Support Vector Classifier

Chapter 8: Support Vector Machine Classifier

Lecture 1: Support Vector Machine Classifier

Lecture 2: Enlarging the Feature Space

Lecture 3: The Kernel Trick

Lecture 4: Summary: Support Vector Machine Classifier

Chapter 9: Concluding Letter

Lecture 1: Concluding Letter

Lecture 2: Bonus Lecture

Instructors

Richard Han
PhD in Mathematics

Rating Distribution

1 stars: 6 votes
2 stars: 13 votes
3 stars: 15 votes
4 stars: 24 votes
5 stars: 27 votes

Frequently Asked Questions

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