As you know I was in progress learning Andrew Ng's Supervised Machine Learning: Regression and Classification, it's so dry! So I also spare some time to pick up some easy ML courses to help me to understand. Today I came across Machine Learning for Absolute Beginners - Level 1 and it's really easy and friendly to beginner. Finished in 2.5 hours - Maybe because I've made some good progress in Supervised Machine Learning: Regression and Classification and so feel it's easy. I want to share my notes in this blog post. Applied AI or Shallow AI Industry’s robot can handle specific small task which has been programmed, it’s called Applied AI or Shallow AI. Under-fitting and over-fitting are challenges for Generalization. Under-fitting The trained model is not working well on the training data and can’t generalize to new data. Reasons may be: An idea training process, it would looks like: Under fitting….. better fitting…. Good fit Over-fitting The trained model is working well on the training data and can’t generalize well to new data. Reasons may be: Training dataset (labeled) -> ML Training phase -> Trained Model The input (unlabeled dataset) -> processed by Trained model (inference phase) -> output (labeled dataset) Approaches or learning algorithms of ML systems can be categorized into: Supervised Learning There are two very typical tasks that are performed using supervised learning: Shallow Learning One of the common classification algorithms under the shallow learning category is called Support Vector Machines (SVM). Unsupervised Learning The goal is to identify automatically meaningful patterns in unlabeled data. Semi-supervised…