Fundamentals of Machine Learning

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This is an introductory course on machine learning. The course covers a wide range of topics, from handling a dataset to model delivery. Some prior training in Python programming and basic calculus knowledge will help you get the best out of this course.

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31 on-demand videos & exercises
Level: Beginner
8hrs 41mins
Access on mobile, web and TV

What to know about this course

Machine learning is a branch of AI and computer science that focuses on the use of data to imitate the way humans learn and improve its accuracy.
The course is divided into two parts. The first part starts with a brief history of how machine learning started and introduces you to the basics of statistical learning. You will also understand linear regression and classification, which is the logistic regression model. Understand what cross-validation, sampling, and Bootstrap are. Explore how to go beyond linearity; we will specifically look at a couple of interesting examples to improve the linear regression model to see if we can create models that are non-linear.

The second part of the course is completely hands-on labs, which start with an example of predicting fuel efficiency in linear regression. We will then look at a lab on logistic regression with a little bit of mathematics behind it. Understand another lab session on random forests and do a review of decision trees as well. Next, we will look at a lab session on Eigenfaces by using Principle Component Analysis (PCA) and wrap up a course with a lab on ROC-AUC (Receiver Operating Characteristic Curve-Area Under Curve).

By the end of the course, you would have given yourself the skills and confidence to start programming machine learning algorithms. All resources and code files are placed here:

Who's this course for?

This course can be taken by beginners in Python programming, machine learning, and data science. Scientists, data scientists, and data analysts can also opt for this course.
The course assumes no prior knowledge. However, some prior training in Python programming and some basic calculus knowledge is helpful for the course.

What you'll learn

  • Learn the basics of statistical learning
  • Understand linear regression, classification, and supervised learning
  • Understand sampling and Bootstrap in machine learning
  • Explore model selection and regularization
  • Understand random forests and decision trees
  • Explore labs on Multilayer Perceptron (MLP)?and RNN

Key Features

  • Build customized deep learning models to start your own data science career
  • Build customized models to use for different data science projects
  • Learn about the fundamental principles of machine learning

Course Curriculum

About the Author

Yiqiao Yin

Yiqiao Yin was a PhD student in statistics at Columbia University. He has a BA in mathematics and an MS in finance from the University of Rochester. He also has a wide range of research interests in representation learning: feature learning, deep learning, computer vision, and NLP.
Yiqiao Yin is a senior data scientist at an S&P 500 company LabCorp, developing AI-driven solutions for drug diagnostics and development. He has held professional positions as an enterprise-level data scientist at EURO STOXX 50 company Bayer, a quantitative researcher at AQR working on alternative quantitative strategies to portfolio management and factor-based trading, and equity trader at T3 Trading on Wall Street.

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