Fundamentals of Machine Learning

Fundamentals of Machine Learning, available at $64.99, has an average rating of 4.15, with 25 lectures, 4 quizzes, based on 15 reviews, and has 2302 subscribers.

You will learn about Learn about the fundamental principles of machine learning Build customized models to use for different data science projects Build customized Deep Learning models to start your own data science career Start your data science career and connect with the tutor in industry This course is ideal for individuals who are Beginners in python programming, machine learning, and data science. It is particularly useful for Beginners in python programming, machine learning, and data science.

Enroll now: Fundamentals of Machine Learning

Summary

Title: Fundamentals of Machine Learning

Price: $64.99

Average Rating: 4.15

Number of Lectures: 25

Number of Quizzes: 4

Number of Published Lectures: 25

Number of Published Quizzes: 4

Number of Curriculum Items: 29

Number of Published Curriculum Objects: 29

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will Learn

Learn about the fundamental principles of machine learning

Build customized models to use for different data science projects

Build customized Deep Learning models to start your own data science career

Start your data science career and connect with the tutor in industry

Who Should Attend

Beginners in python programming, machine learning, and data science.

Target Audiences

Beginners in python programming, machine learning, and data science.

This is an introduction course of machine learning. The course will cover a wide range of topics to teach you step by step from handling a dataset to model delivery. The course assumes no prior knowledge of the students. However, some prior training in python programming and some basic calculus knowledge is definitely helpful for the course. The expectation is to provide you the same knowledge and training as that is provided in an intro Machine Learning or Artificial Intelligence course at a credited undergraduate university computer science program.

The course is comparable to the Introduction of Statistical Learning, which is the intro course to machine learning written by none other than the greatest of all: Trevor Hastieand Rob Tibshirani! The course was modeled from the “Introduction to Statistical Learning” from Stanford University.

The course is taught by Yiqiao Yin, and the course materials are provided by a team of amazing instructors with 5+ years of industry experience. All instructors come from Ivy League background and everyone is eager to share with you what they know about the industry.

The course has the following topics:

Introduction

Basics in Statistical Learning

Linear Regression

Clasification

Sampling and Bootstrap

Model Selection & Regularization

Going Beyond Linearity

Tree-based Method

Support Vector Machine

Deep Learning

Unsupervised Learning

Classification Metrics

The course is composed of 3 sections:

1. Lecture series <= Each chapter has its designated lecture(s). The lecture walks through the technical component of a model to prepare students with the mathematical background.

2. Lab sessions <= Each lab session covers one single topic. The lab session is complementary to a chapter as well as a lecture video.

3. Python notebooks <= This course provides students with downloadable python notebooks to ensure the students are equipped with the technical knowledge and can deploy projects on their own.

Course Curriculum

Chapter 1: Lectures

Lecture 1: Welcome

Lecture 2: Introduction

Lecture 3: Basics in Statistical Learning

Lecture 4: Linear Regression

Lecture 5: Classification

Lecture 6: Sampling and Bootstrap

Lecture 7: Model Selection

Lecture 8: Going Beyond Linearity

Lecture 9: Tree-based Methods – Part 1

Lecture 10: Tree-based Methods – Part 2

Lecture 11: SVM

Lecture 12: Deep Learning

Lecture 13: Unsupervised Learning

Lecture 14: Classification Metrics

Chapter 2: Labs

Lecture 1: Linear Regression

Lecture 2: Logistic Regression

Lecture 3: Ridge

Lecture 4: Decision Tree

Lecture 5: Random Forests

Lecture 6: SVM

Lecture 7: MLP

Lecture 8: CNN

Lecture 9: PCA

Lecture 10: ROCAUC

Chapter 3: Notebooks

Lecture 1: Notebooks

Instructors

Yiqiao Yin
Data Science, Machine Learning, and Artificial Intelligence

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 2 votes

4 stars: 3 votes

5 stars: 9 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!