Machine learning technology is now so common that you probably use it dozens of times a day without even realizing it. And since it has so many applications, the job prospects are great for anyone with a lot of machine learning experience.

We just released a machine learning course on the YouTube channel that is the perfect place to start your learning journey.

Kylie Ying developed this course. Kylie has worked at many interesting places such as MIT, CERN, and freeCodeCamp. She's a Physicist, Engineer, and an excellent teacher.

This course is an excellent introduction to many of the key machine learning concepts. In this video you will learn about the logic and math behind supervised and unsupervised learning models. You will also learn how to can program different machine learning models on Google Collab.

Google provided a grant that made this course possible.

Here are the topics covered in this course:

  • Data/Colab Intro
  • Intro to Machine Learning
  • Features
  • Classification/Regression
  • Training Model
  • Preparing Data
  • K-Nearest Neighbors
  • KNN Implementation
  • Naive Bayes
  • Naive Bayes Implementation
  • Logistic Regression
  • Log Regression Implementation
  • Support Vector Machine
  • SVM Implementation
  • Neural Networks
  • Tensorflow
  • Classification NN using Tensorflow
  • Linear Regression
  • Lin Regression Implementation
  • Lin Regression using a Neuron
  • Regression NN using Tensorflow
  • K-Means Clustering
  • Principal Component Analysis
  • K-Means and PCA Implementations

Watch the full course below or on the YouTube channel (2-hour watch).