Applied ML / 14 April 2019 How to do Linear Regression and Logistic Regression in Machine Learning? Wondering how Linear Regression or Logistic Regression works in Machine Learning? Python code and a walkthrough of both concepts are available here.

ML Theory / 31 March 2019 Machine Learning Introduction 7: Probability Distributions and Maximum Likelihood Estimation (MLE) Probability distributions is all about how we can represent the distributions of probabilities of data.There is fundamentally a formula for each distribution, but I like to visualize the sort of distribution of

ML Theory / 24 March 2019 Machine Learning Introduction 6: Probability Theory This post is where you need to listen and really learn the fundamentals. All modern approaches to Machine Learning uses probability theory. AlphaStar is an example, where DeepMind made many different AIs using

ML Theory / 16 March 2019 Machine Learning Introduction 5: Measures of distance and similarity or dissimilarity Anytime we do machine learning, measures of distance can be important. An example comes to mind, when a machine has to distinguish between two object, say a pig or cow. Well then it

ML Theory / 9 March 2019 Machine Learning Introduction 4: Statistics We want to be able to say something about our data using some statistical measures, because it is important information. There are many ways to use statistics in machine learning, and the mean,

ML Theory / 4 March 2019 Machine Learning Introduction 3: Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) This post will keep us on the level of Machine Learning Introduction, but it will try to give a clearer image of what happens behind, i.e. what PCA and SVD does.Psst.

ML Theory / 24 February 2019 Machine Learning Introduction 2: Data and Data Manipulation What is data, a dataset, and how do we describe it?What we say that data is something, which is held in a dataset, that just contains N observations (rows) and M features

ML Theory / 12 February 2019 Machine Learning Introduction 1: Introductory terms This will be a series of introduction to Machine Learning, starting from the very foundation of machine learning and then moving into topics within the field. I will cover datasets, principal component analysis,