Introduction to Machine Learning by dummy using 

Why Machine Learning? • Series Startup Netflix • My wife is on postgraduate study about material physics using ML • 1on1 mentorship target • Prakerja project about Course Recommendation 

What is Machine Learning Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. Mitchell, Tom (1997). Machine Learning. New York: McGraw Hill. ISBN 0-07-042807-7. OCLC 36417892 

How is machine learning different from traditional programming? Traditional Programming Machine Learning 

How is machine learning different from traditional programming? Traditional Programming Machine Learning 

Machine learning flow 

Step 1: Define your machine learning problem Binary classification Multi-class classification Regression Catalog organization Generative model 

Binary classification Multi-class classification Regression Catalog organization Generative model 

Binary classification Multi-class classification Regression Catalog organization Generative model 

Binary classification Multi-class classification Regression Catalog organization Generative model 

Binary classification Multi-class classification Regression Catalog organization Generative model 

Binary classification Multi-class classification Regression Catalog organization Generative model 

Step 2: Acquire, get to know, & prepare your data Data types: • Tabular • Text • Sound • Image Where to get the data?: • Use a ready-to-use dataset • Extract the data by yourself Correct and Normalize data: 

Step 3: Train your model Features Model Training 

Step 3: Train your model Features Things that influence prediction result Example: 1. demographics, location, education history, employment history, remaining credit 2. username, anime_id, and my_score 

Step 3: Train your model Model • A model maps examples to predicted labels • It is defined by weights that are learned during the training process • Once trained, you can use it to make predictions about data that it has never seen before 

Step 3: Train your model Train Steps: 1. Separate data to be trained and to be evaluated 2. Iterate training by modifying feature and/or modify how to train your dragon (epoch or learning rate) Our model does not get smart right away - it needs to be “trained” Using available Library: - CoreML - fastText 

Step 4: Use the model to make predictions 

Prakerja Phase 1: Content Filtering Phase 2: Collaborative Filtering 

Supervised Unsupervised 

Demo Tutorial